Inclusive Finance in China 9811617872, 9789811617874

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Inclusive Finance in China Edited by Yan Li · Lin Wang

Inclusive Finance in China

Yan Li · Lin Wang Editors

Inclusive Finance in China

Editors Yan Li Renmin University of China Beijing, China

Lin Wang Beijing University of Posts and Telecommunications Beijing, China

This book’s publication is subsidized by Chinese Fund for the Humanities and Social Sciences. ISBN 978-981-16-1787-4 ISBN 978-981-16-1788-1 https://doi.org/10.1007/978-981-16-1788-1

(eBook)

Jointly published with China Renmin University Press. Customers from China Mainland please order the print book from: China Renmin University Press. ISBN of the Original edition: 978-7-300-25510-1. Translation from the Chinese language edition: 致广大而尽精微: 普惠金融中国实践案例 by Yan Li and Lin Wang, © China Renmin University Press. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021, corrected publication 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover credit: © Alex Linch shutterstock.com This Palgrave Macmillan imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

The title of this book is extracted from The Book of Rites, “The superior man honors his virtuous nature and follows the path of inquiry and study; reaches the breadth and greatness and penetrates the refined and subtle to the end; reaches its greatest height and brilliancy and follows the path of the mean.” Though this statement originally discussed the principles for moral conduct and learning of the superior man (a man of noble character), it exactly applies to this book. Finance has been an important part in modern economic and social life like all other daily necessities, and been indispensable to either common people or national decision-makers. Before we know it, numerous elites and excellent students have been attracted to the financial sector, and a great many talented people have joined the high-grade financial street. Thanks to these brilliant minds, the financial industry has created many innovative products, thrived, and earned heavy profits. At the eve of the financial crisis in 2008, “approximately half of American companies’ profits go to Wall Street.”1 1 Joseph

E Stiglitz, The Price of Inequality, Beijing: China Machine Press, 2012.

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While the prosperous financial industry continuously introduces social resources to real businesses and individuals, it is always exposed to some moderate mistakes. For instance, the financial industry introduces too many resources to housing mortgage loans, leading to the global financial crisis and economic fluctuation caused by the subprime crisis in the US; and, in China, it invests too much money in the real estate industry and pushes up the housing price so high that all Chinese worry about the collapse of the industry and the loss of their wealth accumulated in their whole life. These phenomena can be generally summarized as “excessive finance,” which means that financial bubbles will occur if too much finance is provided to an industry (area) of no equivalent demand. Much finance floods the areas which already enjoy plenty of resources, leaving some groups unattended, such as people in remote districts, lowincome groups and medium, small and micro enterprises. These enterprises and people have been “ignored” by traditional finance in varying degrees for many reasons, manifested as insufficient supply of financial services. For example, a few groups have no access to basic account services and thus cannot acquire corresponding payment and deposit services; and medium, small and micro enterprises as well as individual businesses are generally subject to insufficient supply of credit funds. While some customers are rejected by financial institutions for their high default risk, a large number of people with good credit and repayment ability are also shut out of traditional financial institutions for various reasons. Therefore, financial services are exposed to structural imbalance between supply and demand for complex reasons. It takes joint efforts of all sectors of society including governments, nonprofit organizations and commercial organizations to solve this problem. The United Nations proposed the concept of “financial inclusion” in 2006, aiming to integrate various social resources, provide balanced financial services and improve the originally poor financial services for some groups. However, the ideal can hardly be fulfilled in the harsh reality, and endless problems and doubts need to be solved. For example, to adapt to the specific characteristics of microfinance, the financial services for the low-income group and medium, small and micro enterprises shall be

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distinguished from large-scale financial services by technology and procedure, special risk control measures, organizational structure and incentive systems. Traditional large-scale financial institutions must reform their business organizations before running this business, otherwise they will find no motive force to be engaged in microfinance. In the meantime, the organizations dedicated to microfinance are challenged by the high marginal cost. If they raise the interest rate to cover their cost and make some profits, customers’ cost will be increased and controversy on moral and social responsibilities will be caused; but if they reject to increase the interest rate, they will be subject to financial unsustainability. What shall we do? Different segments of microfinance show different characteristics of risk and return. Some segments may attract a great number of financial institutions for their great profit potential, providing excessive finance to certain customer groups and causing disasters (such as the subprime crisis in the US in 2008 and the micro credit debt crisis in Andhra Pradesh in 2010). In this way, the finance intending to serve the weak finds itself hurting this group. How to prevent this problem? Seeming to be a complex systematic project, inclusive finance covers the groups ignored by traditional finance, financial organizations and social groups offering microfinance services, government departments maintaining social stability and harmonious development and relevant interest subjects, adopts special financial service technologies and methods, and includes the establishment and application of laws and regulations. However, the ultimate goal of constructing such a multidimensional system is to provide due financial services to the groups that have been ignored. This complex system presents following characteristics: First, commercial and public-welfare forces are combined to jointly serve customers of different levels, but they target different customer groups. As driven by profit, commercial forces mainly rush to the customers bringing favorable profit including micro enterprises, part of small- and medium-sized enterprises and individual consumers; while public-welfare forces act under their belief of serving the society, enter the sectors that commercial forces have no interest in and offer warm services to the poor. Governments’ support is needed for customer groups where both commercial and public-welfare forces show no interest or capability

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or limited influence. What experiences of success and failure have been gained by different subjects in the practice of inclusive finance? Which models shall be promoted for specific groups? Second, government departments play both supporting and supervising roles in the construction of the inclusive financial system. They promote and meanwhile supervise the construction of inclusive finance; and not only participate in the construction directly, but also introduce market forces to the construction. It is a great challenge for the government to play a correct role. What shall the government do, particularly in the current financial environment in China? Third, information revolution propelled by big data, the Internet, cloud computing and artificial intelligence exerts great influence on the financial sector. Digital finance based on the Internet significantly lowers the marginal cost of microfinance, far exceeds traditional finance in terms of convenience, low cost and accessibility, and technologically benefits microfinance of small amounts but a large quantity. From the service objects’ point of view, digital finance based on digital technology basically equals to digital inclusive finance. With nearly one-fifth of the world’s population, a financial system of inadequate competition and structural defects and a relatively imperfect financial regulatory system, China enjoys favorable conditions for the sudden rise of digital finance. Therefore, digital finance has been developing rapidly in China since 2012, which has violently shaken traditional finance and led the whole world in terms of scale and innovation performance. What are the practices of digital inclusive finance in China? What sufferings and setbacks have digital financial institutions suffered all the way? What impact and influence have been exerted on traditional finance? Numerous questions need to be addressed for inclusive finance, such a great and complex system. Meanwhile, solutions are continuously proposed in innovative practices. Many cases have been developed during the interaction among entrepreneurs, governments and customers. These cases of either success or failure present infinite creativity of practitioners, and offer business logic and moral logic worth thinking and summarizing. We shall observe and reflect on these cases and summarize the

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regularity, which is of great values for promoting the practices of inclusive finance and research on financial theory. This is exactly our original intention of writing this book. This book collects the research findings of several case researchers. We would like to express our thanks to authors including Huacen Xu, Yu Luo, Lin Wang, Jiahong Shi, Lin Zhou, Lianyun Zeng, Yingxin Zhang, Xinyu Chen, Congyi Ju, Yangrui Li, Zuo Wang, Wenzhao Tong, Jianxiao Su, Yuqin Tang, Puning Wang and Bao Sun for their great contributions, to commentators including Chen Yan, Zhang Ning, Li Cun’gang, Song Hua, Wang Jun and Mao Jiye for their wonderful remarks, to editors Fei Xiaolin and Cui Yi of China Renmin University Press for their valuable advices, to Chen Yan for his imaginative suggestion for the title of the book, to Lin Wang for her great work in editing, and to National Natural Science Fund (program No. 71272150), National Social Science Fund (program No. 15AZD012) and the Fundamental Research Funds for the Central Universities (program No. 2020RC28) for their financial support. At last, we hope that our observations and thoughts can inspire social practitioners, policy makers, industrial supervisors and academic researchers and help inclusive finance “reach the breadth and greatness and penetrate the refined and subtle to the end.” Beijing, China September 2017

Yan Li

The original version of the book Frontmatter was revised: Funding information has been updated. The correction to the book Frontmatter is available at https://doi.org/10.1007/978981-16-1788-1_11

Contents

Part I 1

2

Microfinance

Can Microfinance Achieve Both Social and Financial Goals? Investigation on MicroCred Nanchong Huacen Xu 1 Implementation of the Dual Goals 2 How to Implement the Goal of Social Performance? 3 How to Balance Dual Goals? 4 A Long-Term and Arduous Task to Achieve the Dual Goals Case Comments References Boundaries of Microfinance: A Case Study of Microfinance Business of CD Finance Yu Luo 1 Why Do We Discuss the Boundaries of Microfinance? 2 Boundaries Between Microfinance and Traditional Finance 3 Boundaries Between Microfinance and Charity

3 4 8 11 21 23 24 27

27 30 34 xi

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Contents

4 The Minimum Interest Rates Vary by Institution Appendix 1: Microcredit Practices of CD Finance Appendix 2: Research Report on Branches of CD Finance Case Comments References Part II 3

4

43 44 59 72 73

Financing of Small and Medium-sized Enterprises

Solving the Financing Difficulty of SMEs by Community Construction: Exploration of “3+1 Integrity Alliance” Yan Li and Lin Wang 1 Origin of “3+1 Integrity Alliance” 2 The Start of “3+1 Integrity Alliance” 3 Mechanism Design of “3+1 Integrity Alliance” 4 Is “3+1 Integrity Alliance” Sustainable? 5 What Is the Effect of “3+1 Integrity Alliance”? Appendix 1: Measures for Credit Point Management of “3+1 Integrity Alliance” Appendix 2: Summary of the Interview with Goodness · Integrity Fraternity Members of “3+1 Integrity Alliance” Case Comments References Big Bank, Small Finance: The Practice of China Minsheng Bank Jiahong Shi and Lin Zhou 1 Is It Feasible for Big Banks to Run Small Finance? 2 Construct the Small and Micro Finance Risk Control System 3 Risk Control 2.0—Credit Risk Control System Depending on Information Technology

77 77 84 87 103 106 116 119 123 125 127 127 133 149

Contents

4 How Is the Effect of Small and Micro Finance Credit Risk Control? Case Comments References Part III 5

6

7

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162 166 171

Digital Inclusive Finance

Information Reform and Efficiency Improvement of Traditional Micro Finance: Case of CD Finance Lianyun Zeng and Jiahong Shi 1 Why Should CD Finance Carry Out Information Reform? 2 How to Carry Out Information Reform? 3 How Are the Achievements of Information Reform? 4 Confidence and Effort Case Comments Reference Defender “PPDAI”: Study on P2P Online Lending Platform PPDAI Yingxin Zhang 1 Background: P2P Online Lending 2 Persistence of PPD 3 Significance of the Persistence 4 Support to the Persistence 5 The Future of Persistence: Where Should PPDAI Go? Case Comments Reference “Digital Finance + Insurance + Leading Enterprise + E-Commerce”: Rural Inclusive Finance Case of Ant Financial Yingxin Zhang 1 Mongolian Sheep 2 “Digital Finance + Insurance + Leading Enterprise + E-Commerce” Model

175

176 187 194 207 208 209 211 212 226 230 243 248 251 255

257 257 271

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Contents

3 What Does E-Commerce + Rural Supply Chain Finance Bring? Case Comments References 8

Wenshang Pioneer Loan: Case of JD Rural Supply Chain Finance Congyi Ju, Xinyu Chen, Yangrui Li, Zuo Wang, and Wenzhao Tong 1 Rural Strategy of JD and JD Finance 2 JD Rural Supply Chain Finance Program: JD Agricultural Loan—Pioneer Loan Case Comments References

Part IV 9

10

279 286 287 289

290 294 313 317

Credit

“Credit Use” and Cost of Discredit: The Supreme People’ Court and Zhima Credit Cooperating to Punish “Lao Lai” Yan Li, Lin Wang, Jianxiao Su, Yuqin Tang, and Puning Wang 1 How to Solve the Issue of “Lao Lai”? 2 Development Status of Credit Industry in China 3 Cooperation Between Zhima Credit and the Supreme People’s Court 4 Effect of Zhima Credit’s Punishments on Lao Lai 5 “Improve Credit” by “Using Credit” Case Comments References Bairong Zhixin: Big Data and Credit System Construction Bao Sun 1 Origin of Bairong Zhixin 2 Build the Big Data Platform 3 Construct the Risk Model

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321 326 329 343 352 353 354 357 358 362 367

Contents

4 Diversified Credit Analysis and Evaluation Products 5 How Is the Effect of Big Data Credit Analysis and Evaluation? 6 Ensure Data Security and Protect Personal Privacy Case Comments References Correction to: Inclusive Finance in China Yan Li and Lin Wang

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373 380 387 396 400 C1

Contributors

Xinyu Chen Renmin University of China, Beijing, China Congyi Ju Renmin University of China, Beijing, China Yan Li Renmin University of China, Beijing, China Yangrui Li Renmin University of China, Beijing, China Yu Luo Renmin University of China, Beijing, China Jiahong Shi Renmin University of China, Beijing, China Jianxiao Su Renmin University of China, Beijing, China Bao Sun Renmin University of China, Beijing, China Yuqin Tang Renmin University of China, Beijing, China Wenzhao Tong Renmin University of China, Beijing, China Lin Wang Beijing University of Posts and Telecommunications, Beijing, China Puning Wang Renmin University of China, Beijing, China

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Contributors

Zuo Wang Renmin University of China, Beijing, China Huacen Xu University of Maryland, Baltimore, MD, USA Lianyun Zeng Renmin University of China, Beijing, China Yingxin Zhang Renmin University of China, Beijing, China Lin Zhou Fudan University, Shanghai, China

List of Figures

Chapter 1 Fig. 1 Fig. 2 Fig. 3

Five stakeholders of performance management of MicroCred Nanchong The mechanism for balancing the dual goals of MicroCred Nanchong Number of loans issued over the years (Data source Social service report of MicroCred Nanchong [2014])

9 12 14

Chapter 2 Fig. 1

Composition of the intended use of CD finance loans (Data source 2014 annual report of CD Finance)

57

Chapter 3 Fig. 1

Division standards of the World Bank and China for the medium, small and Micro-sized enterprises data (Source Zheng Ziqiang, General Thinking of the Development of the Small and Micro-finance System in China, Renmin University of China “Small and Micro-finance Research Center” Work Report, 2014)

80 xix

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List of Figures

Fig. 2

Financing difficulty of SMEs and phenomenon of “SMEs Disappear” data (Source Zheng Ziqiang, General Thinking of the Development of the Small and Micro-finance System in China, Renmin University of China “Small and Micro-finance Research Center” Work Report, 2014) Relationship among the alliance, financial institutions and enterprises Version 1.0 of the organization structure of Goodness · Integrity Fraternity One-manage-three member management structure Human Resources distribution of the alliance Economic downturn in China

Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7

83 85 95 97 105 116

Chapter 4 Fig. 1

Fig. 2 Fig. 3 Fig. 4

Fig. 5 Fig. 6

Fig. 7 Fig. 8

Fig. 9

Per-capita labor cost of Chinese Commercial Banks in 2014 (Data source National Bureau of Statistics and annual reports of listed companies) Relationship between the recovery rate of non-performing debts and the loan limit (Data source CMBC materials) Small and micro loan size of CMBC 2009–2014 (Data source CMBC annual reports) Structure and process of small and micro loan credit risk control of CMBC (Data source Project summary of Renmin University of China on CMBC Small and Micro Finance Department) CMBC organizational structure (Data source Annual reports of CMBC) Branches are the core of business management and credit risk management in Version 2.0 small and micro finance (Data source CMBC Small and Micro Finance Blue Book) CMBC Shangmaotong credit management flowchart and the process of internal static information acquisition Diagram of the working principle of CMBC “Vertical Search Engine” and “Decision Making Engine” (Data source CMBC risk management technology) Credit comments page of the vertical search engine (Data source CMBC risk control technology)

131 140 141

145 146

148 151

156 158

List of Figures

Fig. 10 Fig. 11

Fig. 12 Fig. 13 Fig. 14

Role of CMBC decision making engine in loan approval (Data source CMBC risk management technology) CMBC retail loan balance, credit loan balance and small and micro loan balance 2009–2014 (Data source CMBC annual reports) Changing trend of the rate of revenue of loans of CMBC 2008–2014 (Data source CMBC annual reports) Changing trend of CMBC non-performing loan rate 2010–2014 (Data source CMBC annual reports) Upgrading of CMBC risk control model—comparison of Version 1.0 and 2.0

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163 164 165 169

Chapter 5 Fig. 1 Fig. 2 Fig. 3

Fig. 4

Fig. 5 Fig. 6 Fig. 7

Fig. 8

Fig. 9

Loan workflow of CD finance microcredit business before information reform Organizational structure of general CD finance general branch offices Number of mobile phones owned by every hundred urban households and rural households (Data source National Bureau of Statistics) Internet penetration rate in rural areas and proportion of farmers accessing the internet by mobile phone (Data source China Internet Network Information Center [CNNIC]) Improvement of rural financial infrastructure (Data source CNNIC, CBRC and National Bureau of Statistics) Comparison of the loan process before and after microcredit information reform of CD finance Loan officers’ feeling on business development based on information reform (Data source Analysis of investigation questionnaires of CD Finance) Average monthly amount of loans released by a loan officer before and after the information reform (Data source Monthly brief reports of CD Finance) Average monthly amount of loans released by a branch office before and after the information reform (Data source Monthly brief reports of CD Finance)

178 179

185

186 187 191

196

196

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Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

Fig. 18

List of Figures

Quantity of loans released and received in cash and time spent by the loan officer before and after information reform (Data source Analysis of investigation questionnaires of CD Finance) Loan officers’ feeling of efficiency improvement based on centralized withholding payment (Data source Analysis of investigation questionnaires of CD Finance) Time spent by the loan officer to sort out customers’ materials before information reform (Data source Analysis of investigation questionnaires of CD Finance) Time spent by supervisors on different tasks before and after information reform (Data source Analysis of investigation questionnaires of CD Finance) Loan officers and supervisors’ feelings of mobile apps (Data source Analysis of investigation questionnaires of CD Finance) Supervisors’ feelings of efficiency improvement based on centralized withholding payment by unionpay (Data source Analysis of investigation questionnaires of CD Finance) Front-office and back-office workers’ feelings of compliance check (Data source Analysis of investigation questionnaires of CD Finance) Loan officers and supervisors’ feelings of the new system (Data source Analysis of investigation questionnaires of CD Finance) Employees and customers’ feelings of centralized withholding payment (Data source Analysis of investigation questionnaires of CD Finance)

198

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200

201

201

202

203

205

206

Chapter 6 Fig. 1 Fig. 2 Fig. 3

Transaction volumes of online lending in different years (Data source Home to Online Lending) Quantity of online lending platforms in different years (Data source Home to Online Lending) Quantity of problematic platforms in different years (Data source Home to Online Lending)

215 216 216

List of Figures

Fig. 4 Fig. 5

Fig. 6 Fig. 7 Fig. 8 Fig. 9 Fig. 10 Fig. 11 Fig. 12

Fig. 13

Fig. 14

Fig. 15 Fig. 16 Fig. 17

Fig. 18

P2P platform transaction process (Data source 2013 Blue Book of China’s Online Lending Industry) Details in the main links of P2P online lending industry in China (Data source 2014 White Book of China’s Online Lending Industry) Traditional model (Data source 2014 White Book of China’s Online Lending Industry) Creditor’s right transfer model Guarantee model (Data source 2014 White Book of China’s Online Lending Industry) Risk reserves model (Data source 2014 White Book of China’s Online Lending Industry) Platform model Development course of PPDAI (Data source PPDAI Chronicle of Events from the PPDAI official website) PPDAI transaction volumes in different years (Data source IResearch report, PPDAI official website and the report of the 1st quarter, 2015) Transaction volumes of four major platforms in different years (Data source Yooli.com 2014 annual report, IResearch report, 2013 White Book of China’s P2P online lending Industry, Renrendai official website) Average single investment amount of 13 online lending platforms (Data source White Book of China’s P2P online lending Service Industry) Risk control ideas of PPDAI Overdue rates of PPDAI platform (Data source PPDAI Report of the 1st quarter 2015) Comparison between the actual overdue rates and predictions of the magic mirror risk control system (Data source PPDAI Report of the 1st quarter 2015) Number of loans released per month by PPDAI in 2015 almost equaled to the sum of top 3 platforms of the industry (Data source Semi-annual Report of PPDAI , Home to Online Lending)

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222 222 223 224 225 225 227

228

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243 245 247

248

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List of Figures

Chapter 7 Fig. 1

Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

Operating income and assets of Mongolian Sheep (Note The line chart in the figure corresponds to the coordinate axis on the left and the bar chart corresponds to that on the right) Whole-industrial-chain operation model of Mongolian Sheep Strategic planning of Mongolian Sheep in next 3–5 years “Sheep Union” model of Mongolian Sheep Mongolian Sheep—Ant Financial—CIC Property Cooperation Flow Chart The solution of driving rural finance with credit assists the upgrading of agriculture

259 260 262 265 275 284

Chapter 8 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6

Products and services of Jining Daliang Company Sales model of Jining Daliang Company Planting production supply chain with Jining Daliang as the core enterprise Operation model of Wenshang Pioneer loan Process of Wenshang Pioneer loan Detailed explanation of the release of Wenshang Pioneer loan

297 298 298 300 302 305

Chapter 9 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7

Extensive, multi-dimensional and real-time big data Zhima Credit score model based on big data Presence of Zhima Credit in all aspects of life Coverage of Zhima credit score among Lao Lai (Data source Zhima Credit) Dishonesty duration of performed Lao Lai who have opened Zhima (Data source Zhima Credit) Dishonesty duration of performed Lao Lai who have not opened Zhima (Data source Zhima Credit) Distribution of scenarios that restrict Lao Lai before they performed obligations (Data source Zhima Credit)

334 336 338 344 346 347 350

List of Figures

Fig. 8 Fig. 9

Top 10 scenarios in the number of performed Lao Lai subject to restrictions (Data source Zhima Credit) Top 10 Scenarios in the Number of Unperformed Lao Lai Subject to Restrictions (Data source Zhima Credit)

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351 352

Chapter 10 Fig. 1

Fig. 2

Fig. 3 Fig. 4

Fig. 5

Fig. 6

Fig. 7 Fig. 8

Relations among Bairong Zhixin, Bairong Financial Service and Percent (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center) Overall Framework of Data Collection, Processing and Output of Bairong (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center) Five Engines and Eleven Systems (Data source Bairong Financial Service) Flowchart of Construction of the Big Data Credit Risk Model (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center) Whole-process Credit Analysis and Evaluation Product System of Bairong Zhixin (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center) Functions of Credit Analysis Assistant (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center) Comparison of KS Values and KS Curves (Data source Bairong Financial Service) Organizational Structure of Bairong Zhixin (Data source Bairong Financial Service)

361

362 366

372

374

379 383 395

List of Tables

Chapter 1 Table 1 Table 2 Table 3 Table 4 Table 5

Measurement indexes and contents for the implementation of the dual goals of MFIs Measurement indexes for the implementation of the dual goals of MicroCred Nanchong MicroCred Nanchong’s social performance indicators and their decomposition List of restricted industries MicroCred Nanchong’s customer satisfaction survey results

5 7 10 15 20

Chapter 2 Table 1 Table 2 Table 3

Profitability indicators and loan quality indicators of CD finance 2009–2014 The sustainable interest rate for CD finance in the Rosenberg model The sustainable interest rate for CD finance with the cost-plus pricing method unit: RMB 10,000

36 40 43

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Table 4

List of Tables

Assets and liabilities of CD finance 2011–2014 Unit: RMB 10,000

56

Chapter 3 Table 1

Table 2 Table 3

Comparison of the risk control effects of joint-guarantee organizations actively helping others and those passively bearing guarantee liabilities Sources of the “Relief Fund” Combination table of credit rating and risk rating

90 93 99

Chapter 4 Table 1 Table 2

Comparison of the credit risk features between large and medium enterprises and small and micro enterprises Big commercial banks’ strengths and weaknesses in risk control compared to small financial institutions

129 167

Chapter 5 Table 1

Reasons for people’s different feelings of centralized withholding payment

206

Chapter 6 Table 1

Core characteristics of online model and offline model

239

Chapter 7 Table 1

Risk control system—time dimension

276

Chapter 9 Table 1 Table 2

Overall performance status of Lao Lai Application scenarios of Zhima Credit

345 349

Chapter 10 Table 1 Table 2

Bairong Zhixin personal credit information analysis reports Example of a Borrower’s Score Card

376 378

List of Tables

Table 3 Table 4 Table 5 Table 6

Comparison of Non-performing Ratios of Customers of Microfinance Company X Risk Level of Personal Behaviors of Small and Micro Enterprise Owners Customer Non-performing Ratios of Different Channels in the P2P Company Principle of Bairong Data Segmentation

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384 385 386 390

Part I Microfinance

Originating in micro loan, microfinance mainly provides credit to individuals and micro-enterprises rejected by traditional financial institutions. Since the 1970s, particularly after the founder of the Grameen Bank in Bangladesh was awarded the Nobel Peace Prize for the bank’s great contributions to the self-development of rural poor women, microfinance has been increasingly considered as an instrument to help the vulnerable groups achieve self-development and financial freedom. As multiple types of financial services including account, deposit and microcredit are covered, micro loan has been renamed as microfinance. However, its principle remains unchanged, that is to serve vulnerable groups and enable everyone to enjoy modern financial services. This part is dedicated to microfinance practices in China.

1 Can Microfinance Achieve Both Social and Financial Goals? Investigation on MicroCred Nanchong Huacen Xu

Microfinance is an important tool for poverty alleviation. The United States had promoted the concept of “the International Year of Microcredit” of 2005 globally. In 2006, Dr. Yunus, the founder of the Grameen Bank in Bangladesh, was awarded the Nobel Peace Prize for his outstanding contribution in poverty reduction. Since 1994 when microfinance was introduced to China, many formal financial institutions including Agricultural Bank of China, Postal Savings Bank of China, Rural Credit Cooperatives and China Development Bank have been actively developing microcredit businesses; and many institutions specializing in microcredit businesses such as rural banks and microcredit companies have been successively established. Hence microfinance has This author of this chapter is Huacen Xu. Members of the investigation team include Huacen Xu and Wang Yalei. This report was compiled based on field investigation and interview records and was not subject to the review by MicroCred Nanchong Co., Ltd.

H. Xu (B) University of Maryland, Baltimore, MD, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_1

3

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H. Xu

become the fundamental power to mitigate vulnerable groups’ difficulty in financing. However, with the market-oriented reform of rural finance in recent years, all types of MFIs including rural banks and microcredit companies have been increasingly exposed to issues such as “deviation from agriculture, rural areas and farmers”, “credit funds released to big cities, enterprises and projects and some industries with excessive concentration” and “despising the poor and favoring the rich”. Under such circumstances, MFIs are faced with a significant difficulty in achieving their social goal of serving vulnerable groups and their commercial goal of making financial profits. With MicroCred Nanchong, the first wholly foreign-owned microcredit company in China, as an instance, this chapter will explore how MFIs give consideration to their dual goals from three aspects, the implementation of the dual goals of MicroCred Nanchong, the balance between the dual goals and the role of social performance in this process.

1

Implementation of the Dual Goals

1.1

Measurement Criteria

In the early stage, microfinance adhered to the main objective to expand the outreach to vulnerable groups; later, financial sustainability became an increasingly important indicator; and the goals of MFIs started to diversify. In the end, a relatively unanimous view has been formed worldwide that MFIs have two goals, serving vulnerable groups and attaining financial sustainability (double bottom line) (Zhang Zhengping 2011). The ideas on the measurement criteria for the implementation of the dual goals vary among different scholars. Representative arguments on the measurement index include: the Subsidy Dependence Index (SDI) proposed by Yaron (1992), and the Operational Sustainability Ratio (OSR) and Economic Sustainability Ratio (ESR) proposed by Morduch (1999). According to Microfinance Information Exchange, an international well-known institution, the return on assets (ROA), return

1 Can Microfinance Achieve …

5

Table 1 Measurement indexes and contents for the implementation of the dual goals of MFIs Measurement Goals Measurement indexes contents Outreach to vulnerable groups

Financial sustainability

Active borrowers Total loan amount Loan amount per capital/GNI per capita No. of active customers/loan officers (Loan cost per capita + accrued expenses for loan loss/total loan amount OSS = Operating income/operating expenses after adjustment ROE = Operating profit/average net assets

Service scope Service scope Service depth Production efficiency Management efficiency Operational sustainability Profitability

Note 1. GNI refers to the Gross National Income 2. Considering the difficult access to relevant data, ROE is adopted as a surrogate index of FSS here to measure the financial sustainability. Same below Data source Zhang Zhengping (2013)

on equity (ROE), operational self-sufficiency (OSS)1 , financial selfsufficiency (FSS)2 and other indexes are generally combined to measure the financial sustainability of MFIs in practice. By integrating the abovementioned ideas, Zhang Zhengping (2013) adopted the two major index systems of operational sustainability and financial sustainability and further divided them into seven indexes to measure the implementation of the dual goals (as shown in Table 1). The viewpoint of Zhang Zhengping is applied here.

= OI/(OF + FC + LC), where OI represents annual operating income; OF represents annual operating expenses; FC represents annual financial costs; and LC represents allowances for impairment losses on loans. If OSS is greater than 1, it suggests that the institution has the “capabilities for operational sustainability”. 2 FSS = OI/(OF + FC + LC + CC), where OI represents annual operating income; OF represents annual operating expenses; FC represents annual financial costs; LC represents allowances for impairment losses on loans; and CC represents capital costs. If FSS is greater than 1, it suggests that the institution has the “capabilities for financial sustainability”. 1 OSS

6

H. Xu

1.2

Measurement Results

Being the first wholly foreign-owned micro-credit company in China, MicroCred Nanchong was established upon the joint investment by MicroCred S.A, International Finance Corporation of the World Bank, and KFW Bankengruppe. As a non-financial institution, the company does not involve in public deposits but is mainly engaged in small loan business. It targets at the least significant end market in the financial system to serve the lowest class of the society. Since its opening in 2007, MicroCred Nanchong has been aiming to help the vulnerable groups who cannot obtain financial services through traditional financial channels, insisting on issuing micro credit loans to serve customers engaged in agriculture or living in rural areas, promoting inclusive finance and practicing corporate social responsibility. By the end of 2014, the company maintained good business performance with the loan balance of RMB 4,753.147 billion, 15,135 active borrowers and ROE of 12.15%. Meanwhile, after opening its first branch in Dazhou in August 2014, MicroCred Nanchong is now preparing to establish branches in Bazhong and Suining. Its Dazhou Branch started to make a profit since January 2015, demonstrating that the micro credit mode of MicroCred Nanchong can be replicated. The measurement indexes for the implementation of the dual goals of MicroCred Nanchong are shown in Table 2. From the perspective of outreach, the loan balance of MicroCred Nanchong had increased by 146 times from RMB 3.2308 million in 2007 to RMB 475.3147 million in 2014. The number of its active customers had also constantly increased by 14 times from over 1,000 customers in the early stage to over 15,000 customers in 2014. The loan amount per capita/GNI per capita had been increasing year by year from 2007 and 2011, and has remained stable afterwards despite a slight decline. As suggested by these three indexes, MicroCred Nanchong has been constantly improving its service scope and depth for vulnerable groups since its establishment. As to financial sustainability, MicroCred Nanchong has been on the rise in terms of its production efficiency, with the number of active customers increasing from 62 at the early stage after its establishment to 139 in 2014. Its management efficiency had been fluctuating at the rate

62 13.27 4.27

– −63.82

1761.46 224.01

323.08 91.09 –

1908

–

2008.12

12.40 3.11

94

6049.80 411.73

3293

2009.12

9.92 6.20

103

13318.48 464.36

5580

2010.12

Data source Auditing reports of MicroCred Nanchong over the past years

No. of active borrowers (households) Loan balance (RMB 10,000) Loan amount per capita/GNI per capita (%) Production efficiency (households) Management efficiency (%) ROE (%)

2007.12

9.55 9.60

103

20084.59 519.28

6311

2011.12

9.52 13.48

145

24646.69 383.45

9181

2012.12

Table 2 Measurement indexes for the implementation of the dual goals of MicroCred Nanchong

7.88 13.26

149

35991.04 387.28

11771

2013.12

10.58 12.15

139

47531.47 356.75

15135

2014.12

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of around 10%, showing an upward trend in recent years. Its ROE has also been increasing year by year and remained above 10% after 2011. These three indexes show that MicroCred Nanchong has already realized financial sustainability, and has seen the constant improvement of its sustainability capacity. Apparently, both outreach and financial sustainability of MicroCred Nanchong have been improved, indicating that the dual goals have been well balanced. In recent years, MicroCred Nanchong has become a model of the industry for its successful operation and aroused the interests of many banks and investment institutions at home and abroad. It was selected as one of the “100 Most Competitive MFIs in China” in 2011, 2012 and 2013, and awarded the “Best Chinese MFIs in terms of Social Responsibility” in 2010, 2011 and 2013. MicroCred Nanchong enjoys extensive popularity and reputation in the microfinance industry.

2

How to Implement the Goal of Social Performance?

MFIs bear the natural mission to expand their outreach to vulnerable groups; while, by comparison, financial sustainability is more like a constraint (Robert S. Kaplan 2013). A new consensus has gradually emerged among MFIs that they can implement their social goal of covering vulnerable groups and fulfill their social responsibility by means of social performance management (Sun Zhengping 2011). Then, how does MicroCred Nanchong perform social performance management? It initiated its social performance management since November 2010, aiming to undertake its social mission of supporting vulnerable groups through practical actions based on the universal values of society. For this end, MicroCred Nanchong has expanded its targets of social performance management to five major groups, including the customers, shareholders, employees, communities and environment. Covering five dimensions, customer service, employee development, environmental protection, community interaction and investor relations, it has incorporated all stakeholders in its social performance management system. By doing this, MicroCred Nanchong has successfully adhered to its social mission and prevented deviation from goals (Fig. 1).

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Customers

Shareholders

Communities

Social performance management

Employees

Environment

Fig. 1 Five stakeholders of performance management of MicroCred Nanchong

Not only putting forward the above concepts, MicroCred Nanchong also implements these ideas in daily management with following measures. First, it has established a special department for social performance management to practically implement the social performance goal. Second, it has spread the company mission among all employees by means of meetings, trainings and publicity etc. Even the general manager at the top and ordinary employees at the bottom can clearly explain the contents and requirements of the company’s mission and social performance combining their own job responsibilities. Third, in order to assure the implementation of social performance management, it has set up and analyzed the refined indicators for social performance (Table 3) to examine the implementation effect of each procedure.

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Table 3 MicroCred decomposition

Customers

Nanchong’s

social

indicators

Indicator

Indicator definition

Loan release speed

The proportion of loans issued in three days The proportion of loans renewed in different periods The proportion of different products used by the customers The proportion of new customers who have not borrowed loans from other financial institutions before cooperating with MicroCred The proportion of new customers who have not cooperated with other financial institutions before cooperating with MicroCred The proportion of customers whose turnover has increased by 25% during the period of borrowing loans from MicroCred The proportion of customers with the number of employees increasing by 10% during the period of borrowing loans from MicroCred

Loan renewal rate

Product utilization ratio

Proportion of inclusive finance

Proportion of non-banking customers

Communities

performance

Customer turnover growth

Growth of employees of the customers

and

their

Examination method Quantitative

Quantitative

Quantitative

Quantitative

Quantitative

Quantitative

Quantitative

(continued)

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Table 3 (continued)

Employees

Indicator

Indicator definition

Employee training rate

The proportion of employees who have received training in the past 12 months The proportion of employees who have left their companies in the past 12 months The proportion of loans that have an impact on the environment Submitting shareholder social performance reports on a quarterly basis

Employee turnover rate

Environment

Shareholder

Environmental impact

Examination method Quantitative

Quantitative

Quantitative

Qualitative

Data source Social performance report of MicroCred Nanchong

In May 2014, Planet Rating, an international rating company, evaluated the social goal implementation of MicroCred Nanchong and finally rated it as 4- (the highest rating is 5 + , which has not been achieved by any company yet). This was the highest rating given by Planet Rating for Chinese companies, fully acknowledging the social goal implementation of MicroCred Nanchong.

3

How to Balance Dual Goals?

Although social performance management is applied to help MFIs to implement their social goals, some MFIs still attach too much importance to their financial goals and deviate from social goals in practice due to institutional defects and considerations on economic interests etc. China’s regulatory system stipulates that MFIs are not allowed to absorb social public deposits. Therefore, self-owned funds constitute the major

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Aim at Social performance management

Social goal

Outreach to vulnerable groups

Service depth

Balance Service scope

Balance

Social mission

Financial goal

Profitability

Management efficiency

Balance Production efficiency

Fig. 2 The mechanism for balancing the dual goals of MicroCred Nanchong

fund sources of MFIs. Subject to such constraint of credit resources, MFIs are seeing an increasingly prominent problem in balancing between service depth and service scope. Then, how does MicroCred Nanchong take into account the dual goals? MicroCred Nanchong has built an internal mechanism for this sake (Fig. 2). On one hand, considering the credit resource constraint, it balances the service depth (per capita loan/per capita GNI) and service scope (active loan customers) to continuously widen the outreach to vulnerable groups and achieve its social goal. On the other hand, faced with the human resource constraint, it balances production efficiency (the number of active customers/the number of loan officers) and management efficiency [(per capita loan cost + accrued expenses for loan loss)/total loan amount], effectively controls the operating costs and obtains financial profit with the interest rate as the leverage, thus to realize its financial goal. Balance should be deemed as the critical element here.

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Balance Between Service Depth and Service Scope

Since MFIs are not allowed to absorb public deposits, they will inevitably deviate from their original value goal and market positioning if they excessively pursue profits with limited capital funds. In this respect, MicroCred Nanchong has gained following experiences. First, balance the supply of the credit line. On the one hand, within the credit limit (the maximum credit limit is RMB 500,000), MicroCred Nanchong implements hierarchical management of customers (classifying loans less than RMB 50,000 as micro-loans, those between RMB 50,000 and RMB 150,000 as small loans, and those more than RMB 150,000 as small and medium loans) according to their loan amount. MicroCred Nanchong evaluates the performance of employees based on the number of loans issued rather than the amount. It sets the upper limit for “large loans” in terms of the total amount, which means that loans more than RMB 100,000 shall not exceed 25% of the total loan balance every month, so as to avoid employees’ excessive pursuit of profits and encourage employees to develop customers. On the other hand, MicroCred Nanchong has acquired Smart Campaign Client Protection Certification and implemented the joint action principles for the protection of microfinance customers. It rates customers according to their credit history in MicroCred Nanchong (at five levels including AA, A, B, C and D, ranking from high to low), and gradually adjusts the credit line according to the customer rating and customers’ practical needs to avoid customers’ excessive debt. In this way, though benefits may not be obtained in the short term, long-term benefits will be acquired as the customers grow with the company. The customer loan renewal rate of MicroCred Nanchong nearly approaches 50%; and among the customers who have renewed their loans, 70% have had their credit lines increased over the previous time. It is exactly based on such “investment” vision that MicroCred Nanchong can sacrifice short-term benefits for long-term ones, continuously expand the scope of customers and consolidate its customer base (Fig. 3).

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Loans

Fig. 3 Number of loans issued over the years (Data source Social service report of MicroCred Nanchong [2014])

Second, balance the types of credit products. Although the main credit products of MicroCred Nanchong, including “MicroCred Business Loan” and “MicroCred Easy Loan”, meet the needs of most target customers, it has also launched the products such as “house-decorating loans”, “freight transportation loans”; “start-up loans”, “student loans” and “environmental protection loans” to further expand its service scope. For example, MicroCred Nanchong mainly runs its businesses in the three districts and six counties in Nanchong, most of which are ruralurban fringe zones. With the development of new rural construction in local areas, many farmers have received new houses compensated by the government for land requisition, but they may still suffer poverty as they have no professional skill for a job. To this end, MicroCred Nanchong has launched the “start-up loans” in good time to support such customers and their families. This loan helps customers combine their unused human capital and monetary capital and rapidly change their poverty status, and has achieved good effect of poverty alleviation. At the beginning of 2015, MicroCred Nanchong further launched the “environmental protection loans”, focusing on supporting the construction of biogas digesters, the purchase of solar water heaters or other projects of energy conservation, emission reduction and environmental protection.

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In addition, MicroCred Nanchong has set up a disability loan indicator (accounting for about 0.5% of the total credit amount), which is specifically used to encourage credit officers to develop disabled customers who have long been excluded by traditional finance, so as to improve outreach to vulnerable groups. Third, balance the environmental benefits of credit. MicroCred Nanchong stresses on the environmental benefits of credit and resolutely rejects the industries covered by the List of Restricted Industries (Table 4) released by International Finance Corporation, the World Bank, and Table 4 List of restricted industries No.

Prohibited industries

1. 2.

Production or actions involving forced labor or child labor Production or transaction of products in violation of national laws and regulations, or international treaties and agreements, or international bans, and the above-mentioned illegal behavior Cigarette production and transaction Production and transaction behavior involving weapons, arms and ammunition Production and transaction of alcoholic beverages Enterprises engaging in casinos, gamble or similar business Transaction of wildlife and its products specified by the society and CITES Production and transaction of radioactive materials Production and transaction of unbonded asbestos fibers Behaviors such as commercial logging operations Production and transaction of products containing PCBs materials Production, trading and transaction of a large number of hazardous chemicals Production and transaction of drugs under the control of international bans Production and transaction of pesticides and herbicides under the control of international bans Production and transaction of ozone-depleting substances under the control of international bans The behavior of marine fishing with fishing nets in the length of over 2,500 meters Embezzling, exploiting or conducting other actions towards the land owned by indigenous people without their content

3. 4. 5.. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17.

Data source Regulations on Social Responsibility of MicroCred Nanchong (Employee Handbook)

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classifies the loans into classes A, B and C according to their environmental influence. Class A indicates severely adverse effect, compared to Class B suggesting moderately adverse effect and Class C meaning small or no adverse effect on environment. As of now, the loans of Classes B and Class C account for more than 95% of all the loans released. After the loans are issued, the credit officers regularly visit the customers to track the environmental impact of the loan projects. In this way, MicroCred Nanchong has effectively balanced the service depth and service scope with favorable business governance, risk control and business scale, thereby leveraging more capital and obtaining longterm and stable business returns.

3.2

Balance Between Management Efficiency and Production Efficiency

With a certain total loan amount, better credit risk control means the greater management efficiency. With a certain number of active customers, more loan customers managed by one credit officer on average suggest the higher production efficiency. Apparently, the balance between management efficiency and production efficiency is essentially a balance between human resource investment and credit risk control. First, it balances staffing and risk control. Combining the regional characteristics, MicroCred Nanchong has introduced and significantly localized the microfinance mode of MicroCred S.A., changing the former edition that focused on the mandatory indicator (the financial indicator) and relied on quantitative analysis to the current model where equal attachment is attached to both mandatory and flexible indicator (non-financial indicator). A variety of loan application and evaluation documents are especially designed, based on which credit officers evaluate the customers. By doing this, the loan approval officers can accurately evaluate customers without on-site assessment. In this way, the production efficiency has been significantly improved and the operation cost has been reduced.

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In addition, this risk control mode mainly relies on evaluation of credit officers and judgment of loan approval personnel, so high requirements are proposed on the quantity and quality of personnel. In the longterm practice, MicroCred Nanchong has established its unique concept of talents. It believes that micro and small credit business is essentially a long-term process of accumulating customers. Since the “social relations” and “resources” of individuals are limited, it is inclined to select those who are willing to endure hardships, and are practical, good at communicating and self-motivated. Therefore, applicants who have had certain social experiences or even suffered setbacks and are in a difficult situation or have a heavy family burden are preferred. In this way, the main characteristic of employees is determined in the recruitment process to reduce frictions in the future. The company will arrange threemonth closed trainings for new employees, enabling them to quickly integrate into the team. While adopting a competitive and stimulating remuneration and welfare system, MicroCred Nanchong has established staged talent trainings for backup talents, middle managers and elites. At present, among the 182 employees of MicroCred Nanchong, 90% are from Nanchong with the turnover rate of employees of less than 10%, and the senior managers are all customer managers from the first-line teams. The perfect human resource management system has effectively improved the management efficiency of MicroCred Nanchong. Second, it balances process settings and risk control. As customer evaluation is completed by credit officers, the control of the ethical risk of credit officers becomes a key factor. In the long-term practice, MicroCred Nanchong has established the dual-person due diligence mode with a chief credit officer and an assistant credit officer. Credit officers with outstanding selection ability are elected as team leaders, who are supported with another credit officer. Two credit officers are assigned to each loan project, including a chief credit officer who shall be the team leader and an assistant credit officer. They are responsible for investigation before issuing loans, examination during loan release and post-loan management. The team leader is responsible for managing the team, conducting investigation before issuing loans and helping other credit officers in credit business. Evaluation of a team leader is based on the team’s overall performance, particularly the number of customers and

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quality of the loans. After the dual-personnel pre-loan investigation, the credit officers may independently review the loan or submit it to the credit committee for review according to the application amount (the credit committee is divided into three levels according to different approval authorities). In this way, it has given full play to the business advantages of the chief credit officer that the chief credit officer is urged to “teach, help and lead” the assistant credit officer; and also, the check and balance relation is established between the chief and assistant credit officers to improve efficiency and control risks. At present, 110 credit officers are managing more than 15,000 customers, i.e. about 140 customers managed by one credit officer on average. The non-performing loan ratio is only 0.34%.

3.3

Balance Between Outreach and Profitability

For small-credit companies that mainly issue small loans, the price of loans, i.e. the interest rate, plays a vital role in the profitability. The too high interest rate will damage the interest of target customers and affect poverty alleviation. At present, the amortization loans account for 80% of all loans issued by MicroCred Nanchong. Such loans implement the monthly interest rate of 1% and charge the fee at 1% of the loan amount in a lump sum when issuing the loans, suggesting an actual annual interest rate of 22.32%. The loans with lump-sum loan issuance and payment account for 20% of all loans. Such loans adopt the monthly interest rate of 1.6%, which can be converted to an annual interest rate of 19.2%. In the meantime, the interest rates of other local microfinance institutions’ loans range from 1.25% to 2.5%; and the annual interest rates implemented by commercial banks range from 8.95% to 15.6%. Apparently, the interest rate implemented by MicroCred Nanchong is higher than that of commercial banks and close to the average level of microfinance companies. According to investigation, the high interest rate implemented by MicroCred Nanchong is attributed to the loan costs to a large extent. Firstly, the labor cost is too high. Distinguished from commercial banks which implement the counter business model, MicroCred Nanchong

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assigns credit officers to issue loans to customers at their home, thus to provide customers with short-term, frequent, quick and convenient credit services. Moreover, in order to reduce the customers’ repayment burden and lower the risk, the repayment is often made once a week or once a month, so a total of 12–50 times of repayments shall be made for one loan, which considerably increases the operating cost. Secondly, compared to the large amount of loans issued by commercial banks is large, which may exceed several million yuan or even hundreds of million yuan, the average amount of a single loan issued by MicroCred Nanchong is about merely RMB 40,000, but the operating cost is almost the same. Therefore, the loan cost is significantly increased. Thirdly, the sources of funds vary between MicroCred Nanchong and commercial banks. Since MicroCred Nanchong is not allowed to attract public savings, it needs to absorb funds by paying dividends or borrowing external debts. As a result, the capital cost is higher. The fourth factor is the risk cost. More than 90% of the loans issued by MicroCred Nanchong are loans on credit, the risk of which is much higher than that of mortgage loans (mortgage guarantee is generally required by most microfinance businesses of commercial banks). Are vulnerable groups willing to accept such interest rate? The answer seems to be affirmative. Small-scale production investments generally have good cash flow, so their income can cover the cost of borrowing under normal conditions. It is especially so for farmers. In view of the highly seasonal farming activities, farmers borrow money from MicroCred Nanchong to run production projects during the slack seasons, which does not have time cost, yet generates additional benefits. In addition, MicroCred Nanchong provides open and transparent price of loans without invisible and extra expenses, and shows great advantages in terms of the efficiency of issuing loans and professional services, etc. For example, it implements the regulations on customer protection to prevent credit officers from insulting or forcing customers during the collection process. These strengths constitute the “soft power” of MicroCred Nanchong. Its balance between the pricing of interest rates and outreach has been proven by its extremely low non-performing loan ration, the continuously increasing number of customers and high customer satisfaction (Table 5).

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Table 5 MicroCred Nanchong’s customer satisfaction survey results Investigation items

2012

2013

Impression on MicroCred Loan products Process of issuing loans Service quality Professionalism of employees

90.60% 93.00% 89.30% 78.00% 98.00%

94.00% 98.00% 92.60% 79.80% 99.80%

Data source Survey report on the customer satisfaction conducted by the marketing department of MicroCred Nanchong (2012, 2013)

To sum up, by focusing on its social mission of supporting vulnerable groups and adopting social performance management as the means, MicroCred Nanchong has implemented flexible management to balance between service depth and service scope, between production efficiency and management efficiency, and between interest rate pricing and outreach. Therefore, MicroCred Nanchong has won the respect and trust of local government and people, built a positive image, increased its credit rating and obtained more financing opportunities. In 2013, MicroCred Nanchong increased its total investment amount to RMB 300 million with the approval of Sichuan Provincial Department of Commerce and successfully obtained overseas funds of USD 19 million. In 2014, its registered capital reached RMB 150 million with the capital increment of RMB 50 million contributed by its shareholders, and it also used financial leverage to bring in foreign debts of RMB 169.6 million. As a result, it has ensured the quick growth of business and market demands, further achieved the sustainable development of financial profitability and well balanced the social goal of supporting vulnerable groups and the financial goal of sustainable development.

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A Long-Term and Arduous Task to Achieve the Dual Goals

4.1

Balance the Dual Goals

21

One of the dual goals pursued by MFIs rests in serving vulnerable groups in a sustainable way. However, the financial goal of sustainable development may conflict with the social goal of serving vulnerable groups. Such conflict is essentially a conflict between fairness and efficiency. From the practice of MicroCred Nanchong, we can see that the key to achieving its dual goals is to balance. First of all, balance is a scale. It refers to both the balance between development goals and plans in time and space, and the balance of resource supply for plan implementation. Secondly, balance requires attitude. MFIs shall firmly adhere to the social mission of serving vulnerable groups and further explore a path for sustainable development in the commercial world, which requires both proper conceptual recognition and practical exploration. Thirdly, balance requires giving up. During business development, MFIs must do something and leave some things undone for the sake of the dual goals. Any business that harms the environment and customer benefits and is not conducive to long-term development shall be resolutely given up even if it can bring financial benefits in the short term.

4.2

Implement Social Performance Management

Social performance management is of great significance for MFIs to achieve sustainable development without deviating from their purposes and goals. It is a tool to achieve the dual goals as well as a dynamic process to advance the dual goals in a balanced way. The role and significance of MFIs implementing social performance management is manifested in different dimensions including customers, employees, communities, environment and shareholders. We can learn the follows from the social performance management of MicroCred Nanchong. Firstly, social performance management and evaluation is a complex and systematic

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project, where different stakeholders have different demands and participate in the evaluation in different forms. To a certain extent, it is conducive to the development of MFIs in China if they participate in international organizations to draw on their advanced experience, and adopt standardized social performance systems. Secondly, China’s MFIs have made great progress in recent years, but they still need to further build and improve their social performance evaluation system and refine the operational procedures, so that the fulfillment of their financial and social goals can be clearly evaluated and disclosed to help them improve social recognition and development environment.

4.3

Improve Market-Supporting Measures

The development and improvement of policy-supporting environment constitutes an indispensable factor for MFIs to acquire further development, help more poverty-stricken and low-income people who are excluded from regular finance seize development opportunities and promote the construction of the inclusive finance system. On one hand, we shall bring the market competition mechanism into full play and establish and improve the mechanism for the entry and exit of MFIs funds to regulate and standardize the development of MFIs by market means. On the other hand, we shall also combine the economic and social development and the actual situation of microfinance and improve the supporting measures such as laws and regulations, rating classification, and planning formulation, thus to lay a good market foundation for long-term development of microfinance. To sum up, to gain long-term development and win greater investment, policy and institutional support, MFIs are required to pay more attention to giving consideration to the dual goals. With MicroCred Nanchong as an example, this study explores how MFIs balance the dual goals. This study only plays a limited role in helping people understand the rapidly developing microfinance in China, but I hope it can inspire more valuable ideas in this respect.

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Case Comments Microfinance mainly serves low-income groups, small and micro enterprises and other customer groups ignored by traditional financial institutions and bears a strong sense of social responsibility; however, distinguished from charity, it provides financial services for profit. Microfinance offers paid services for two reasons: first, urging borrowers to establish the repayment consciousness and enabling them to use the borrowing effectively (applying the borrowing to a purpose by which they can acquire repaying ability) to improve their economic capability and income; and second, achieving the sustainable development of microfinance. Therefore, though many commonweal workers dedicated to eliminating poverty through financial means have high expectations for microfinance, microfinance institutions always adhere to an important goal of realizing financial balance and surplus and further achieving financial sustainability. Social performance benefits others, while financial performance benefits themselves. These two goals seem to be conflicting, and the extreme pursuit of either goal will necessarily jeopardize the other goal. Hence, the balance between the “altruistic” and “egoistic” goals shall be attached importance to. Any imbalance will cause the insufficient service result or sustainable development capability. In this case, MicroCred Nanchong, which is specialized in offering credit services to small and micro enterprises, has delivered impressive results of how to achieve the internationally recognized dual goals, i.e. the extensive credit coverage of vulnerable groups and financial sustainability. According to analysis of this case, the key factor contributing to the realization of dual goals rests in the sound control mechanism and means, including “balanced credit line”, “balanced products” and “balanced environmental influence” etc. An enlightenment of this case is that with clear strategic goals, microfinance institutions can realize social benefits and institution benefits at the same time. Regrettably, this case fails to further display how decisionmakers of MicroCred Nanchong stick to serving the underclasses as the target microfinance customers or provide relevant backgrounds.In addition, when reading this case, I always had a question in my mind: why only the number of loan customers and the credit line are applied as the

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indexes to measure the loan coverage in respect of social performance, but not the loan interest rate? Can social performance be better achieved with a wider loan coverage based on a higher interest rate? This question needs to be further discussed. Commentator: Yan Li

References Abraham A, Pavoni N, The Efficient Allocation of Consumption Under Moral Hazard and Hidden Access to the Credit Market, “Journal of the European Economic Association”, 2005, 3(2–3):370–381. CGAP of The World Bank. Interest Rate Ceiling and Microfinance. 2004. Copestake J, Mainstreaming Microfinance: Social Performance Management or Mission Drift, “World Development”, 35(10):1721–1738. Dabla-Norrise Ji Y, Townsend R, Filiz Unsal D, Identifying Constraints to Financial Inclusion and Their Impact on GDP and Inequality: A Structural Framework for Policy, IMF Working Paper, 2015. He Guangwen, County Economy Needs Localized Financial Services More, “Rural Finance Research”, 2013(1). He JIanwei, Target Deviation in the Commercialization of Microfinance—A Theoretical Model and Empirical Study of the Microfinance Institutions in Western China. “Modern Economic Science”, 2012 (7). Jacob Y, Successful Rural Finance Institutions. Discussion Paper No. 150. Kaplan R, Case Study Research: Design and Methods, London: Sage, 2013. Morduch J, The Microfinance Schism, “World Development”, 2000, 28: 617– 629. Sun Liangshun, Zhou Mengliang, A Review of Research on Mission Deviation of Microfinance Institutions. “Journal of Northwest A&F University” (Social Science Edition), 2014 (5). Zhou Mengliang, Research on the Design of Social Performance Evaluation Indicators of Microfinance in China. “Rural Finance Research”, 2011(2). Zhang Zhengping, Guo Yongchun, An Empirical Study on the Influencing Factors of Target Deviation in Microfinance Institutions—Test and Comparison Based on the Fixed Effect Mode. “Finance and Trade Economics”, 2013 (7).

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Zhang Zhengping, Conflict and Governance of the Dual Goals of Microfinance Institutions: A Review of Research Progress, “Economic Review”, 2011 (5). Zhang Zhengping, Wang Maixiu, Can Microfinance Institutions Balance the Dual Goals of Serving the Poor and Financial Sustainability?—Statistical Evidence from the International Microfinance Market and Its Implications. “Issues in Agricultural Economy”, 2012 (1).

2 Boundaries of Microfinance: A Case Study of Microfinance Business of CD Finance Yu Luo

1

Why Do We Discuss the Boundaries of Microfinance?

At present, “inclusive finance” has become a social hot issue. The concept of inclusive finance refers to “providing all sectors and groups of society in need of financial services with proper and efficient financial services at affordable costs based on the requirements of equal opportunities and the principle of business sustainability by means of increasing financial guidance and support, strengthening the construction of the financial system

The author would like to express gratitude to Zhang Haobing’s research work and the help of Duan Hongbo, Gao Wei, Lianan Hexia, Yan Li, Li Zhenni, Luo Shen and Lin Wang in case investigation.

Y. Luo (B) Renmin University of China, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_2

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and improving financial infrastructure”.1 As far as international experience is concerned, microfinance, which is dominated by micro loans, is regarded as one of the most important ways to achieve inclusive finance. Rich theories and practices of microfinance have been accumulated worldwide. However, many institutions carrying out business in the name of microfinance in China actually do not conform to the international general definition of microfinance. For example, the so-called “micro and small loans” issued by commercial banks in China differ greatly from international standards; most of the 9,000 small loan companies still adopt the credit model of banks to issue loans at a large amount and charge high interest rates. Only a few are adhering the social mission of microfinance. The confusing use of the concept of microfinance in China is associated with the background of longterm “financing difficulties” faced by small and medium-sized companies in China. However, the misuse of the concept of microfinance has marginalized the true micro loans that serve the customers at the lowest end. Some mainstream issues in the field of international micro-finance research have not been studied well, so micro-finance in line with the international standards faces many difficulties in practice in China: how to design systems to issue loans to low-income groups who lack collateral, how to conduct credit investigation and review loans, how to control risks, how to distinguish microfinance from traditional banks, and how to balance its business sustainability and social mission. To answer the above questions, we must accurately understand the service boundaries of microfinance. The definition of the boundaries of microfinance indicates its difference from other financial service methods. Firstly, there is the boundary between microfinance and traditional finance, i.e. the upper boundary of microfinance.2 Are there any financial needs that traditional financial institutions cannot or are unwilling to satisfy, and must rely on the supply of other institutions? 1 Acronym

notes of the Government Work Report in 2015 (March 11, 2015) [January 31, 2018]. http://www.gov.cn/xinwen/2015-03/11/content_2832629.htm. 2The “traditional finance” mentioned here refers to the main financial service methods. For example, the traditional banking industry usually serves the medium and high-end customers with assets available for mortgage, and the banking and non-banking financial institutions which provide credit services for small and medium-sized enterprises have been paid more and more attention to in recent years.

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The answer is affirmative. In rural areas,3 for example, state-owned large commercial banks, rural cooperative financial institutions and postal saving banks are the main suppliers of rural credit services. In recent years, a number of new rural financial institutions, such as township banks and micro-loan companies, have emerged. These financial institutions all provide microfinance services, but show significantly different levels. The differences between microfinance and traditional credit are as follows. The customers of microfinance are the poor or low-income people who cannot gain loans from traditional financial institutions and are at the bottom of the customer pyramid. Microfinance needs to use the product design and risk control methods that are different from those of traditional credit. For example, it makes use of the information symmetry of acquaintance society to adopt the way of joint-guarantee for loans to increase peer supervision; issues loans to women; adopts the mechanism of social punishment and refinancing incentives; implements simple and fast approval, etc. The credit line of microfinance is quite low. Secondly, there is the boundary between microfinance and charity, i.e. the lower boundary of inclusive finance. Cost issue is an eternal topic for financial services. Under the circumstance of certain technical conditions, the more inclusive finance inevitably costs more. A lower boundary necessarily exists if no charitable donation or continuous subsidies. Microfinance can achieve independent sustainable survival above this lower boundary. Microfinance must seek a balance point between its business sustainability and social mission. To this end, a core task is to determine the loan interest rate that enables the realization of financial self-sufficiency while conforming to the established social mission and goal (seeking a low level of profitability). In the early 1990s, an international non-governmental organization carried out microfinance pilot projects in China. After the trial periods of these projects ended successively, some microfinance projects turned into MFIs, with CD Finance as a representative. Formerly known as the Microfinance Project Department of China Foundation for Poverty Alleviation, CD Finance was restructured into a company in 2008. 3 Microfinance

is achieved in different forms in urban and rural areas, but with the consistent essential characteristics.

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As a social enterprise that focuses on microfinance targeting rural low and middle-income groups, CD Finance aims to support the low and middle-income families in poverty-stricken areas to carry out incomegenerating activities through microfinance with on-site services. Also, it provides non-financial services in a variety of forms to enhance the comprehensive abilities of customers in an all-round way, so as to help them get rid of poverty and achieve prosperity in a sustainable way. Among a number of microfinance institutions in China, CD Finance has its business closest to the internationally-acknowledged concept of “microfinance” and develops more successfully than many others. It is a representative case for the study of the development of inclusive finance in China. By studying the business mode and financial data of CD Finance, we can observe the upper and lower boundaries of microfinance, which will help people better understand the concept of microfinance and develop targeted policy that is conducive to the development of China’s microfinance industry.

2

Boundaries Between Microfinance and Traditional Finance

We take the micro loans issued by CD Finance in rural areas as an example to discuss the boundaries between microfinance and traditional finance. On the surface, microfinance extends the targets of financial services from customers of traditional finance to customers at a lower end. In fact, it has essentially changed the financial operation mode. Microfinance is vastly different from traditional finance in terms of credit product design and risk control methods etc. Such differences exactly define the upper boundary of microfinance.

2.1

Customer Positioning

From the perspective of the levels of customers, almost no overlap is found between the customers served by CD Finance and the customers of traditional financial institutions and emerging for-profit small loan

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companies. The customers of CD Finance are low-income farmers who find it impossible to obtain loans from financial institutions. Such customer positioning was determined as CD Finance was formerly an institution established within China Foundation for Poverty Alleviation with a fundamental goal of poverty alleviation and the accomplishing means of micro loans. It is in line with the mainstream concept of microfinance in the world. At present, in the rural credit market of China, rural credit cooperatives occupy the most important position, followed by the Agricultural Bank of China and the Postal Savings Bank. However, when providing credit support for “agriculture, rural areas and farmers”, these three financial institutions face the problem of a surge in transaction costs. When the loan limit is below RMB 50,000, the profitability is very small, so it is difficult to further lower the level of customers. Rural credit cooperatives, for example, offer credit services to 87,000 farmer households, accounting for 1/3 of the total number of farmer households in China. There are still quite a huge number of farmers who are excluded from financial services and can only rely on private lending. CD Finance serves a part of the remaining 2/3 of the farmers. In the rural financial market, it serves as a supplement to the existing financial system instead of a competition to other financial institutions.

2.2

Credit Line

Although being generally referred to as “small loans”, the loans provided by CD Finance are of smaller amount compared with the small loans provided by traditional financial institutions, so the loans provided by CD Finance shall be called “micro loans” in a more rigorous manner. At present, the per capita loan size of CD Finance is RMB 10,000. In the group model, the credit lines vary among different levels of customers. The customers applying for loans for the first time are tier-1 customers, and they automatically become tier-2 customers if they repay their first loans on time and apply for loans again. The tier-2 customers automatically upgrade to tier-3 customers if their repayments for their second loans are not overdue. The credit line for the first loan applied by each farmer household is RMB 10,000, compared to RMB 12,000 and RMB

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16,000 respectively for tier-2 and tier-3 customers. The upper limit of individual credit loans is only RMB 50,000. Group loans are generally issued with a term of one year. When farmers make repayments, they need to repay the equal principal and interest of loans by installments for 10 months, which relieves the repayment pressure on the farmers throughout the lending period. Micro loans can neither directly affect the large investment of farmers, nor be so high that farmers deliberately breach the contract at the price of their credibility. Even in case of defaults, the loss of a single loan will not exert a big impact on CD Finance.

2.3

Credit Products

The main credit product of CD Finance is the group-based jointguarantee loan, which is different from the mainstream credit products due to its characteristics as follows. Firstly, no mortgage is required. When approving loans, traditional financial institutions pay attention to whether there is collateral or guarantee, which is based on “objects”; while CD Finance serves the customers who are not high quality as regarded by traditional financial institutions without mortgage or guarantee by public officials. In response to such customer characteristics, microfinance products adhere to the design concept of not to look at the customers’ current assets, but to believe in the customers’ future repayment ability and willingness to repay. Therefore, microfinance is a people-centered credit product. Secondly, it adopts the joint-guarantee method. The joint-guarantee loans issued by CD Finance have learned from the credit model used by international microfinance institutions represented by Grameen Bank, and renovated the model slightly according to the specific situations in China. Customers applying for loans must first organize a joint-guarantee group on their own, and CD Finance completely does not interfere with the formation of the group. A joint-guarantee group usually consists of five team members in need of borrowing and who are jointly responsible for loan repayment. Joint-guarantee plays a significant role in selecting credit customers.

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Customers who offer joint-guarantee to each other are generally acquaintances such as neighbors, friends and relatives (other than immediate family members). In the acquaintance society, people have a relatively thorough understanding of each other’s background and personality, information communication in a timely manner, rich and high-quality information, so that some people with poor credibility can be automatically screened out in the process of forming joint-guarantee groups. Therefore, the mechanism of joint-guarantee groups has an invisible role in credit investigation to lower the degree of information asymmetry. Thirdly, loans are mainly issued to women. The reason why women are regarded as an important customer group is that women are the core of families. They are more expecting family stability and have higher credibility. They have a stronger sense of responsibility in making repayment and controlling risks. In addition, women usually do not need large investments, and the micro loans issued by CD Finance are more in line with the needs of this group. Therefore, issuing loans to rural women helps improve their family status and reduce the risk and default rates of loans, as well as helps revitalize small household industries and achieve the goal of increasing household income.

2.4

Risk Control Methods

Since the joint-guarantee loan model without mortgage is adopted, the risk control method of CD Finance is significantly different from the risk control means adopted by traditional financial institutions that require mortgage or guarantee. Joint-guarantee is the most important risk control method in microfinance, and joint-guarantee groups enable the realization of major risk control through the mechanism of automatic selection and supervision among group members. The form of group loans takes advantage of the trust and ethics formed by interpersonal communication in Chinese rural society, which not only reduces the degree of information asymmetry, but also increases the cost of reputation damage brought by defaults of the people borrowing loans. As a result, the common problems of adverse selection and ethical risks are reduced in lending activities. The joint-guarantee groups share the work

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of pre-loan review, monitoring during issuing the loans and post-loan processing for microfinance institutions. However, the joint-guarantee approach also has operational difficulties and potential risks. One of the problems is that the cost of forming groups may increase and the quality of groups may decrease as the radius of service is expanded. It is required by the rules that the formation of a group requires three to five borrowers, but the borrowing needs of different people do not often occur at the same time. It is more difficult to find people who trust and are familiar with each other and have the credit needs with similar credit limits in the same period. To conduct such a match for new loan applicants, the transaction cost is not low and it may lead to a decline in the quality of groups. The second problem is about how to prevent the occurrence of collusion, i.e. “one person using money obtained from the loans borrowed by several persons”, which requires credit officers to have a stronger screening ability, but the selection of excellent credit officers requires higher costs. The work of grass-roots credit officers of CD Finance is generally laborious. For example, for a one-year loan at the amount of less than RMB50,000, credit officers have to offer on-thespot service for at least 12 times, including one time for investigation, one for issuing the loan and 10 for collecting repayments. The credit officers are local farmers and most of them are married women over 30 years old. If such work is to be completed by white-collar credit officers at traditional financial institutions, high wage compensation is required and their ability to acquire information is weaker than that of farmer credit officers due to the lack of mass base in communities. This is also the reason why it is very difficult for traditional financial institutions to spread businesses to the lower level of customers.

3

Boundaries Between Microfinance and Charity

CD Finance is an institution initiated by China Foundation for Poverty Alleviation with the original intention of providing inclusive financial service to achieve its social mission of poverty alleviation rather than gaining profit through issuing loans. Its social mission is manifested in

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the following aspects. It mainly serves low-income farmers in povertystricken areas; the proportion of farmer households in its customer base is 99%, and 80% of them cannot obtain loans from traditional financial institutions for three consecutive years; women account for 94% of the customers.4 In the early days of its development, CD Finance mainly relied on financial funds and the non-profit capital that emphasized social values for its sources of funds. However, with the expansion of its development scale, CD Finance becomes in greater need of acquiring funds from the market, such as the wholesale loans issued by commercial banks, which require costs. Therefore, financial sustainability is crucial. Financial sustainability can guarantee the business performance of CD Finance and help with its business development. Moreover, it enables CD Finance to maintain normal services without depending on longlasting donations. In order to achieve sustainable development, CD Finance needs to be financially self-sufficient, so it charges customers a fair interest rate with the goal of a low profit margin, which distinguishes it from public charitable organizations. At present, CD Finance has certain profitability, but its profit rate is lower than that of financial institutions and its asset quality is good. As can be seen from the financial indicators (see Table 1), CD Finance can maintain its financial selfsufficiency with its current loan interest rate. So, is this interest rate really fair? Is it necessary that public microfinance institutions should charge farmers an interest rate lower than that of banks as believed by some people? If we can find the lowest loan rate that allows CD Finance to maintain financial self-sufficiency, we can determine the lower boundary of microfinance. Microfinance services below this boundary are more of the nature of charity.

4 Annual

report of CD Finance (2014).

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Table 1 Profitability indicators and loan quality indicators of CD finance 2009– 2014 2009 2010 2011 2012 2013 2014 (%) (%) (%) (%) (%) (%) Total loan yield Net interest yield Cost-to-income ratio Debt-to-equity ratio Return on total assets Return on net assets PAR > 1 NPL > 30

– 16.69

– 15.88

20.07 15.20

21.14 15.47

20.93 15.28

20.71 16.45

65.49

64.24

52.70

53.65

57.90

60.88

109.41

153.02

200.95

300.56

171.58

292.21

1.02

2.78

2.20

1.62

1.32

1.04

2.22

6.58

6.20

7.62

4.16

3.48

0.05 0.05

0.07 0.05

0.76 0.61

0.28 0.23

0.84 0.80

0.33 0.27

Note 1. PAR > 1 indicates the non-performing loan ratio with loans overdue for more than one day; and NPL > 30 indicates the non-performing loan ratio with loans overdue for more than 30 days. These two indicators are used to measure the quality of loans issued by CD Finance 2. Total loan yield, net interest yield, cost-to-income ratio, debt-to-equity ratio, return on total assets and return on net assets comprehensively reflect the profitability of CD Finance. Refer to the annual report for the specific calculation methods Source Financial data in the annual reports and monthly brief reports of CD Finance from 2009 to 2014

3.1

Analysis of the Loan Interest Rate Implemented by CD Finance

The one-year loan interest rate announced by CD Finance is 13.5% (which can be lowered to 12.5% for high-quality customers). The one-year loan interest rate implemented by commercial banks is about 5%, which may be increased by 30–50% based on different situations of individuals or small enterprise customers (but mortgage or guarantee is required). The one-year loan interest rate implemented by for-profit microfinance companies is usually close to four times the benchmark interest rate. The loan interest rate currently implemented by CD Finance is 13.5%, which seems to be much higher than that of commercial banks and much lower than that of for-profit microfinance companies. It is reasonable for its interest rate to be higher than that of

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banks and other large financial institutions, so that the people with other financing channels will not apply for loans from microfinance institutions, and the microfinance funds can truly be offered to the people who cannot obtain loans from other financial institutions. As a result, the microfinance institutions can fulfill their mission of serving rural development. However, it is not proper to directly compare the interest rate of 13.5% with the interest rate announced by other financial institutions, since CD Finance implements the interest rate based on method of repaying the equal principal and interest of the loans by installments. Next, we will convert the loan interest rate implemented by CD Finance into the interest rate for lump-sum repayment of the principal and interest for comparison with the interest rate implemented by banks. The one-year interest rate of micro loans issued by CD Finance is 13.5% if equal repayment of principal and interest by installments is adopted. The specific mode of repayment is as follows: for example, the loan at the assumed amount of RMB 10,000 is issued in the Installment 0; no repayment is required in the first two months, and a repayment of RMB 1,135 should be made each month from the third month to the 12th month. In fact, farmers can no longer completely use the loan of RMB 10,000 since the third month. For the calculation of the net present value, we adopt the method of internal rate of return (IRR) to calculate the actual interest rate in the case of lump-sum repayment of principal and interest based on the repayment of RMB 1,135 each month in the last 10 months, and then discount each subsequent cash flow to the Installment 0 to make it equivalent to the loan at the amount of RMB10,000 issued in the Installment 0. By using the IRR method, the monthly rate of return of approximate 1.72% can be obtained. Therefore, we can get the annual rate of return of 20.64% (i.e. the annual percentage rate, which is called by CD Finance as the “comprehensive rate”). This comprehensive rate can be compared with the loan interest rate announced by commercial banks and for-profit small loan companies. Based on the monthly rate of return of 1.72%, it can be calculated that the actual rate of return of one-year loans issued by CD Finance is theoretically 22.71%.5 5The realization of this rate of return depends on the premise that credit funds must be effectively allocated in a timely manner.

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It can be seen from the comparison that the interest charged with the method of lump-sum repayment of principal and interest is obviously higher than that of equal repayment of principal and interest by installments. With the equal repayment of principal and interest by installments, the amount of interest and principal is declining month by month and the total interest repaid is low; while with the lump-sum repayment of principal and interest, the interest paid by the borrower every month is the same, so its interest rate is higher. For farmers, repayment by installments can reduce the pressure compared to lump-sum repayment. It is proven by practice that the interest rate of 13.55 is acceptable. For CD Finance, repayment by installments can accelerate turnover of capital to produce higher returns. According to statistics, the turnover rate of microfinance funds of CD Finance is much higher than 1 (Li Jing 2013). For example, according to the calculation based on the rate of return of 13.5% and annual turnover rate of 1.68% for each loan, the annual rate of return can be as high as 22.71%. When the equal repayment of principal and interest by installments, the interest rate announced by CD Finance does not seem to be very high compared with the general microfinance companies. It makes it easier for farmers who are not proficient in financial knowledge to accept it emotionally. However, as a public welfare microfinance institution, CD Finance charges an annual loan rate of 20.64%. Is it too high? What is the minimum interest rate required to maintain its commercial sustainability? Many international research results support the microfinance institutions to charge high rates, because microfinance has high costs in terms of customer investigation and screening, loan issuance and collection. Therefore, it needs high interest income to make up for its high financing, operating and loan loss costs (Armendáriz and Morduch 2013). The interest rate mainly covers the operating cost, capital cost, loan loss provision and profit. Rosenberg, Gonzalez and Narain pointed out in 2009 that the operating cost constituted a main part of the interest rate; loan loss provision accounted for a small proportion for most loan companies, so it exerted a very small impact on the interest rate; and the cost of debts of microfinance institutions was higher than that of banks, promoting the increase in the loan rate. According to the investigation

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on hundreds of microfinance institutions in the world by the Consultative Group to Assist the Poor at the World Bank (hereinafter referred to as CGAP) and MIX (Microfinance Information Exchange) (CGAP 2003), MFIs need to maintain the loan rate around 26% on average to achieve financial sustainability. In this way, the interest rate implemented by CD Finance is lower than the global median level.

3.2

Estimates of the Sustainable Interest Rate for CD Finance in the Rosenberg Model

Rosenberg, a CGAP expert, proposed in 2002 a loan rate pricing model that could enable MFIs to achieve sustainable development: R=

(AE + LL + CF + K − LL) (1 − LL)

where AE indicates the ratio of administrative cost to average loan balance; administrative cost refers to the sum of costs excluding the capital cost and loan loss, and including employee wages and benefits, social insurance premiums, advertising fees, rental fees, office expenses, etc.; LL refers to the loan loss rate, i.e. the ratio of total loan loss to average loan balance; CF means the ratio of cost of capital to average loan balance; K stands for the ratio of expected profit to average loan balance; and II suggests the ratio of returns on investment activities to average loan balance. Despite certain problems in the Rosenberg model in estimating the sustainable interest rate for MFIs, it is still a simple and practicable calculation method. In order to compare the actual interest rate implemented by CD Finance with the interest rate that conforms to sustainable development of CD Finance, we have introduced relevant data in CD Finance’s annual reports from 2009 to 2014 into the Rosenberg model for examination, and the result is shown in Table 2. We have selected three expected profit targets for comparison. K1 indicates that the profit rate is 0 and the corresponding R1 indicates the lowest sustainable interest rate when the income makes up for the cost just right; K2 indicates that the profit rate is 1%, which is the profit rate target that CD

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Table 2 The sustainable interest rate for CD finance in the Rosenberg model AE LL CF K1 K2 K3 II R1 R2 R3

2009 (%)

2010 (%)

2011 (%)

2012 (%)

2013 (%)

2014 (%)

13.39 1.12 2.87 0 1.00 0.98 – 17.58 18.60 18.58

11.97 1.42 2.80 0 1.00 2.39 – 16.42 17.43 18.84

13.28 1.56 5.08 0 1.00 2.89 – 20.23 21.25 23.16

13.50 0.60 4.95 0 1.00 0.17 – 19.17 20.17 19.34

14.18 1.11 4.82 0 1.00 2.11 0.39 19.95 20.96 22.08

14.26 1.17 3.93 0 1.00 1.60 0.09 19.49 20.51 21.12

Note The actual loan loss data is not available, so the data on loan loss provision is used instead; there is no data on return on investment from 2009 to 2012, so 0 is used instead for calculation Source Calculated according to the annual reports of CD Finance from 2009 to 2014

Finance wishes to maintain and corresponds to R2 ; K3 indicates the actual profit rate realized by CD Finance, which is obtained by dividing the current operating profit by the average loan balance, and corresponds to R3 . According to the calculation results, based on the data from 2011 to 2014, we can acquire a difference of about one percentage between the minimum sustainable interest rate R1 for CD Finance obtained by using the Rosenberg model and the annual loan rate of 20.64% implemented by CD Finance; and when maintaining a low profit rate target of 1%, R2 almost equals to the annual loan rate implemented by CD Finance. R3 is used as a comparison item, which is close to the theoretically calculated actual rate of return of 22.71% of CD Finance, indicating that the model is basically applicable. The calculation results for 2009 and 2010 are low, which is mainly related to data quality. Judging from the result based on the Rosenberg model, the current interest rate implemented by CD Finance can support its sustainable development, which explains why CD Finance can achieve cumulative profit while also giving attention to its social mission. However, we have also noticed that the proportion of the administrative cost of CD Finance is increasing year by year. The reason is that micro-loans in rural areas are labor-intensive services. In the case of inadequate rural infrastructure and a large number of outlets, it

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is required to add more employees, and the rural labor cost is increasing year by year, leading to the increase in the administrative cost year by year.

3.3

Estimates of the Sustainable Interest Rate for CD Finance in the Cost-Plus Pricing Model

In order to make a comparison with the result obtained in the Rosenberg model, we have used the cost-plus pricing model, which is a loan rate pricing method commonly used by commercial banks, to re-estimate the sustainable interest rate for CD Finance. The formula for the cost-plus pricing method is as follows: Loan interest income + non - loan interest income = total cost + fair profit

(1)

where the total cost includes the operating cost, capital cost, loan loss provision, other business expenses, taxes, etc. Formula (1) can be refined as follows: LI + CI + II + OR = (FC + AC + FE + TS + LA) + RP

(2)

where, LI indicates the loan interest income; CI means the net fee and commission income; II stands for the return on investment; OR is the net income of other businesses; FC is the cost of capital; TS is the business tax and income tax; AC is the management fee; FE is the financial cost; LA is the asset impairment provision; and RP is the fair profit. Debt capital of CD Finance mainly comes from two sources. The first is the wholesale loans issued by commercial banks, with the actual cost of capital rate of 8 to 9% after adding the loan rate and the guarantee fee. The second is the borrowings from shareholders. The capital cost of borrowings from shareholders is lower than that of commercial loans, but it accounts for a small proportion. From the perspective of shareholders, the fair rate of profit R0 includes the expected profitability (1% determined by CD Finance) plus the compensation for inflation. In summary,

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Formula (2) is further changed to: LB × RL + CI + II + OR =FC + AC + FE + TS + LA + (A − L) × (R0 + π)

(3)

where LB indicates the average normal loan balance; RL indicates the average sustainable loan interest rate6 ; A indicates the average total assets; L indicates the average total debt; and π indicates the inflation rate. The expression of the sustainable loan interest rate RL is obtained based on Formula (3) as follows:  RL =

(A − L) × (R0 + π)+(FC + AC + FE + TS + LA) − (CI + II + OR) LB



Based on the data of annual reports of CD Finance, we have estimated the RL s in different years. R0 indicates the expected profit target of 1% proposed by CD Finance, and R0L indicates the sustainable loan interest rate calculated by substituting R0 value. R1 indicates the actual profit rate obtained by dividing the current operating profit by the average loan balance of the current year. R1L indicates the sustainable loan interest rate obtained after substituting R1 , and it is used as reference for R0L . It can be seen from the results that the sustainable loan interest rate from 2011 to 2013 calculated in the cost-plus pricing model was higher than the annualized loan rate of 20.64% of CD Finance; and the sustainable loan interest rates in 2010 and 2014 were lower than 20.64%. In general, the sustainable loan interest rate calculated in the cost-plus pricing model is higher than the rate calculated in the Rosenberg model, mainly because that the cost factors investigated by the latter are less than those by the former. It shows that the micro loan business of CD Finance has been approaching the lower boundary of microfinance with the current loan rate (Table 3).

6 R may also indicate the sustainable rate of return on loans and be compared with the actual L rate of return on loans of 22.71% realized by CD Finance.

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Table 3 The sustainable interest rate for CD finance with the cost-plus pricing method unit: RMB 10,000 LB FC A-L π (%) AC FE LA OR TS CI II R0 (%) R0L (%) R1 (%) R1L (%)

2009

2010

2011

2012

2013

2014

18,797 409 7639 −0.70 1774 – 166 −25 300 −0.1 – 1 18.28 0.98 18.27

38,914 81 13,120 3.30 3443 18 409 −26 −50 −62 – 1 18.08 2.39 18.71

66,081 267 21,716 5.40 6365 321 819 −22 542 −395 – 1 23.09 2.89 23.87

85,619 376 28,084 2.60 8969 665 452 30 1200 −636 – 1 21.12 0.17 20.81

118,146 491 41,905 2.60 13,108 494 1131 0.4 1323 −574 392 1 21.67 2.11 22.13

187,216 6014 55,870 2.00 19,532 0 1794 280 1933 −965 139 1 19.94 1.60 20.16

Note The data on return on investment from 2009 to 2012 is not available, so 0 is used instead for calculation Source Calculated according to the annual reports of CD Finance from 2009 to 2014

4

The Minimum Interest Rates Vary by Institution

Taking the micro loans issued by CD Finance to rural low-income groups as an example, this paper explores the difference between microfinance and traditional finance and charity, and defines the boundaries of microfinance. The target customers of microfinance are the low-income people that the traditional financial institutions are unwilling to or unable to provide financial services for. The credit line is low and the risk is high, so it is necessary to adopt design and risk control methods that are different from those of mainstream credit products. Since micro loans have high costs in the operation process, MFIs must charge a certain level of interest to achieve financial sustainability. When the interest rate implemented by an MFI is lower than its minimum sustainable interest rate for a long term, it tends to be more like a charity organization.

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The minimum sustainable interest rate of a microfinance institution is variable. It depends on the operational efficiency of the institution on one hand and on the subsidies received on the other hand. CD Finance enjoys many subsidies and concessions. For example, it enjoys the special preferential tax policy for exempting business income and income tax from interest income in agricultural-supporting loans at an amount of less than RMB 50,000; and it can obtain wholesale loans of hundreds of millions of yuan from China Development Bank, Agricultural Bank of China and other large financial institutions. By comparison, only a few of other public welfare MFIs have access to similar qualifications and treatment due to their institutional background, business management, risk control capabilities and other reasons. Multiple advantages, such as policy support, tax reduction and exemption, and financial support, have improved the reputation of CD Finance and its efficiency of business development, and reduced its operating cost. For the MFIs that cannot enjoy subsidies and benefits, their minimum sustainable interest rate should be higher. Therefore, MFIs shall reduce costs by improving operational efficiency, thereby lowering the minimum sustainable interest rate and increasing the financial inclusion rate. And financial policy makers shall clarify the boundaries between microfinance and other financial services and develop more targeted policies.

Appendix 1: Microcredit Practices of CD Finance7 CD Finance is a social enterprise dedicated to providing microcredit to middle and low-income groups in rural areas. It aims to support income-generating activities of middle and low-income families in poor areas by means of mortgage-free and door-to-door microcredit. In addition, it also offers various forms of non-financial services, in order 7 Descriptive

materials used in this article refer to the information provided by CD Finance headquarters, the annual reports on the official website of CD Finance and CD Finance Microcredit Cases written by Li Jing (included in the book Microfinance in China) etc.

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to improve customers’ comprehensive ability and achieve sustainable poverty alleviation and prosperity.

Development History Pilot Project The microcredit business of CD Finance can be traced back to the microcredit pilot project of foreign non-governmental organizations in China in the 1990s. In 1996, the World Bank Loans Poverty Alleviation Project in Qinba Mountain Area was initiated, including the microcredit pilot projects of Western China Human Resources Development Center implemented in Langzhong Sichuan and Ankang Shaanxi. In 2000, China Foundation for Poverty Alleviation (CFPA) had fully taken over the microcredit programs of Western China Human Resources Development Center and established the microcredit project department. In 2001, the Poverty Alleviation Office of the State Council accepted CFPA as the pilot unit of microcredit poverty alleviation, suggesting the official approval of the microcredit poverty alleviation pilot project of CFPA. During this period, microcredit of CFPA was co-run and co-managed by local governments. To be specific, local governments (generally led by the Poverty Alleviation Office) established the county service agency, and CFPA signed agreements with local governments, provided fund, product design and technological support and carried out supervision and management etc.

Transformation CFPA microcredit project department was officially established in January 2005, devoted to the strategic transformation and management of microcredit; and it has been running under CFPA all this time. At the end of 2006, CFPA received a credit line of RMB 100 million yuan from China Development Bank, becoming the first microcredit institution acquiring a wholesale loan from a bank in China. It indicated that microcredit of CFPA started using interest-bearing loans, while,

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by comparison. special government funds and endowments had been adopted before. In the year of 2007, the total loans released exceeded RMB 100 million for the first time, and the pilot project of personal loans began. In 2008, CFPA obtained a wholesale loan of RMB 20 million from Standard Chartered. On November 18, 2008, CFPA transformed the microcredit project department into CD Finance Project Management Company Limited.

Expansion By the end of 2008, the microcredit project department was transformed to be CD Finance Project Management Company Limited, a specialized microcredit project management company controlled by CFPA. In 2009, CFPA acquired a credit line of RMB 200 million from the Agricultural Bank of China. In 2010, the credit tracking system independently developed by CD Finance was put into operation, marking the new stage of professional chain management. In 2010, the number of active credit customers of CD Finance exceeded 100,000 for the first time; the loans released of the year went beyond RMB 1 billion; and CD Finance was successfully incorporated into the credit information system of the People’s Bank of China and had grown to be the largest public-welfare microcredit organization in China. In 2013, CD Finance had successfully developed Android-based smart phone business system, and transferred the management end of loan business from computers to mobile phones. By December 31, 2014, CD Finance had provided microcredit to 141 counties in 16 provinces nationwide, most of which were poverty-stricken counties at the national or provincial level, had outstanding loans totaling RMB 1.88 billion, attracted 238,000 farmers as active loan customers, achieved a risky loan ratio for loans over 30 days as low as 0.27%, and had more than 1,860 employees. In over a decade, it has released approximately 938,000 small loans to farmers and benefited over 2 million poor people.

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Structure As a national specialized microcredit organization adopting chain management, CD Finance has gradually established a group-based management method characterized by centralized research and development and unified supervision by the head office and strict implementation of regulations by branch offices. CD Finance has created a credit, finance and human resources management system under the centralized operation by the head office and formulated the uniform operation process and management system. All branches in different regions are incorporated in the management system of the head office; human resources, finance and materials are subject to the unified management of the head office; funds are operated in a closed manner; and front-line managers and credit operators of different levels are cultivated. In addition, CD Finance as constructed an independent internal audit system, which assures at least one internal audit for each branch office a year and controls the operational risk. CD Finance applies the four-line three-level matrix management system. It sets up the head office in Beijing, which consists of functional departments and is responsible for fund management, credit policy, branding, risk control and technology research and development etc., and establishes regional offices in different areas of China to guide local businesses. The branches directly under the head office are established at the county level, responsible for the release and recovery of loans in accordance with the standards of the head office. At present, it only takes three months of preparation for a new branch of CD Finance to conduct business. CD finance has been adhering to the principle of “locals serving locals”. Regional branches fully apply local management. They recruit local employees, assign loan officers to each township or town and provide door-to-door services, to make it as convenient as possible for farmers to obtain loans. Loan officers come from local rural areas, who may have not received higher education but know well about rural areas and have strong ability. In particular, the grass-root loan officers are local farmers, mostly married women aged over 30. As these women’s working ability and comprehensive quality will be improved through

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a series of business and skill trainings, they will gradually grow to be excellent loan officers. In this way, great jobs are offered to these project areas. CD Finance also performs regional distribution of loan officers based on the loan officer’s location and personal ability as well as the population density of the region. Loan officers may manage 20,000 to 40,000 people, and, on average, each loan officer is responsible for the management of over 20,000 people. CD Finance specially designs the examinations for front-line employees, to make sure that they regard micro loans as their long-term careers, assure the security of each loan, and develop businesses and expand the volume of credit.

Products and Services Credit Products CD Finance supplies simple credit products, including the jointguarantee credit loan, and the personal credit loan that was only launched in recent years. These two products vary in two ways. First, their ways of credit guarantee are different, as group lending is guaranteed jointly by five households, and personal lending is guaranteed by personal credit. Second, they have different limits for each single loan. The maximum amount of group lending is RMB 16,000, while the limit of a personal loan is RMB 50,000. The credit products of CD Finance show the following characteristics: First, mortgage-free design. CD Finance serves those called low-quality customers in traditional financial institutions, who have no collateral and may bring high risks. Considering the features of these customers, the microcredit products are supplied based on customers’ future ability and willingness of repayment instead of their current assets. Breaking through the thinking pattern of traditional financial institutions, CD Finance has developed the mortgage-free fivehousehold-guarantee loan and personal credit loan that adapt to rural areas.

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Second, access to rural women. As CD Finance mainly supports the development of rural families by providing loans, rural women become its major objects of lending. In this way, it improves the family status of women and also lowers the loan risk. At present, 93% of CD Finance customers are women. Third, interest rate higher than that of banks. The comprehensive rate of CD Finance micro-loans is 20.64%. The higher interest rate effectively prevents people who have other financing channels from lending from microcredit institutions, and therefore assures that the microcredit funds flow to the groups who cannot acquire loans from other financial institutions. In this way, the mission of microcredit institutions can be fulfilled. Anyone with financing ability will choose the loans of a lower cost rather than microcredit. By now 99% of CD Finance customers are farmers. Fourth, convenient lending and lower cost. If farmers want to borrow from commercial banks, they need to go to the banks in the county; but they only need to make a call if they borrow from CD Finance, as the workers of CD Finance will provide door-to-door service. Moreover, housing mortgage and guarantors are needed in most cases for the loan from commercial banks. By comparison, CD Finance offers credit loans, which save farmers’ time cost and economic cost to a great extent. Furthermore, in order to satisfy different demands of customers in different regions, CD Finance released such new products as agricultural loans and pastoral loans etc. in 2014.

Services in Addition to Micro-Loans Free insurance: CD Finance cooperates with China Life. Since 2009, CD Finance started introducing commercial insurance and presenting “term life insurance” to all farmer customers as a gift. If exposed to any accident, the farmer will be exempted from remaining debt, so farmers’ repayment pressure decreases. In 2014, CD Finance had provided free credit life insurance to 253,000 customers, with the total amount insured reaching RMB 2.8 billion and the total insurance premium reaching

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RMB 4.73 million, 40% higher than that in the previous year. 137 families suffering from death or accidental disability had been exempted from debts in this year. Agricultural technology training and information technology training: Most farmers borrowing micro loans are engaged in agricultural production. CD Finance provides diversified agricultural technology training for free based on local agricultural characteristics, to equip farmers with confidence and ability to develop income-generating projects. In 2014, CD Finance had offered more than 4,000 agricultural technology trainings of various forms to over 20,000 customers and released more than 60,000 copies of technology materials. Rural financial education: CD Finance updates customers’ financial management concept, increases their incomes, lowers risks, and helps them realize the sustainable development of their families. In August 2011, the information of farmers borrowing micro loans from CD Finance was officially included in the credit information system of the People’s Bank of China, and all transactions of CD Finance were imported to the credit system. More than a hundred thousand customers who had no loan record in formal financial institutions had their own credit records all at once.

Risk Management Risk Management of CD Finance The current risk management system of CD Finance consists of three parts, the risk management department, risk management system and credit tracking system. CD Finance mainly adopts follow rick control measures: small loans, joint guarantee, information transparency among different regions and relatives and moral culture. Small loans mean that the maximum amount of a loan is RMB 50,000, and the average loan amount per capita is RMB 10,000. The small amount principle assures that a single loss will not exert big influence on CD Finance and the risk is relatively decentralized, lowering the unsystematic risk. 10-month equal repayment of principal and interest is adopted, so farmers’ repayment pressure is amortized in the whole loan

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period, making the loan more acceptable to farmers. In this way, small loans show no direct impact on farmers’ major investments, and will not induce farmers’ willful default at the cost of credit. Therefore, small loans are safer and more effective for risk control. Joint guarantee is the most important method of risk control for loans. Group lending fully arouse farmers’ initiative to participate in financial activities and makes full use of the trust and morality formed in interpersonal communication in Chinese local society. It not only relieves information asymmetry, but also increases the reputation cost if the helped breach the contract, thus reducing the adverse selection and moral risks that are commonly seen in borrowings. Loan officers have better local connections. As they come from the local village or neighboring village, they are more acquainted with the farmers and can better promote credit products and control the risk in real time. CD Finance mainly runs businesses in poor areas in north China, where the people are simple and honest and moral discipline plays an important role. Therefore, morality constitutes another important risk control measure against loan defaults.

Operation Process of CD Finance Loans Distinguished from formal financial institutions that mostly control risks depending on the scoring model or loan officers’ experience and judgment, CD Finance cannot grade customers’ credit based on customers’ previous transaction records since most customers have no credit record at all. Loan officers, except those from the front line, cannot timely acquire information on customers’ repayment ability or rapidly develop risk response measures. Moreover, considering the small amount of loan and dispersed customers of CD Finance, it would take a higher time and economic cost to control risks in a traditional way. Hence CD Finance applies a special procedure of loan for risk control. CD Finance has established a whole set of operation process of providing a loan, by which all farmers’ self-supporting service communities and microcredit companies must strictly comply when releasing

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and recovering loans. In addition to few types of products, CD Finance also adopts uniform operation process nationwide. The regulations on the processes of farmer selection, information transfer, loan release, loan recovery and risk monitoring etc. are expressly provided and are incorporated in the information system of CD Finance. This special credit model also improves risk control. 1. New loan application. To apply for a loan, the new customer shall first organize a joint-guarantee group and then call the loan officer for loan application. The loan officer will review the information on site, provide loan knowledge training, record necessary information such as the identity card, family status and loan purpose etc. on the same day and check with the information system, and then submit the application to the service community. In this period, the customer does not need to offer any official application material, as all agreements are formalized. Joint guarantee plays an important part in the selection of customers. Since neighbors often communicate with each, have rich and high-quality information about each other and know each other’s background and moral quality well and deeply, some bad customers are directly screened out by joint guarantee. In most cases, the five households of joint guarantee are neighbors, friends or relatives. Joint guarantee is an invisible process of credit investigation and reduces information asymmetry. However, vigilance shall be maintained against the credit group completely made up by people of bad reputation. 2. Identification of customers’ credit rating. The maximum amount of loan varies for different grades of customers. The customer applying for a loan for the first time is identified as Grade 1; the customer who had applied for a loan and made repayment on time will be identified as Grade 2 if he/she applies for a loan again; and the customer will be automatically ascended to Grade 3 if no default in two loans. The maximum amount of loan for Grade 1 is RMB 10,000, compared to RMB 12,000 for Grade 2 and RMB 16,000 for Grade 3. In the meantime, the comprehensive rate for Grade 3 customers decreases from 20.64 to 19.2%. CD Finance restricts the borrowing amount for the sake of risk control.

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3. Loan review. This process includes office review and field investigation. Office review mainly examines whether the age, amount and risk of member groups comply with requirements and generally takes 2–3 days. Field investigation mainly aims to check the results of the training performed by the loan officer on the customer, including whether the customer is fully aware of the loan liability and repayment requirements etc. before the release of loan. After field review, the customer will sign and put his/her thumb print on the documents and fulfill the loan formalities. The loan officer’s control of loan risks constitutes a major part of risk control. The whole review process from the establishment of the joint-guarantee group to loan release lasts up to 10 days. 4. Loan release. The county service community is responsible for the release of loans. In general, loans are released jointly by the accountant, cashier and supervisor on site, when a group photo of the customer, loan officer and the supervisor of the service community shall be taken. The loan officer shall record the customer’s repayment information and the group photo in the CD Finance credit management system on the same day. 5. Loan recovery. The loan is recovered by the loan officer. The customers shall first give the repayment to the group leader, from which the loan officer will collect the repayments. The loan officer shall communicate with customers on the phone, make an appointment 2 or 3 day ahead, visit the customers to recover the loan and deposit the cash received in the bank (or credit cooperative) nearby on the same day. Each loan officer has one basic account exclusive for loan recovery, where no withdrawal can be made. After the loan officer deposits the repayment, the money will be automatically transferred to the account of the service community. In order not to affect the group’s reputation, the members of the credit group will help each other out in repayment, so the repayment risk in the joint guarantee method is born by three to five people rather than only one person. The unsystematic risk borne by the company has been evidently lowered. 6. Risk monitoring. After the release of the loan, the service community and the loan officer shall call customers back for risk monitoring in

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the later stage. In this process, the loan officer is considered as the major factor of risk control. The loan officer shall carry out collective trainings on the group, visit customers’ homes, make irregular return visits and inquire about customers when passing by etc.

Funding Sources Public-welfare micro-loan companies mainly rely on non-profit capital that stresses social values in the early stage. However, along with scale expansion, these companies have a growing need of for-profit funds from the market. To be specific, funds for CD Finance credit come from following channels. 1. Investments. Investments refer to the funds given by the investors of CD Finance, including domestic and foreign contributions collected by CFPA, the donations made by commercial institutions and interest-free loans provided by some non-governmental organizations to CFPA. 2. Wholesale loans. CD Finance has cooperated with China Development Bank, Standard Chartered, Agricultural Bank of China and Bank of Beijing successively and acquired wholesale loans from these banks. Considering the wholesale loan interest rate of approximately 6.5% and the guarantee cost, the total cost of capital ranges between 8 and 10%. By now, wholesale loans have become the major funding source of CD Finance. 3. Strategic investors. Strategic investors can replenish the capital fund of CD Finance. 4. Asset securitization. In 2014, the China Securities Regulatory Commission approved the CD Finance public-welfare micro-loan asset support special plan, totaling RMB 500 million, which opened the channel of capital market financing. In 2015, the product of CD Finance asset special plan of RMB 500 million had been completed, suggesting that the brand new capital market financing model of public-welfare microcredit has become a conventional financing model.

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Moreover, CD Finance also enjoys the preferential tax policy exclusive for financial institutions that release agriculture-supporting loans. According to the circular published by the State Administration of Taxation and the Ministry of Finance in 2010, the interest income of agriculture-supporting loans smaller than RMB 50,000 released by CD Finance is exempted from the business tax. CD Finance is the only one among public-welfare microcredit institutions that enjoys the privilege.

Development Performance Through years of exploration and efforts, CD Finance has become the largest public-welfare microcredit institution in China and is playing an important role in rural finance. In 2015, Planet Rating, a world-famous microcredit rating agency, evaluated the microcredit programs of CD Finance and concluded that CD Finance ranked Top 20% among global microcredit institutions in terms of its business management and social performance management. The asset size of CD Finance has been expanding swiftly in recent years. In the year of 2014, CD Finance had released 253,001 loans, totaling RMB 2.9 billion. By the end of 2014, the amount of outstanding loans of CD Finance reached RMB 1.9 billion, having increased by 58.46% compared to the previous year; and the number of active customers attained 237,817. The total assets have gradually grown from RMB 800 million in 2011 to 2.24 billion in 2014 by 1.8 times; and the owner’s equity has increased from RMB 270 million in 2011 to 570 million in 2014 by 1.1 times (See the major data of the balance sheet in Table 4). While expanding the scale of assets, CD Finance also maintains favorable asset quality. In 2014, 100% of the loans provided by CD Finance were mortgage-free, 69% had a loan limit lower than RMB 10,000 and 63% were directly applied to the farming and breeding industry. Since 2006, the risky loan ratio (PAR) for loans over 30 days of CD Finance has been stably below 0.5%, and was merely 0.27% in 2014. CD Finance has realized commercial sustainability and social values at the same time. It has made profits cumulatively since 2008, which

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Table 4 Assets and liabilities of CD finance 2011–2014 Unit: RMB 10,000 Current assets Non-current assets Total assets Current liabilities Non-current liabilities Total liabilities Owner’s equity

2011

2012

2013

2014

77,883.02 1623.90 79,506.92 52,531.16 0 52,531.16 26,975.76

115,074.63 1858.09 116,932.72 86,990.66 750.00 87,740.66 29,192.06

145,979.33 2352.61 148,331.95 88,023.40 5690.00 93,713.40 54,618.55

220,927.36 3109.31 224,036.67 106,090.73 60,824.35 166,915.08 57,121.59

Data source 2011–2014 annual reports of CD Finance

demonstrates that releasing loans to poor farmers is a sustainable commercial model but not transfusion-like poverty alleviation. Equal cooperation is established between CD Finance and customers, by which CD Finance provides microcredit to farmers, earns appropriate interests to support its own sustainable operation and further offers customers with credit services, and by which customers apply for and acquire loans for their development, generate new incomes, improve their living standards and get rich. In 2014, CD Finance achieved an operating income of RMB 310 million and a net profit of RMB 19.43 million, reaching a total return on assets (ROA) of 1.04% and a net return on equity (ROE) of 3.48%. In regard to the service area and object, in 2014, 81% of the loans of CD Finance were given to poor counties; 97% of active customers were farmers; 81% of customers had never borrowed from other financial institutions; and 93% were women. As CD Finance provides mortgage and guarantee-free loans and door-to-door services, customers can apply for and repay the loans at home, which significantly save farmers’ economic and time cost. In the article The Role and Influence of Microcredit – Empirical Research with China Foundation for Poverty Alleviation Microcredit as Instances, the Center for Chinese Agricultural Policy, Chinese Academy of Sciences, appraised CD Finance as follows: “CD Finance microcredit is popular among farmers. Most microcredit customers say that CD Finance is always their first choice if they need money. 84% of microcredit farmers believe that microcredit is more convenient and rapid compared to formal financial loans.” See the composition of the intended use of CD Finance loans in Fig. 1.

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Composition of Intended Use of Loans (Based on the number of loans)

Farming industry Breeding industry Production and processing industry Transportation industry Wholesale and retail trade Social service Rural house renovation Others

Fig. 1 Composition of the intended use of CD finance loans (Data source 2014 annual report of CD Finance)

Summary and Discussion Attribute of CD Finance Is CD Finance a poverty-alleviation agency or a financial institution? As an organization established by CFPA, CD Finance adheres to the original intention of providing the inclusive financial service instead of making profit by releasing loans. In the rural credit market, the Rural Credit Cooperatives plays the most important role, followed by the Agricultural Bank of China and the Postal Savings Bank of China. However, these three major financial institutions see a sharp rise of the transaction cost when they offer credit to “agriculture, rural areas and farmers”. As the profit can hardly be made for the loans smaller than RMB 50,000, so these three major financial institutions can hardly reach farmers at the lowest level. For example, the Rural Credit Cooperatives covers 87 thousand farmer households as its credit customers, only accounting for a little more than 1/3 of farmers all over the country. A great number of farmers are excluded from these financial services and can only rely on private lending for financing. CD Finance is actually serving the other 2/3 farmers through micro loans instead of donations, to support their

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entrepreneurial development and achieve the goal of hemopoiesis-like poverty alleviation. In view of the particularity of the level of customers, in the rural financial market, CD Finance is a supplement to the current financial system rather than a competitor to other financial institutions. Therefore, CD Finance is not a financial institution in a strict sense based on its current state of development. It is a social enterprise derived from an NGO with microcredit as the means and poverty alleviation as the fundamental goal.

Replicability of CD Finance CD Finance is a successful case among China’s public-welfare microloan companies. Hence people concern about whether the model of CD Finance can be duplicated, and whether other agencies can also achieve both commercial sustainability and public welfare in rural credit market by copying the operation procedure and management method of CD Finance. CD Finance is a specialized microcredit project management company controlled by CFPA, while CFPA is a corporation under the Poverty Alleviation and Development Office of the State Council. Based on its professional microcredit management ability and outstanding social performance, CD Finance has won vigorous support of the government in terms of preferential policies, qualification to microcredit pilot projects and bank funds etc. The support is mainly embodied in the follows: CD Finance enjoys the preferential tax policy exclusive for formal financial institutions that the agriculture-supporting loans smaller than RMB 50,000 are exempted from the business tax and income tax; CD Finance is allowed to access to the credit information system of the People’s Bank of China as a regional financial institution and shares its customer credit information in the system; CD Finance can acquire wholesale loans totaling hundreds of millions yuan from large financial institutions including China Development Bank and Agricultural Bank of China; it is the first microcredit institution admitted to the pilot project of credit asset securitization after the registration system is implemented; and it has received the visits by and the care of relevant departments.

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Featuring manifold advantages in policy support, tax relief and financial support, CD Finance has improved its reputation and business efficiency, and meanwhile lowered its operating cost. By comparison, it is more difficult for other ordinary public-welfare micro-loan institutions to survive the severe market challenges, practice their social missions, and transform their operating principles by introducing the marketoriented management model and operation system. As a matter of fact, policy support and the institutions’ favorable development lay the foundation for each other. If a microcredit institution is weak in operation and management, it may finally lose the support it acquired due to its management problem.

Appendix 2: Research Report on Branches of CD Finance In order to better understand the microcredit operation process, risk control method and development achievements of CD Finance, the research team8 of Microfinance Research Center of Renmin University of China went to Haixing County, Cangzhou, Hebei Province for field research in April 2015. The research was mainly conducted on Haixing Farmers’ Self-supporting Service Community and microcredit customers through in-depth interviews with relevant personnel and on-site observation of the lending process.

Overview of Haixing Farmers’ Self-Supporting Service Community Located in the southeast of Hebei Province and by Bohai Sea, Haixing is one of the key counties of national poverty alleviation and development. A famous local saying goes like “Planting ten thousand hectares of land to harvest a thousand kilograms of grain”, which demonstrates the extremely low grain output. Covering 965 square kilometers, Haixing 8 Members:

Yan Li, Yu Luo, Gao Wei, Duan Hongbo, Lin Wang, Lian Anhexia, Li Zhenni.

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County consists of 3 towns and 4 townships and has a population of 220 thousand. In 2014, its regional gross domestic product (GDP) was RMB 3.73 billion, its per-capita disposable income of urban residents was RMB 19,166 and per-capita net income of farmers was RMB 5,196.9 The branch of CD Finance in Haixing, Haixing Farmers’ Selfsupporting Service Community, as established on March 7, 2011, providing services to 7 townships and towns and 197 administrative villages. It has 10 staff members: three are executives, including a director, an accountant and a cashier (the accountant and the cashier concurrently work as supervisors); and seven are loan officers. The office of the Farmers’ Self-supporting Service Community is in an ordinary two-story building, and the office conditions are not that good. The director of the Farmers’ Self-supporting Service Community is selected by the head office of CD Finance. The major selection criterion is the grassroot work experience and the sense of responsibility, followed by the requirement on the professional level of finance. The current director Wang had held a deputy post in three townships, served as the deputy director of the poverty alleviation office of the county and worked in grass-root units for 10 years. Wang had no finance-related experience before taking the office. The head office has set up the process. We shall implement the process, which means to communicate with the people face to face. —Director Wang When choosing loan officers, the main points are whether the candidates have credit problems and extensive social connections, and whether they are living in the villages of convenient traffic, high population density and large information flow, which may facilitate their travel and information gathering. Loan officers shall be strictly examined in an all-round way. A person’s reputation is very important in rural areas, so the personal character is considered the primary factor. Loan officers shall have a good understanding of local conditions and live in an advantageous region, i.e. the towns or townships of a large size along the vital communication line.

9 Data

source: Haixing County Government Work Report 2015.

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In addition, they shall have many relatives, grasp a large amount of information and be hardworking. Loan officers of Haixing Service Community are 40 years old on average. Their base salary is RMB 2,000 per month, which will be raised according to their performance. At present, their monthly salary is RMB 3,000 or 4,000 on average, and each can earn a net income of approximately RMB 2,000 or 3,000 after the “five insurances and the housing fund” and all traffic, communication and advertising costs are deducted. Compared to the urban per-capita net income of RMB 2,700 per month in Haixing, a poverty-stricken region, the monthly salary of RMB 2,000 or 3,000 and the welfare of “five insurances and the housing fund” are fairly good for ordinary farmers. Since the income is associated with the performance, workers keep high enthusiasm. CD Finance assigns loan officers regionally according to the regions the loan officers live in, population density and loan officers’ personal ability. Loan officers provide services to the entire population of the county, i.e. 220 thousand people, and each of them is responsible for 31 thousand people on average. Loan officers shoulder heavy workload. For example, to run a one-year loan (group) of RMB 50,000, loan officers have to visit the group on site for at least 12 times, 1 for investigation, 1 for loan release and 10 for loan recovery. CD Finance’s loan risk control largely depends on the grass-roots loan officers’ understanding and judgment of the farmers. Therefore, the sense of responsibility of grass-roots loan officers plays a vital part in the execution of loans; and credit risk will occur if they are out of control. CD Finance incorporates the total amount of loans, the recovery of loans and the number of non-performing loans in the examination of loan officers. For the appraisal of the director of the branch office, the “staff turnover” is included. With this special design, CD Finance makes sure that its employees regard micro loans as their long-term careers, assure the security of each loan, and develop businesses and expand the volume of credit.

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Specific Process of Microcredit Loan Process The loan application is completed in the following 4 steps: 1. The farmer applies for borrowings to CD Finance, and the loan officer visits the farmer and understands the farmer’s intention and purpose of borrowings. 2. The loan officer introduces the rules of the application for and repayment of loans to the farmer. 3. The farmers set up the credit group independently, which generally comprises 5 people who all have borrowing demand and bear the joint and several liability of repayment for each other. Five-household joint guarantee was adopted before 2014; and after 2014, three to fivehousehold joint guarantee has been applied according to specific local conditions. There is no special regulation on the group members, so relatives (except the immediate family) or neighbors can establish a group together. As a strong entrepreneurial awareness has been formed among local farmers, the “borrowing” culture has been created here. In this place, when a son needs money to start a business, he often chooses to apply for a loan instead of borrowing from his father, as he does not want to depend on his father. Group lending has fully aroused farmers’ initiative to participate in financial activities and makes full use of the trust formed in interpersonal communication in Chinese local society to solve the issues in pre-loan review, loan monitoring and after-loan management and reduce the adverse selection and moral risks that are commonly seen in borrowings Grouping is not a simple task. Farmers have to organize a group by their own. Grouping relies on farmers’ reputation, while some relatively rich farmers cannot find a group. Once a credit officer asked a farmer whether she was willing to make repayments for the other four people if they could not pay the loan. She said yes because they had helped her out when she was building her house. In another case, a

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customer who worked in the transportation industry had an accident, other group members paid money back for her…This is how the joint-guarantee group works. Group members make commitments to each other while grouping. As these women live in the same village and know each other well, they are willing to repay the loan for others and help each other. 4. The loan officer reports the borrowing demand to the Service Community for approval. Upon approval, the loan will be released.

Characteristics of CD Finance Microcredit The financial institutions that can offer credit services in Haixing include commercial banks, rural credit cooperatives and micro-loan companies, but they target customers of a higher level and provide a higher loan limit compared to CD Finance. Microcredit of CD Finance is distinguished from these financial institutions by following characteristics: 1. CD Finance adopts microcredit. By providing borrowings of a small amount, it shows no direct impact on farmers’ major investments, and will not induce farmers’ willful default at the cost of credit. It is mainly used to help meet an urgent need or used as a supplement. To be specific, customers can be divided into three grades by their repayment records. The customer of a higher grade can borrow more money. The customer applying for a loan for the first time is identified as Grade 1 and can borrow no more than RMB 10,000; the customer applying for a loan for the second time will be identified as Grade 2 and can borrow no more than RMB 12,000; and the customer applying for a loan for the third time will be identified as Grade 3 and have a loan limit of RMB 16,000. Our aim is to “assist micro-entrepreneurship”. By “assisting” we mean lend some money when entrepreneurs still have not enough money after self-financing instead of giving them all the money they need. 2. The term of the loan is one year, and the repayment in equal installments is adopted. The loan interest rate of the equal repayment of principal and interest is 13.5% (annualized interest rate of 20.64%,

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covering the insurance), which can be further lowered to 12.5% (annualized interest rate of 19.2%). The repayment shall be made since the third month, and 10-month equal repayment of principal and interest is adopted, so farmers’ repayment pressure is amortized in the whole loan period, making the loan more acceptable to farmers and lowering the risk of the institution. The higher interest rate effectively prevents people who have other financing channels from lending from microcredit institutions, and therefore assures that the microcredit funds flow to the groups who cannot acquire loans from other financial institutions. In this way, the mission of microcredit institutions can be fulfilled. According to the practice, this interest rate is acceptable to farmers. The loan is released based on the credit and needs no mortgage or collateral. At present, cash is used in the borrowing and repayment. The loan officer directly sends the borrowing to and collects repayments from the customers’ homes. Later electronic money will be gradually adopted. All group members must be on the spot when the loan is released, to attend the special ceremony of signing, sealing and photo-taking. The borrowers shall submit their ID cards and household registers. After having gone through the formalities, the loan officer shall immediately send relevant information to the head office for real-time monitoring through the mobile end of the credit tracking system independently developed by CD Finance. Borrowings can be applied to multiple purposes, including production and business such as planting, breeding and processing, durable goods consumption such as house building, and also medical expenses, wedding costs and school expenses etc. There is no specific provision on the purpose of borrowings. Customers can apply for the loan as long as they can repay it. The loans are released to farmers at the bottom level and are exclusive for married women aged between 20 and 65. Farmers use their mobile phones only to make or answer calls, and some of them have no fixed-line phone, not to mention smart phones.

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Some women even cannot sign their names and have to learn it on the spot. In regard to the borrowing, women have a greater sense of responsibility and risk control than men do. In this region, men cherish friendship a lot and they may spend their money when they drink together. Women will not leave their families and run away. 8. Convenient access to loans. The advertisements of CD Finance microcredit are seen in many places on the walls at the village entrance 。When farmers were asked why they applied for loans from CD Finance in the investigation, the most common answer was “convenience”. They only need to make a call, and CD Finance will offer them door-to-door services, saving them from complex formalities and high transport expenses and lowering their time and economic cost. Moreover, distinguished from the mortgage-free credit loans provided by CD Finance, the banks’ loans require housing mortgage or guarantors’ guarantee, so the traditional method of loans costs too much compared to farmers’ small capital demand. After the farmer calls to apply for a loan, a loan officer will provide the door-to-door service, and must visit the farmer customer’s home for at least 12 times for each loan. The loan officer visits the customer for the first time to ask for information, to release the loan in cash for the first time, and to recover the loan monthly in the 10-month repayment period totaling 10 times.

Microcredit Risk Control Model of CD Finance CD Finance has established a whole set of loan process applicable to rural microcredit. However, the risk control means that play a part are not the credit system or the risk control model, but are the “folk methods” that cannot be standardized or quantified, which include the small loans, joint guarantee, information transparency among different regions and

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relatives and moral culture. It is said that Haixing Service Community has run into no overdue loan in the four years since its establishment. Small loans mean that the maximum amount of a loan of CD Finance is small, and the average loan amount per capita is RMB 10,000. By doing this, it assures that a single loss will not exert big influence and the risk is relatively decentralized, lowering the unsystematic risk of CD Finance. The joint-guarantee group assures risk control based on the independent selection and mutual supervision among group members. CD Finance does not involve the establishment of the group and the allocation of the loan, but mainly examines the group members’ willingness to shoulder the joint responsibility, i.e. the connections within the group. The loan officer attaches importance to the credit of the whole group. Though the director is responsible for loan approval after the loan officer reports the group’s information, it is the grass-roots units’ task to decide whether to release the loan. Two supervisors shall conduct investigation at the farmers’ home at the release of the loan. We have rejected the cases by which the applicant borrowed money for others, rejected to bear the joint responsibility or failed to acquire the consent from her husband. We always ask the customer whether she has reached an agreement with her husband, as we can only lend money if both of them agree on applying for a loan. Since the credit group is set up independently by villagers, group members will examine each other when selecting members. Any rational person will reject anyone of bad reputation to avoid any impact on the group credit. Since neighbors often communicate with each, have rich and high-quality information about each other and know each other’s background and moral quality well and deeply, greatly reducing information asymmetry. Based on this screening method, all members of the credit group will enjoy a good reputation. However, vigilance shall be maintained against the credit group completely made up by people of bad reputation. In the pre-loan stage, some bad customers are directly screened out by the joint guarantee system. This system is an invisible process of credit investigation. In order not to affect the group’s reputation, the

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members of the credit group will help each other out in repayment, so the repayment risk in the joint guarantee method is born by three to five people rather than only one person. The unsystematic risk borne by the company has been evidently lowered. However, this joint guarantee system is not universally applicable to all places, but only applies to rural areas where people are simple and honest, neighbors are close and the population is relatively dense. Loan officers have better local connections. They are mostly laymen in finance and do not meet the rules of official financial institutions. As they come from the local village or neighboring village, they are more acquainted with the farmers and can better promote credit products and control the risk in real time. As a matter of fact, the work of CD Finance is different from that of the banks, and will benefit from visiting relatives and friends. Loan officers have relatives in their own villages or neighboring villages and have a deep understanding about local customs and villagers’ economic status and family conditions. CD Finance lends money only to the people at the bottom level but not to large projects or anything like that. Its loan officers have a special understanding of the grassroots. Instead of going to and getting off work at a regular time, they visit farmers including their relatives to find out more information.

Field Observation of the Loan Release of CD Finance The research team visited Zhangchu Village and observed the practical process of loan release by the Serving Community. In this case, the borrowing team was made up by four women aged between 30 and 50 who all applied for the loan for the first time. They intended to use the loan to raise chickens or build a house. The specific loan information is as follows: Group leader Wang borrowed RMB 8,000 to build a house; Group member Han borrowed RMB 8,000 to build a house; Group member Wang borrowed RMB 6,000 to raise chickens; Group member Zhang borrowed RMB 8,000 to raise chickens.

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This group borrowed RMB 30,000 in total, with a term of one year and an interest rate of 13.5%. Half of the borrowing was applied to production, and half to consumption. Two supervisors respectively asked group members some questions, followed them to their homes for investigation, then came back to the group leader’s home to verify borrowers’ willing to borrow, read out the rules of the loan, examined borrowers’ certificates if no objection to the rules, had borrowers sign and put their thumb prints on the documents, and released the loan in cash. The four borrowers counted money on site for confirmation, held their own borrowings and took a group photo with CD Finance workers again. The questions that two supervisors asked repeatedly included: “Do you clearly know that you have to repay the loan for other three group members if they cannot make repayments?” “How will you use the borrowing?” “Have you reached an agreement with your family?” “Is there anything you don’t understand about our rules?” In addition, the research team also asked about the proportion of loan amount to demand. Take the house building as an example. The customer borrowed RMB 8,000, while RMB 60,000 was needed to build a house. The borrowing amount accounted for approximately 13% of the demand, and the rest should be raised by the customer or borrowed from other channels.

Field Investigation on Previous Customers of CD Finance The research team visited Biwangwen Village and had an interview and deep communication with farmers. The team first visited Liu in Biwangwen Village. Liu, 53 years old, is a Grade-3 customer. Her husband and son are working out of town. She applied for a loan for the first time in 2013 to raise pigs, and now she has become a specialized pig farmer. She raises 80 or 90 pigs and the feed for each cost over RMB 1,300, so she needs approximately RMB 120,000 for capital turnover. A pig becomes full grown and ready for slaughter after 9 months of breeding and earns the farmer around RMB 400. Therefore, the total income per year reaches over RMB 30,000.

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Mentioning why she chose CD Finance for borrowing, Liu said it was convenient and only one call was needed to apply for a loan, while the rural credit cooperatives required mortgage for a loan and was more complicated. As a Grade-3 customer, Liu could borrow a maximum of RMB 16,000 at an interest rate of 12.5%, but she borrowed only RMB 8,000 in most cases. Regarding this matter, she answered that she mainly depended on her own money and applied for the loan only for capital turnover. Other group members also borrowed money to run businesses, including shrimp aquaculture, pig raising and driving. When establishing the group, Liu gave priority to the group member’s personal character and credit, and then examined whether the member could make good money for timely repayment. She also considered whether the member’s family members had a stable income such as salary. She had organized three groups successively by now, which were made up by the same members. In over two years, they had not encountered any problem. When asked why she did not borrow such a small amount of money from her friends or relatives, she answered: “Everyone is willing to do something with money rather than saving it.” “Sometimes I need only 1,000 yuan, but I find it very difficult to borrow it in the rural area.” “Nobody saves money. They all invest all their money to do businesses.” There was also a subjective reason that it is unworthy to owe other people favors for such a small amount of money, especially in rural areas of close local connections. Then the team visited Liu in Biwangwen Village. Liu is nearly 50 years old, Grade-3 customer, and borrowed RMB 16,000 for her third loan at an interest rate of 12.5%. She borrowed money mainly for her husband and her oldest son’s plastic steel window business, though she knew only a little about their business conditions. Despite the success of their business, their tie-up money had reached over a hundred thousand yuan. Liu was not the group leader. Another member of the group also ran the plastic steel window business, and others were raising chickens or ducks. All members were quite familiar with each other. Liu’s family income per year was RMB 30,000 to 50,000. When asked about her demand on microcredit, Liu answered: “Borrowing is used only as a last resort considering the interest.” When asked “how to find group members who also need money”, Liu said she would ask her acquaintances, and “they

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might not need money at that moment, but I can wait until they need and then borrow money together.”

Conclusions and Discussions 1. Being an effective supplement to rural finance in China, the microcredit of CD Finance serves the bottom level as a part of inclusive financial service. The service objects of CD Finance microcredit, completely distinguished from that of traditional financial institutions and emerging for-profit micro-loan companies, are middle and low-income farmers who can barely acquire any loan from financial institutions. The microcredit service of CD Finance is inclusive and different from transfusion-like poverty alleviation, as farmers will repay the principal and certain interest. 2. The loan interest rate of CD Finance is not low, but is acceptable to farmers, demonstrating the rationality of the high interest rate. A substantial portion of the interest income is used to cover the operating cost. According to the investigation, the operating cost of microcredit is high. Considering the “labor-intensive” loan process of CD Finance, loan officers have to spend a lot of time and energy, which cannot be realized by traditional financial institutions. Therefore, the income of loan officers shall be substantially raised as compensation, leading to the significant rise of the cost. The branches of CD Finance are mostly set up in poverty-stricken areas, and loan officers are all farmers. They can be engaged in agriculture and loan release at the same time, so the labor cost is still low. Under the circumstances, “labor-intensive” loan process is still the better choice. When the local labor cost increases or the CD Finance model is promoted to richer areas, this credit model may cause a higher operating cost. 3. The main risk-control mechanism of CD Finance is five-household joint guarantee. The service community plays a significantly smaller part in loan monitoring, and mainly develops customers and releases and recovers loans. The risk control before, during and after the loan is basically completed by the headquarter office and the loan groups.

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This is different with traditional commercial banks, the risk control of which stresses on the technical model to some extent. By comparison, micro-loans do not need the risk control model, but mainly rely on the system design, i.e. three to five people joint guarantee group. However, the group joint-guarantee model is neither applicable in sparsely populated areas (such as prairies) nor effective in areas of damaged rural culture. Microloans also do not apply to places of high per-capita income and developed commerce, since the small amount of loans can hardly satisfy the demand here. Hence the promotion of microcredit shall be in line with the local actual situation. 4. The five-household joint-guarantee of CD Finance is also exposed to certain operation difficulties and potential risks. The first problem is “how to match borrowing needs to establish a group”. According to the rules, a group can only be set up and the loan can only be released when 3 to 5 borrowers are brought together. However, different people’s borrowing demands do not always emerge at the same time, and it is even more difficult for people who know and trust each other to have a credit demand of a similar amount within a similar period. Farmers have to match their needs within the village, resulting in a high transaction cost. The second problem is how to prevent “multiple people borrowing money for only one person”, that is they conspiring together to acquire the loan by cheating. As group members are all relatives or close friends, they may lie that they all need the borrowing but give all the money to one person. According to the investigation, the RMB 10,000 per-capita loan limit is not high even in poor areas, so collusion may arise when the customer’s large capital demand cannot be satisfied. Against this situation, loan officers shall improve their identification ability, and the loan limit shall be appropriately adjusted based on research accordingly. 5. CD Finance provides one-year loans and the repayment shall be made since the third month; but it takes as long as half a year or a whole year before the return on the investment in production and operation is acquired. It means that farmers have no cash flow income and have to seek funds from other channels for monthly repayment. Another flexible alternative of farmers is to apply for a loan greater than their practical need. For example, they need to invest RMB 5,000 and can

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only gain revenue a year later, so they borrow RMB 10,000 to assure that they do not need to repay the RMB 5,000 too early. Farmers are rational enough to find the best balance between the interest expense and the capital demand, forcing the expansion of the credit scale.

Case Comments Microfinance offers necessary financial services to the low-income group ignored by traditional finance and is characterized by a small amount, timeliness and convenience. In practice, we often encounter these questions: what are the differences between microfinance and traditional finance, and between microfinance and charity? This case study not only discusses the definition of microfinance, but also clearly identifies the differences between microfinance and traditional finance and between microfinance and charity, and proposes the concepts of the “upper boundary” and “lower boundary”. How to understand the boundary between microfinance and traditional finance, i.e. the upper boundary of microfinance? With CD Finance specialized in microcredit, the author puts forward the four differences in terms of customer positioning, loan limit, credit product and risk control measures. To be specific, microfinance targets the lowincome group s customers; its loan limit is below RMB 50,000; it adopts credit loans as its major lending mode; and its risk control mainly depends on self-managed communities. By comparison, traditional finance targets large-sized enterprises, provides large loans, offers mortgage loans and relies on professionals for risk control. These four differences demonstrate that the business mode as well as the organization, human resource structure and incentive and restraint mechanism decided by the business mode vary completely between microfinance and traditional finance. Great organizational adjustments are required if traditional financial institutions venture into microfinance. If without strategic changes, they cannot succeed. Then how to understand the boundary between microfinance and charity, i.e. the lower boundary of microfinance? According to the author, the dividing criterion is self-sustainability. Charity only gives without

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considering about the revenue, but microfinance does not and cannot do so. First, microfinance stresses on the nature of transaction of finance. The principal and interest must be repaid for a loan; otherwise it is giving but not trading. Second, if microfinance gives its money away or offers interest-free loans, the credit culture will be damaged, which may even ruin the beneficiaries. Microfinance attaches importance to both commercial sustainability and social missions, and strives to balance and adhere to the both goals. The author has put forward an important point in the case study, which is to fulfill the goal of social missions (seeking a relatively lower profit rate), and meanwhile determine a loan interest rate that enables financial self-sufficiency. According to the author, CD Finance adopts a typical microfinance credit model. Therefore, considering the high operating cost resulting from the high labor cost, the loan interest rate of microfinance is higher than that of large or middle-sized mortgage loans, thus to achieve financial sustainability. Since there is no other measure to lower the cost, the high interest rate has become an unavoidable feature of microfinance. As digital technology is playing an increasing role in lowering the human cost, it is expected that this unavoidable feature of microfinance will fade away. Commentator: Yan Li

References Beatriz Armendariz, Jonathan Morduch. The Economics of Microfinance. Shenyang, Volumes Publishing Company, 2013. CGAP. Definitions of Selected Financial Terms, Ratios and Adjustments for Microfinance. Microfinance Consensus Guidelines, 2003. Li Jing, Microcredit Cases, Editorial Committee for the Series of Microfinance in China. Microfinance in China: Public Welfare Microfinance Develops against Hardships. Beijing, China Financial & Economic Publishing House, 2013. Rosenberg R, Microcredit Interest Rates. CGAP Occasional Paper 1, 2002.

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Rosenberg R, Gonzalez A, Narain S. The New Moneylenders: Are the Poor Being Exploited by High Microcredit Interest Rates? CGAP Occasional Paper 15, 2009.

Part II Financing of Small and Medium-sized Enterprises

Small and medium-sized enterprises (SME) constitute important subjects in economic activities in either developing or developed countries. Despite their small sizes, they have created so many jobs that they are playing invaluable roles in the harmonious development of society. Since the Reform and Opening up in China, an increasing number of SMEs have emerged and created half of social wealth and employment. Along with the development of digital information technology, renewable energy and 3D printing technology, many SMEs will be established in not only the service sector but also the manufacturing sector. “Small but beautiful” enterprises will add much great scenery to Chinese enterprises. Despite all this, financing is even more difficult for SMEs compared to large enterprises, which severely restricts the development of SMEs. Among the complex factors contributing to this situation, the major reason lies in that financial institutions can hardly provide credit products to SMEs due to their high risk, relatively lower profit and relatively lower capacity of asset mortgage. Apparently, the key to solve this problem is to lower the credit risk borne by financial institutions.

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The two cases in this part will discuss the risk control of credit for SMEs from the perspectives of community construction and banking model innovation.

3 Solving the Financing Difficulty of SMEs by Community Construction: Exploration of “3+1 Integrity Alliance” Yan Li and Lin Wang

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Origin of “3+1 Integrity Alliance”

In spring 2013, Xing Yong, who had worked in a loan guarantee company for two years, quitted the job to help SMEs acquire loans. He found that SMEs had no enough collateral to acquire credit funds from banks. A simple and rude way of credit guarantee or mutual guarantee was adopted by banks. In order to acquire loans, the enterprises Members of this case study group include Yan Li, Lin Wang, Jiang Linlin and Luo Shen. This report is prepared based on the data collected on site and interview record. We would like to express our gratitude to “3+1 Honesty Alliance” for its vigorous support to our case study and report writing.

Y. Li Renmin University of China, Beijing, China L. Wang (B) Beijing University of Posts and Telecommunications, Beijing, China e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_3

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within a guarantee circle are brought together as arranged by the banks or the guarantee agencies. These enterprises know little about each other as they might have little communication previously. If the majority of the enterprises are running well, they can cooperate peacefully, but if some enterprises have debts, it will cause a domino effect and nobody can help. Instead of the passive mutual guarantee, is there an effective way to solve the financing difficulty of SMEs by which enterprises can truly help each other? Xing Yong had rented a room at a hotel nearby, thought hard about this question independently for three months, and finally come up with the initial scheme of “3+1 Integrity Alliance”. The name, “3+1 Integrity Alliance”, has a deep meaning. According to the original statement of Xing Yong, “3+1 Integrity Alliance” is a “club-style bank-enterprise credit win-win platform that follows the government guidance, cooperates with financial institutions and provides small and micro-enterprises with financing services.” The “alliance” in the name suggests that it unites everyone in an equal manner and aims to achieve mutual help and win-win through common agreements and constraints; and the name of “3+1 Integrity Alliance” means that the alliance adheres to integrity as the principle. Xing Yong still believed in integrity in borrowing and lending, the pure monetary transactions, and held that these transactions could only last long based on integrity. “3+1 Integrity Alliance” guides members to be honest, trustworthy and self-disciplined, applies rational institutional design to encourage and reward the honest and punish the dishonest and realizes the symbiotic development of alliance members. Hence “integrity” here is the concept guarantee for the alliance to achieve its vision as well as the core of the corporate culture; and the implication of “3+1” is derived from Tao Te Ching by Lao Zi, “One is the child of the divine law. After one comes two, after two comes three, and after three come all things.” “Three” symbolizes the spirit of continuity and inclusion, while “one” is the basis and the divine law. “3+1” reflects the founder’s

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pursuit for the continuous and inclusive development of the alliance. Therefore, the name of “3+1 Integrity Alliance” embodies the wisdom of Chinese Taoism as well as Xing Yong’s expectation for the Alliance. In a word, “3+1 Integrity Alliance” adheres to the principle of honesty, closely brings medium, small and micro-sized enterprises together, lowers the credit risk through mutual help, self-discipline and heteronomy, and thus solves the difficulty in financing. In July 2013, “3+1 Integrity Alliance” was officially established in a small picturesque courtyard. By placing the office in a courtyard presenting a strong atmosphere of the lyre-playing, chess, calligraphy and painting culture, the Alliance creates a relaxing environment and a sweet home for entrepreneurs. One day at the end of August, Xing Yong invited Professor Li who was engaged in financial research in a university to the “3+1” courtyard to further demonstrate the “3+1 Integrity Alliance” plan. Professor Li had been studying on the financing issue of SMEs and been fully aware that the financing of SMEs was a global difficulty. According to Professor Li’s research, SMEs make up a special group that is neither big nor “micro”. Based on the standards of the World Bank (number of people, sales income and loan amount), SMEs have 10-300 employees, a sales income between USD 100 thousand and 15 million and a loan amount between USD 10 thousand and 1 million1 (see Fig. 1).

1The National Bureau of Statistics of China identified the middle, small and micro-sized enterprises based on two standards (operating income and number of people) in 15 industries in 2011, the standards of which were relatively higher than that of the World Bank.

China 100-300 10-100 10

World Bank 50-300

10-50

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Fig. 1 Division standards of the World Bank and China for the medium, small and Micro-sized enterprises data (Source Zheng Ziqiang, General Thinking of the Development of the Small and Micro-finance System in China, Renmin University of China “Small and Micro-finance Research Center” Work Report, 2014)

Division Standards for Medium, Small and Micro-sized Enterprises: by Loan Size Type World Bank China Medium-sized USD 100 thousand – 1 million > RMB 5 million enterprise (approximately > USD 800 thousand) Small-sized USD 10 thousand – 100 RMB 1 million – 5 million enterprise thousand (approximately USD 150 thousand – 800 thousand) Micro-sized < RMB 1 million (approximately USD 10 thousand enterprise < USD 150 thousand)

Division Standards for Medium, Small and Micro-sized Enterprises: by Sales Income Type World Bank China Medium-sized USD 3 million – 15 million RMB 30 million – 300 million enterprise (approximately USD 5 million – 50 million) Small-sized USD 100 thousand – 3 million RMB 5 million – 30 million enterprise (approximately USD 800 thousand – 5 million) Micro-sized < RMB 5 million (approximately USD 100 thousand enterprise < USD 800 thousand)

Type Medium-sized enterprise Small-sized enterprise Micro-sized enterprise

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图片内容: In July 2011, the Chinese government publishes the new Division Standards for Medium and Small-sized Enterprises, which incorporated “micro enterprises” in the scope of medium and small-sized enterprises. However, China’s division standards of small and micro-sized enterprises are evidently higher than international practice, particularly the division standards by loan size. Domestic commercial banks are forced to support small and micro enterprises by policies, but they mainly serve large and medium-sized enterprises in practice. Therefore, they go after the “division standards” for their own sake. The different “division standards” exert influence on the targets and implementation effect of China’s small and micro enterprise-supporting policies. From the perspective of enterprise characteristics, compared to microenterprises, the SMEs are at the turning point of size expansion and management improvement and are therefore exposed to greater instability and risks; and compared to large and middle-sized enterprises, SMEs have weaker capital strength and technology strength as well as a smaller size, lack the ability to resist industry volatility and macroeconomic volatility and suffer from higher risks. In general, SMEs are riskier compared to the larger or the smaller enterprises. From the perspective of banks (investors), SMEs have fewer assets for mortgage and lower information transparency, so the banks find no effective measure to prevent their loan loss; and SMEs can hardly accept the high loan interest rate due to their low profit, so the banks cannot over their risks through the high interest rate. In view of this, SMEs may show a greater risk of default compared to the large and middle-sized enterprises and micro-sized enterprises. Some studies find that the financing of SMEs is particularly difficult in developing countries, where “SMEs disappear”. It means that the credit resources assigned to SMEs are fewer than those to micro enterprises and large enterprises (Ayyagari et al. 2003) (see in Fig. 2). However, SMEs are always the important economic pillars of a country and play an important role in creating jobs, GDP and taxes. Governments of the world have seriously explored the solution to the financing difficulty of SMEs.

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After listening to Xing Yong’s introduction of “3+1 Integrity Alliance”, Professor Li was very excited as she believed this might be a very good model to solve the financing difficulty of SMEs. This valuable model can improve the management ability of SMEs and the banks’ ability to identify risks ahead of time as well as their ability to deal with and solve risks by means of community construction. As to inclusive finance (microcredit), the Grameen Bank in Bangladesh created by Professor Yunus is a typical credit model based on community organizations. Depending on the borrowing group’s close connections in economy, life and study, this model successfully controls the borrowing risk, renders mortgagefree loans to the poor and breaks the traditional rule that the poor are always rejected by the banks as borrowing customers due to no collateral or credit. If run properly, the integrity alliance model will help enterprises timely discover their management issues and apply appropriate measures to solve issues and correct mistakes, relieve and transfer their risks, enable the banks to receive early warnings and thus lower the banks’ credit risks. However, distinguished from the borrowing group of the Grameen Bank in Bangladesh where members were poor women from the same village or town, prospective members of “3+1 Integrity Alliance” were the owners of SMEs who came from different corners of China and ran businesses in Zhengzhou. So Professor Li doubted how to establish the close connections and promote mutual help among the alliance members. Compared to the loans of approximately USD 1,000 provided by the Grameen Bank in Bangladesh, SMEs often apply for a loan ranging between RMB 1 million and 3 million. Professor Li wondered how to control the credit risk despite the complex business content.

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Enterprise Quantity

Comparison of Enterprise Quantity and Structure Between Developed Countries and Developing Countries

Developed Countries “Disappearing SMEs”

Developing Countries Micro-sized enterprises SMEs Large-sized enterprises

Loan Size

Credit Resource Allocation in Developing Countries (Diagram)

“Disappearing SMEs”

Micro-sized enterprises SMEs

Large-sized enterprises

Phenomenon of “SMEs Disappears” “SMEs disappears” In regard to the enterprise quantity and structure, the number of SMEs in developing countries is much lower than that in developed countries. As to the role in economy, SMEs create approximately 65% jobs and 50% GDP in developed countries, compared to respectively about 30% and lower than 20% in developing countries. Factors contributing to the phenomenon of “SMES disappears” The high tax rate and strict supervision (represented by the licensing system) gives rise to the business environment of high cost, in which large enterprises and the self-employed micro economies outside the formal economic system can survive easily, but SMEs face great difficulties. Compared to micro enterprises, SMEs have low profit and low marginal capital return and can hardly bear high financial cost. The small-loan model (such as group loans) for micro enterprises and low-income groups do not apply to SMEs. Business interpretation of the phenomenon of “SMES disappears” The financing of SMEs in developing countries is faced with great challenges, and the business models shall be innovated. More attention shall be paid to small and micro enterprises instead of medium and small-sized enterprises In credit business, the middle part meets the biggest difficulties (which means that the medium and small-sized enterprises are in a more difficult situation compared to the large and micro enterprises).

Fig. 2 Financing difficulty of SMEs and phenomenon of “SMEs Disappear” data (Source Zheng Ziqiang, General Thinking of the Development of the Small and Micro-finance System in China, Renmin University of China “Small and Microfinance Research Center” Work Report, 2014)

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图片内容: Phenomenon of “SMEs Disappears” “SMEs disappears” In regard to the enterprise quantity and structure, the number of SMEs in developing countries is much lower than that in developed countries. As to the role in economy, SMEs create approximately 65% jobs and 50% GDP in developed countries, compared to respectively about 30% and lower than 20% in developing countries. Factors contributing to the phenomenon of “SMES disappears” The high tax rate and strict supervision (represented by the licensing system) gives rise to the business environment of high cost, in which large enterprises and the self-employed micro economies outside the formal economic system can survive easily, but SMEs face great difficulties. Compared to micro enterprises, SMEs have low profit and low marginal capital return and can hardly bear high financial cost. The small-loan model (such as group loans) for micro enterprises and low-income groups do not apply to SMEs. Business interpretation of the phenomenon of “SMES disappears” The financing of SMEs in developing countries is faced with great challenges, and the business models shall be innovated. More attention shall be paid to small and micro enterprises instead of medium and small-sized enterprises In credit business, the middle part meets the biggest difficulties (which means that the medium and small-sized enterprises are in a more difficult situation compared to the large and micro enterprises).

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The Start of “3+1 Integrity Alliance”

According to the original plan, “3+1 Integrity Alliance” is an organization in between a financial institution and a SME. It is beneficial to the banks as members of the alliance help and guarantee each other, thus lowering the credit risk of banks; and it also benefits the borrowing enterprises by reducing the banks’ risks and increasing the availability of funds.

Banks and non-banking financial institutions

Information flow

Zhengzhou “3+1 Integrity Alliance”

Enterprise 1 Enterprise 2

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Capital demand

Capital supply

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Capital flow

Fig. 3 Relationship among the alliance, financial institutions and enterprises

As shown in Fig. 3, “3+1 Integrity Alliance” appears to be a jointguarantee organization. It requires members that apply for bank loans to submit 15% of the loan amount as the risk deposit, which is a common practice of loan guarantee organizations. Then, what is special about “3+1 Integrity Alliance”? Xing Yong explained that distinguished from common guarantee organizations, “3+1 Integrity Alliance” established a whole set of incentive and restraint mechanism, organized member enterprises to provide self-managed mutual help (including business management and risk management) and finally defused and transferred the credit risk. Therefore, the core of the “3+1 Integrity Alliance” model is to reduce or solve the default risk of SMEs by means of the community construction organized by members themselves and hence settles their financing difficulty. The system of “3+1 Integrity Alliance” has developed from Version 1.0 to 2.0. In Version 1.0, “3+1 Integrity Alliance” identifies, monitors and defuses risks before, during and after the loan for the banks, to reduce the banks’ losses to the minimum. The cooperative bank for the design and implementation of Version 1.0 is Zhengzhou Commercial Bank Jincheng Sub-branch. Jincheng Sub-branch has been engaged in SMEs credit business for many years and is fully aware of the financing difficulty of SMEs. When Xing Yong talked about his idea with Su, the

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President of the bank, who was clever and straightforward, Su immediately agreed to support the plan and cooperate with Xing Yong. As “3+1 Integrity Alliance” bore the default risk for Zhengzhou Commercial Bank, President Su promised to offer 3% of the profit to “3+1 Integrity Alliance” for the daily management of the Alliance. According to the plan agreed by both parties, Xing Yong found 100 SMEs who needed bank credit funds from the customer base he had accumulated during his work in the guarantee company and made them the members of “3+1 Integrity Alliance” for the pilot project cooperated with Zhengzhou Commercial Bank. “3+1 Integrity Alliance” is responsible for the whole process of loan, including the pre-loan review as well as loan and after-loan management. Pre-loan management. In this part, “3+1 Integrity Alliance” shall submit the list of enterprises to apply for loans (also the members of the Alliance) accepted by the Alliance preliminarily to the bank. Then the Alliance and the bank shall establish the joint loan-review group. The Alliance is responsible for preliminary approval, and the bank makes the final judgment. Loan management. Upon the approval, the bank shall release the loan, which ranges between RMB 1 million and 3 million. The enterprises that have acquired the loan shall deposit 15% of the loan amount in the special account of the bank as the risk deposit. In this way, the deposit pool is established as the funding source for collective guarantee. After-loan management. After-loan management completely depends on “3+1 Integrity Alliance”. The Alliance adopts the incentive and constraint mechanism independently designed by itself to promote the integrity and mutual help of members and stimulate them to monitor, restrict and help each other until the recovery of the loan. In case of any default, the risk deposits paid by the enterprises will cover the loss to assure the fund security of the bank. Apparently, after-loan management constitutes the core content and value of the Alliance. Meanwhile, the institutional design is regarded as the core of after-loan management. How to bring together the owners of SMEs from all parts of the country who are strangers to each other and have them help each other?

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Mechanism Design of “3+1 Integrity Alliance”

Xing Yong is a middle-aged man with a peaceful mind. However, when he sets up his mind to something, he will hold on to the last and firmly believe that he will succeed. As he said, “no problem is the biggest problem”, and “there are always more solutions than problems”. Despite his optimistic attitude, he had spent most of his energy on the mechanism design of “3+1 Integrity Alliance”. Every mechanism is backed by a certain culture, and the philosophy supporting “3+1 Integrity Alliance” is to “help others with the idea of goodness”. A senior financial expert commented that this idea seemed to be childish. He held that the “original sin hypothesis” of borrowers should be stressed in the credit sector. This hypothesis assumed that all borrowers might be the “bad person” who defaulted on a loan, and all kinds of measures should be adopted to reject any “bad person” and all means including mortgage should be used to cover the loss caused by the default of the “bad person”. By contrast, “3+1 Integrity Alliance” adheres to the “good person hypothesis” that assumes all borrowers are good people and attempts to restrain the default by upholding the good thoughts of “honesty and helping others”.

3.1

Home to Members

According to Xing Yong, “3+1 Integrity Alliance” is a home to members, and anyone joining “3+1 Integrity Alliance” is a family member. This concept is embodied in not only the ingenious design of tea, boxing, painting, calligraphy and catering in the courtyard, but, more importantly, the regular gatherings of members. In most parties, members simply gather together for dining, tea and an outing. These activities can relax these entrepreneurs physically and mentally and provide them a platform to share their work and life experience. The results of these activities are surprisingly good. Why? In the case interview, a young female entrepreneur coming from Shanxi and engaged in the trade in

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maternity, infant and children clothing in Zhengzhou, Tian Fengmei, told us the follows. The middle and small-sized business entrepreneurs are actually lonely. They are going straight ahead alone without knowing the risks ahead, and nobody can help them if they encounter setbacks. Even their families and followers know nothing about why they fail. It is no exaggeration that the business world is full of difficulties and traps. Despite their competitors, companions and followers, all risks and responsibilities are borne by themselves. After doing business for a long time, we will see that families cannot help us though they want to. What they only can do is to offer us three meals or find us a job. However, when running a business, we are considering not only our interests but also the future or our employees. Any difficulty arising in this course cannot be solved by our families. Hence we need another family made up by entrepreneurs. Xing Yong has brought us together to be a family. We go forward together hand in hand, and help those who fall behind. Despite our different goals, we can make progress together at present. This is great. We have found a family that can really help each other. This is true. Though members of the Alliance come from all over the country, they are getting closer and closer through the activities of the Alliance and feel being at home. Xing Yong and workers of the Alliance are the head of this family. By constructing the home to members, “3+1 Integrity Alliance” gathers together middle and small-sized business entrepreneurs who do not know each other and creates an association of certain loyalty. Entrepreneurs like this place as they can share their problems and be comforted by others. But this is only the first step. The goal of “3+1 Integrity Alliance” is to effectively control credit risks through community construction and solve the financing difficulty of SMEs.

3.2

Goodness · Integrity Fraternity

Goodness · Integrity Fraternity is a key department to achieve the goal of “3+1 Integrity Alliance”, which epitomizes the characteristic of the

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Alliance that it depends on member autonomy to achieve its goal. As agreed, once the member enterprises of the Alliance successfully acquired the loan, they would automatically become the members of the Goodness · Integrity Fraternity under “3+1 Integrity Alliance”.2 The high-quality enterprises elected by members will lead the internal selfmanagement of the Fraternity, to realize “self-help” and “self-correction”3 based on collective help, control and defuse risks and achieve common development.

3.2.1 Concept of Honesty and Trustworthiness of Goodness · Integrity Fraternity The Goodness · Integrity Fraternity embodies the main highlights of “3+1 Integrity Alliance”. First, it promotes and develops the culture of honesty and inspires the borrower to keep promise with the concept of honesty and trustworthiness. Second, it advocates mutual help, achieves the mutual restraint and mutual aid among members by means of the “home” culture and the incentive mechanism, and thus controls and defuses risks through common development. Promote and develop the culture of honesty. Xing Yong believes that despite the different educational levels and credit levels of small and medium-sized business entrepreneurs, they all have the seeds of “goodness” and honesty deep in their mind. If carefully cultivated, the entrepreneurs will be inspired to voluntarily observe the agreement and repay the principal and interest for the loan. Based on this belief, all members of “3+1 Integrity Alliance” will be required to sign the Treaty of “3+1 Integrity Alliance” to promise to be honest.

2 “3+1

Integrity Alliance” also includes the enterprises having not borrowed from the bank, who are the non-core members of the Alliance. 3 According to the explanation of “3+1 Integrity Alliance”, so-called self-help means that the enterprises, which suffer from business issues due to force majeure in daily management, will be helped by the Alliance when four conditions are met (the secured fixed asset is under the name of the Alliance; the relatives have signed the guarantee agreement; the enterprise undertakes that the Alliance interests prevail over external interests; and the priority will be given to the Alliance for selective repayments). Self-correction means that the Alliance adopts litigation and non-litigation means to collect and deal with the debts from high-risk customers.

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Advocate “helping people based on the idea of goodness”. Since only enterprises that apply for a loan can join the Fraternity, considering the joint guarantee for the loan, economic connections have been established among the Fraternity members. The joint guarantee system has tied up the interests of these enterprises, exerting a restriction effect through collective punishments. In this context, “3+1 Integrity Alliance” shares the same contract rules with other loan joint-guarantee organizations. However, the difference between the Alliance and other organizations lies in that the Alliance attempts to introduce more content, advocates “helping people based on the idea of goodness”, turns the passive collective punishments into active mutual help and inspires people to take the initiative to do good by helping themselves and others. Don’t underestimate these “soft” designs and adjustments. These finetuned designs impel the borrowing enterprises participating in joint guarantee to help others actively instead of being involved in collective punishment passively. In this case, completely different results may be seen, as they may take the initiative to relieve the debt difficulties of enterprises in financial distress and improve the recovery rate of non-performing loans. Rather than bearing the losses passively, these enterprises may do the follows: first, offer help to others actively to defuse the risks; second, monitor others initiatively and give early warnings; and third, participate in credit information collection actively and provide valid information of peers, to improve the accuracy of credit information. This borrowers’ fraternity organized based on the concept of goodness and integrity gives full play to the borrowing enterprises’ subjective initiative and achieve the enterprises’ self-discipline, self-improvement and common development on the basis of mutual help. Not only sharing risks like other simple credit joint-guarantee organizations do, it also monitors and defuses credit risks (see Table 1). All these sound effects are Table 1 Comparison of the risk control effects of joint-guarantee organizations actively helping others and those passively bearing guarantee liabilities Risk monitoring Risk sharing Risk resolution

Passive guarantee

Active help

No realization Passive realization No realization

Active realization Active + passive realization Active realization

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realized by the fraternity members helping weak enterprises. The Goodness · Integrity Fraternity that prioritizes helping others over its own gains and losses and prioritizes long-term interests over short-term interests will assure the more effective and stable development of all members of the Alliance. The idea of the Goodness · Integrity Fraternity deserves the compliment, but “fancy dream, but poor reality”. Will Fraternity members truly take the initiative to help others as inspired by the idea of goodness and integrity? The key problem is how to motivate Fraternity members to actively participate in doing goodness in the face of the damages resulting from operation failure. The mechanism design of the Fraternity assures the realization of the idea.

3.2.2 Incentive and Restraint Mechanism of Goodness · Integrity Fraternity The incentive and restraint mechanism of the Fraternity is mainly reflected by three institutional arrangements, “credit point management”, “credit power fund pool” and “deposit refund mechanism”. 1. Credit Point Management The basic principle of credit points is to reward anyone who repays principal and interest honestly and timely, actively participates in the activities of the Fraternity to help others, offers help to risk monitoring or actively cooperates with the bank in deposit settlement with extra points. The points will be accumulated since the enterprise has successfully acquired the loan. Each member has zero point at first, which will be added or subtracted later according to the rules on the points. The monthly average point increase of the members will be publicized and ranked on the website of the Alliance each quarter. See the Measures for Credit Point Management in Appendix 1. The ranks of credit points will be classified into four levels, respectively 3A, 2A, 1A and B from high to low, where 3A enterprises take up 10%, 2A occupying 20%, 1A accounting for 50% and B taking up 200%. The enterprises with higher credit points will enjoy the greater loan interest

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subsidy. 3A enterprises are given the interest subsidy, and their monthly loan interest rate will be lowered from the current bank interest rate of the loan to the base interest rate of the bank. For example, the monthly interest rate can be decreased to 0.404% by the end of 2016. 2. Credit Power Fund Pool The point system is established for appraisal and reward. The credit power fund is specially designed to reward who help others and keep promise, in order to gradually replace the risk deposit through selfdevelopment and improve the Alliance’s ability to undertake risks. The credit power fund pool consists of three parts, interest discount, relief fund and mutual-aid fund. The interest discount is the interest subsidy given to the members of high points, the amount of which is 1% of the administration cost collected by the Alliance from the members. The administration cost is about 2% to 5% of the loan. The interest discount points are divided into two kinds, “quarterly interest subsidy” and “balance interest subsidy”. As to the “quarterly interest subsidy”, members ranking top 10% in terms of the points will have their loan interest rate reduced to 0.404% per month (based on the current base interest rate of the bank), compared to 0.525% per month (based on the current base interest rate of the bank) for those ranking top 11% to 30%. The interest subsidy is paid to the members in a form of cash each quarter. As to the “balance interest subsidy”, the favorable balance of every credit year (if any) will be allocated to all loan members of the Alliance after the last-quarter appraisal (members of Level B, the lowest level of credit points, shall not enjoy this subsidy). The relief fund is provided to help the enterprises subject to any difficulty in short-term capital turnover in operation activities. The first batch of the relief fund, totaling RMB 500 thousand, was donated by Xing Yong, the person in charge of the Alliance, as the start-up fund. The subsequent funds come from the following sources: first, the donations based on a certain percentage of the interest rate made by the members who were helped when subject to any difficulty in short-term capital turnover in operation activities; and second, the donations based on a

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certain percentage of the interest rate made by the members who were helped when suffering from any difficulty in short-term capital turnover upon the loan renewal. See the donation rules in Table 2. The release of the relief fund must be reviewed by the branch presidents and the Deputy President of the Goodness · Integrity Fraternity, and be approved by the member management center of the Alliance. The amount shall be no more than RMB 500 thousand and the term shall be no longer than half a month. The relief fund not only helps the enterprises in financial difficulties, but also reflects how the helped pay back to the helpers. This incentive mechanism promotes and develops altruism by helping others, and meanwhile defuses the risks through the self-relief among members. Moreover, the helped are required to donate funds back in this mechanism, which, on one hand, solves the problem of fund source, and, on the other hand, embodies the wisdom of “all for one and one for all” and guarantees the positive circulation of the Fraternity, this self-organization of enterprise members. The mutual-aid fund is used to cover the temporary fund shortage when the enterprise applies for a new loan to repay the old loan. This fund is voluntarily provided by other members of the Fraternity (which is essentially mutual lending among members) and guaranteed by the Alliance. As review and approval is needed to apply for a new loan to repay the old, the borrower shall make a request to the Alliance through the branch president one month in advance. Then the Alliance will Table 2 Sources of the “Relief Fund” Sources of the “Relief Fund” (interest by day) Time

Within 15 days

15 days (including 15 days)–1 month 2%

Interest rate of 0.5% donation Sources of the “Relief Fund” (one-off donation) Time Within 7 days 7 days and above Interest rate of 1% 2% donation

1 month (including 1 month)–2 months 3%

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provide the guarantee and raise the demand, while other members will voluntarily provide the interest-bearing mutual-aid fund. The member having acquired the mutual-aid fund shall return the fund immediately upon the arrival of the new loan. To assure the security of the mutualaid fund and reward the enterprises of high credit points, this aid is only offered to members above level 1A. Comment: The credit power fund constitutes an important guarantee to implement the incentive and restraint mechanism. In view of the limited sources of funds, “3+1 Integrity Alliance” creates an operation mode that provides material reward (interest subsidy) to the honest as the incentive and meanwhile supplements the funds conversely. It positively inspires the Alliance members to be honest and help each other, while accumulates the credit power fund slowly but steadily. Moreover, under these circumstances, the spiritual civilization inspired by the power of credit is exerting immeasurable energy on the development of the business world. 3. Deposit Refund Mechanism According to the rules of the Alliance, the members ranking Top 5 in terms of credit points per quarter are qualified for deposit refund. To be specific, the qualified enterprises will be refunded with 1/12 of the deposit per month since the next quarter, with 1/4 of the deposit refunded every quarter. However, the quarterly evaluations are independent from each other, and the points will be cleared to zero by the end of each quarter. By refunding excellent enterprises, the Alliance prevents the honest enterprises from paying a bill for the dishonest enterprises and supporting the healthy growth of more excellent enterprises. Furthermore, this deposit refund mechanism also inspires member enterprises to discipline themselves to earn high points and tangible benefits. Rewards to the honest and punishments against the dishonest constitute the core of the incentive and restraint mechanism of the Goodness · Integrity Fraternity. In the four years since 2013, this incentive and restraint mechanism has been constantly improved and has guided the members to keep promise and help others.

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3.2.3 Organizational and Management Structure of Goodness · Integrity Fraternity 1. Version 1.0 of the Organizational Structure The organizational structure of Goodness · Integrity Fraternity has been exposed to several adjustments. In the initial design, the Fraternity has the development committee, credit and self-discipline committee and Sanyi (meaning 3+1) courtyard under it (see Fig. 4). The development committee is responsible for organizing and constructing the Fraternity. Members are classified into different levels, respectively the member, executive member, organizing committee member, branch president, deputy president and executive president Goodness · Integrity Fraternity

Integrity and Self-discipline Committee

Development Committee

Alliance Resource Department

Executive President

Review Committee

Sanyi Courtyard

Integrity Supervision Department

Deputy President

Branch President

Organizing Committee Member

Member

Fig. 4 Version 1.0 of the organization structure of Goodness · Integrity Fraternity

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from low to high. Executive members are the members who have already acquired the loan. The branches are set up for different industries. There are approximately 20 branches, and each has 10 members according to the standard. These branches adopt the one-manage-three system, where 1 branch president manages 3 organizing committee members and each organizing committee member manages 3 executive members. Therefore, the members of the Fraternity are regarded as the core members of the Alliance as well as the main targets of monitoring and incentives. The integrity and self-discipline committee mainly supervises the “self-discipline” of members and conducts risk assessment against the focused members and loan-renewal members of the Alliance on behalf of all members. The self-discipline committee is entitled to suggest whether the enterprise shall renew the loan or increase or decrease the loan amount. The self-discipline committee comprises 7 members, who are recommended by themselves or others and finally decided by the member management center of the Alliance. Sanyi courtyard is known as the “offline club” of the Alliance, located in a small courtyard that has the tearoom and coffee bar etc. where members communicate, exchange and keep up a friendship with each other.4 In this home to members, Sanyi courtyard provides a good venue for resource docking and business negotiation of member enterprises and hence benefits the businesses of enterprises. Moreover, “3+1 Integrity Alliance” also motivates members to keep promise and help each other by making excellent members the shareholders of Sanyi courtyard. Shareholders are allowed to enjoy the resources of Sanyi courtyard. As this two-committee and one-courtyard structure is too complex and shows role overlapping, Xing Yong adjusted the organizational and management structure of the Fraternity in the beginning of 2016 and released Version 2.0.

4 Sanyi courtyard was pulled down in 2016 and then moved to a house in a residential building. Though it loses the common touch, the style of Sanyi courtyard is consistent from beginning to end.

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2. Version 2.0 of the Organizational Structure In the new structure diagram, the two committees and one courtyard have been replaced by the “grid-based community management structure”. This new structure shows the characteristics of 3+1 everywhere. A community consisting of 31 Fraternity members is set up for each industry, and has 1 chief and 3 branches (each branch comprises 10 members); and a branch is made up by 1 president and 3 groups (each group has 3 members, including one group leader) (see Fig. 5). According to Xing Yong, this structure, though looking complicated, is a triangular structure of the greatest stability, and is named as the “onemanage-three member management system”. If the members of a certain industry are not enough to make up a community of 31, several small triangles can be temporarily removed. Apparently, the grid-based community management structure is more precise and compact compared to the previous two-committee onecourtyard structure. Under the “self-management” model of mutual help, these combinations of 3+1 will be superposed, to access to everyone in the structure.

President

Group leader

(a) Group of 3

(b) Branch of 10

(c) Community of 31

Fig. 5 One-manage-three member management structure

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3. Risk Control Though emphasizing the idea of integrity and mutual help, “3+1 Integrity Alliance” always adheres to the final goal of reducing the credit default risk of members. Therefore, how to achieve risk control depending on the self-organization of the community is a great challenge to the Alliance. According to Version 1.0 operation model of “3+1 Integrity Alliance”, risk control runs through the whole process, before, during and after the loan. The pre-loan risk control is mainly realized through the “3×3+1” loan process. To be specific, “3+1 Integrity Alliance” and Zhengzhou Commercial Bank have jointly developed a set of standards for loan review to examine the loan applications of enterprises, and the bank is responsible for the approval and release of loans. The so-called “3×3+1” means that the loan review consists of three 3-day stages, respectively for information collection and review, field investigation and decision making, and the last 1 day is for the release of the loan. According to the “3×3+1” loan process, it takes 10 days from the enterprise submitting the application to acquiring the loan. For the loan of RMB 1 million to 3 million, this time is neither too long nor too short. The institutional design of after-loan risk control mainly includes two points, risk assessment and risk disposal, which are exactly the special features of the risk control of “3+1 Integrity Alliance”. First, “3+1 Integrity Alliance” adopts the dual rating system that combines “credit rating” and “risk rating” for after-loan risk assessment. The credit rating is determined by whether the member actively participates in community activities, actively joins the relief activities of the Alliance, or repays principal and interest timely. Risk rating is similar to that generally performed by banks, which evaluates the business status, financial position and repayment ability of the enterprise. The information of risk assessment includes three parts, special financial information, non-financial information and visual monitoring information. The financial information is regularly reported by the enterprise

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to the member management center through the website of the Alliance and mobile app etc., which shall be supervised by the President and verified by the Alliance. Non-financial information comes from acquaintances of the Fraternity and other channels and is rated by the same-trade members of the Fraternity and external experts. In this way, the information collected is more targeted and reliable and the experts are more professional, significantly reducing information asymmetry and saving costs. The visual monitoring information is from the Alliance’s video monitoring systems mounted in the major office spaces of all Fraternity members. According to the specific rating standards, the members’ credit is classified into 3A, 2A, 1A and B from high to low, and the risk rating is graded into A, B, C and D. In this dual rating system, credit rating reflects the borrowing entrepreneurs’ moral character and repayment willingness, while risk rating shows the enterprises’ operation and financial risks as well as the associated repayment ability. Second, as to risk disposal, the dual rating results for each member will be weighted by the Alliance and then sorted by community credit (see Table 3). The enterprises with higher ratings will be rewarded and enjoy preferential policies including interest subsidy and deposit refund Table 3 Combination table of credit rating and risk rating No.

Credit rating + risk rating

1.

3A 10% 3A 10% 3A 10% 3A 10% 2A 20% 2A 20% 2A 20% 2A 20% 1A 50% 1A 50% 1A 50% 1A 50% B 20% B 20% B 20% B 20%

2.

3.

4.

+ + + + + + + + + + + + + + + +

A B B C A B B C A B B-/C D A B B-/C D

Data source “3+1 Integrity Alliance”

Means Interest subsidy To be reviewed No interest subsidy Additional means Interest subsidy To be reviewed No interest subsidy Additional means Normal Normal Additional means Additional means or withdrawal Normal Additional means Additional means Additional means or withdrawal

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etc. The 20% from the bottom will be considered as risky members and will be eliminated. However, risky members are not dismissed at once. 15% of members will withdraw steadily and 5% will be forced to quit. “Steady withdrawal” means that “3+1 Integrity Alliance” will help and support the enterprises in difficulties. If they can regain strength with the help, they will be listed as good enterprises again; and only the member enterprises who cannot get back on their feet with the help will be finally weeded out. As a highlight in risk disposal, “3+1 Integrity Alliance” always helps and aids the enterprises. Instead of adopting the simple, rude and cruel risk disposal means between the bank and SMEs (such as warning and accelerated maturity), the Alliance does its best to help and aid enterprises and never gives up easily. The key lies in how to help the members in difficulties. If the Alliance does it right, the enterprises will go through the crisis and be brought back to life; and if not, the moral crisis will arise, resulting in a bad effect by which enterprises are equally punished regardless of their performances. Therefore, “3+1 Integrity Alliance” gives priority to helping the honest enterprises that help others and are willing to make repayments. The enterprises that repudiate debts and have no willingness to repay shall be resolutely eliminated. The Alliance sets up two grades of rescue and help for enterprises in difficulties, namely help and correction, and provides corresponding rules. “Help group”: The help group consists of the enterprises of high credit points and repayment willingness that have repaid the interest regularly, but have suffered from a business performance decline over 30% and will improve its performance in the next 3 to 6 months, or the enterprises of high credit points that have a self-rescue program, are willing to accept the requirements of the Alliance (guarantor or collateral) and will pay off the Alliance’s borrowing before others. The Alliance will help the enterprises of the help group repay principal timely and release the risks smoothly. The measures of the help group include the follows: (1) credit enhancement, i.e. the increase of guarantors or collaterals etc.; (2) credit enhancement and reduction of the loan amount; (3) and help by Alliance members, which means that the members will repay the loan,

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and the helped will gradually repay the loan principal and interest to the helper with monthly operating income in the later period. “Correction group”: The enterprise with high credit points and repayment willingness that is subject to business stagnation and can hardly improve itself between 6 months and 1 year is classified into the “correction group”. By correcting the errors of the enterprise, the Alliance helps the enterprise repay the loan finally. Major measures of correction include: (1) reduction of the loan amount + credit enhancement; (2) payment collection, i.e. collecting loans in advance, recovering funds through litigation or claiming the payments paid on behalf of others; (3) and help, which hereby means pressing for money, borrowing from other channels and having Alliance members make repayments for the enterprise. As these enterprises are faced with more severe problems, the risk control of the Alliance will be stricter. The enterprises with low credit points, repayment ability but no repayment willingness (defaulting enterprises already in legal proceedings), or the enterprise that have the willingness to pay but cannot continue their businesses or offer mortgage assets and guarantors are included in the “bad group”. In this “bad group”, the enterprises’ loans are attributed to bad debts according to bank standards. Against the risks of these enterprises, the Alliance will try every means to recover the loans. The enterprises with low credit points that repudiate debts will be forced to quit the Alliance (eliminated), for which the credit power fund of the Alliance will be sued to repay the principal and interest owed by these enterprises to the bank. Meanwhile, the Alliance will do its best to help the enterprises that have high credit points and are striving to make repayments. In this case, the Alliance will help them in the three following ways: (1) performing internal merger and reorganization to bring these enterprises back to life; (2) protecting their assets to assure their further operation; (3) and arranging work for them to guarantee their survival. Apparently, the last measure is provided to the entrepreneurs who have gone broke and cannot survive. Even for these members, the Alliance will not give them up, but will find jobs for these “family members” in difficulties to support their lives until they stand up again.

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Now, we can see a very interesting phenomenon: Both “3+1 Integrity Alliance” serving SME owners and the Grameen Bank in Bangladesh borrowing group serving poor women are community organizations offering financial services, and both stress on the “family” culture and promote and develop the spirit of helping others, but, differently, the Alliance will weed out as high as 1/5 of the family members. Why is this? Xing Yong explained that this was for getting rid of the stale and taking in the fresh. Only with this system can the Alliance assure the favorable environment to support good enterprises, otherwise the Alliance would be dominated by bad enterprises. The loans provided by the Grameen Bank in Bangladesh to rural women are smaller than RMB 10,000, while the loans offered to the Alliance to SMEs range between RMB 1 million and 3 million. Therefore, the latter bears much higher risks than the former. Faced with the high risks, only the strict rule of elimination can bring about the long period of peace and stability of the community. Nevertheless, “3+1 Integrity Alliance” is much more affectionate than banks. In regard to risk disposal, to encourage the help among members, the Alliance also provides that the high-quality members that have actively participated in mutual help enjoy preferential treatment: (1) They will have their credit points increased, acquire the interest subsidy according to the Measures for Credit Power Fund Management and reduce the proportion of risk taking; (2) They have the priority to help; (3) No extra fee will be collected for the increase of the loan amount (see the explanations of fees later); (4) They will be awarded the title of “Excellent Member”, enjoy the different rate and the interest rate reward and decrease their financing cost. 4. Differentiated Loan Policy In order to stimulate Alliance members to keep promise and help each other, Xing Yong has repeatedly stressed on being clear about rewards and punishments. For this reason, the Alliance treats member enterprises in a different manner in terms of the loan amount and interest rate. As agreed, Zhengzhou Commercial Bank authorized the Alliance to increase the loan interest rate by 90% of the base interest rate of the

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bank loan based on the borrower’s condition and collect 4% from the customer as the administration cost.5 1% of the administration cost will be used to cover the cost of daily management of the Alliance, while the remaining 3% will be used as the fund for the implementation of the Alliance’s incentive and restraint mechanism. The Alliance will rank members based on dual ratings from high to low. Those with higher ratings enjoy a lower interest rate of borrowing, and the interest rate for those with lower ratings will be higher. Between 2014 and 2016, the minimum borrowing cost of the Alliance member was only 4.35%, while the maximum reached as high as about 13%. To realize differentiated management regarding the loan amount, the Alliance makes suggestions to the bank. It advises the bank provide the maximum-amount credit to high-quality members of high dual ratings and full-amount collaterals, and reduce the credit lines when the enterprises of lower ratings apply for or renew the loans.

4

Is “3+1 Integrity Alliance” Sustainable?

“3+1 Integrity Alliance” is a non-profit community organization under the Promotion Association for SMEs of the Department of Industry and Information Technology of Henan Province.6 Though being a nonprofit organization that provides financing services to SMEs, it still needs enough money for survival and demands financial sources for sustainable development. The financial source of “3+1 Integrity Alliance” is the administration cost collected from members. As agreed by “3+1 Integrity Alliance” and Zhengzhou Commercial Bank, “3+1 Integrity Alliance” can collect 4% of the loan amount from borrowing enterprises as the administration cost. 1% is applied to cover daily management cost, while the remaining 3% are used as the credit power fund to inspire members and gradually replace the deposit paid by members. 5The base interest rate of Zhengzhou Commercial Bank was 4.35% in 2016, and 8.265% can be acquired if increased by 90%. 6 “3+1 Integrity Alliance” is considering to change the name to “Sanyi Financial Service” in 2017, to highlight its nature as a financial service organization.

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By May 2017, the total loan balance of Alliance members amounted to RMB 270 million, giving rise to an income of administration cost of RMB 2.7 million. As a financial service agency, the daily expense of the Alliance is mainly the payroll. In addition to the administration expense, traffic cost, electricity and water fee and office rent etc., the average annual pay of the 26 employees of the Alliance is approximately RMB 80 thousand, which is at the lower-middle level in Zhengzhou financial service agencies. Then, how to attract and retain talents in the long run? It’s worth noting that the front-line workers who followed Xing Yong to start the business mostly share the same ideal. Inspired by Xing Yong, they gave up their stable jobs and devoted themselves into the undertakings of the Alliance. Some of them came from universities, some from financial institutions and some from other NGO organizations. Like other new start-ups, they seldom talked about the work benefits at the beginning but were committed to working with all their strength. However, along with the expansion of Alliance businesses and the increase of employees,7 it is of great significance to assure staff ’s stable and appropriate income (see Fig. 6). As a matter of fact, the Alliance can easily make quick money based on its operation model. For example, it can simply increase the number of member enterprises and loans, to raise the income from the administration cost; or lower the ratio of the administration cost used as the credit power fund without changing the total loan amount, and apply the majority of the administration cost collected to daily expenses and payroll of the Alliance. However, Xing Yong holds that the Alliance is a non-profit organization which will never be profit-oriented. Yet he knows that it is necessary to appropriately increase the operating income of the Alliance to render employees a life of “dignity” and support the sustainable development of the Alliance. But in the early stage, the most important task is to improve the institutional design and create a rational operation mode. The Alliance “shall never get lost 7To assure the favorable development of the member system, the Alliance allocates 4 workers to each community, one as the community leader, one for member management, one for risk control and one for business processing. These workers will guide, assist and serve the community to perform self-management, self-risk control and self-development.

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Educational structure postgraduate degree and returnee 4%

Below junior college 4%

Undergraduate degree 34%

Junior college 58%

Below junior college

Junior college

Undergraduate degree

postgraduate degree and returnee

Age structure Under the age of 30 19%

Above the age of 40 19%

30-40 62%

Under the age of 30

30-40

Fig. 6 Human Resources distribution of the alliance

Above the age of 40

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for the sake of money”. To this end, Xing Yong had refused several opportunities of rapid expansion knocking his door. He also denies the idea of reducing the management cost used as the credit power fund to increase employees’ income, since he believes that the management cost collected from members shall be applied to members to the maximum. The Alliance will not have the high-quality member enterprises pay the bill for low-quality enterprises, and will inspire member enterprises to actively participate in self-organization, self-management, self-risk control and self-development and achieve mutual help and mutual benefit, which is exactly the goal of “3+1 Integrity Alliance”. Hence the reduction of the ratio of the administration cost put into the credit fund pool will be postponed. After four years of operation, the Alliance is considering expanding its scale and cooperating with more banks. In light of its own advantages, the Alliance adjusts the operation mode by changing the whole-process credit risk control services including pre-loan, in-loan and after-loan services to only after-loan risk control services, thus to lower the risks. By doing this, the Alliance’s scope of work has been reduced. No longer participating in pre-loan review, the Alliance only serves current SME borrowers of the bank and provides after-loan risk control for the bank through community management. Apparently, the shortened operation chain is a good choice as it is conducive to resource concentration, cost reduction and quality improvement. This operation mode after adjustments is called Version 2.0 according to Xing Yong.

5

What Is the Effect of “3+1 Integrity Alliance”?

What’s unique about “3+1 Integrity Alliance” lies in that it brings together borrowing enterprises through community construction and manages credit risks by means of active help instead of passive joint guarantee. It aims to lower the bank’s credit risk without damaging everyone’s interests, solve the financing difficulties of SMEs and achieve mutual help and benefits. Then, how well does it work upon the joint efforts of Xing Yong and his colleagues? To further examine the effect of the

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Alliance operation, we break down this major question into four minor questions: whether SMEs are willing to become the members of “3+1 Integrity Alliance”; whether Alliance members (particularly members of Goodness · Integrity Fraternity) enthusiastically participate in activities to help others, and if yes, how they help others; whether the mechanism design of the Alliance can effectively lower the bank’s credit risk; and faced with the economic downturn since 2013, whether the Alliance dedicated to the credit of SMEs can bear the risks in this economic cycle. 1. Are SMEs Willing to Become Alliance Members? To understand the real opinions of SME entrepreneurs about the Alliance, the case study group interviewed three member enterprises on Feb. 4, 2015, discussing whether they agreed with the idea of the Alliance and the Fraternity and whether they supported the “mutual help and mutual discipline” or other activities of the Fraternity. See the interview summary in Appendix 2. Following common grounds can be summarized from the conversations with the interviewed entrepreneurs. First, they have a strong sense of identity with the Fraternity and feel at “home” in the Alliance. While they have to struggle hard outside, they can communicate with friends here and help each other, and this is what they greatly desire. Second, the Alliance is serving them truly and wholeheartedly, especially the interest subsidy and deposit refund given by the Alliance to the members of high points. For instance, a member of the first branch of the 2nd community8 is engaged in the production and installation of security technology products. It has stable businesses, but also many receivables. Therefore, it applied for a loan of RMB 2 million from the bank through the Alliance and meanwhile submitted a deposit of RMB 300 thousand. As it actively cooperated with the Alliance in cross-marketing with the bank and selforganization management and run businesses steadily, it enjoyed the Alliance’s preferential of interest subsidy and deposit refund for several 8A

community is also known as a “pool” in the Alliance.

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times. As of the end of 2014, the total interest subsidy and deposit refund given by the Alliance to the enterprise had amounted to RMB 64 thousand. Upon the maturity of its loan in March 2016, despite no extra fund demand, the enterprise chose to stay in the Alliance, participated in the self-organization management system and joined the Alliance activities actively in the WeChat Group of members. 2. Are Alliance Members Enthusiastic in “Helping Others”? How Is the Effect? The core of the self-organized management model of the Alliance is to help others. Can the incentive mechanism designed by Xing Yong truly motivate SME entrepreneurs from all over the country to care and help other members of the Alliance? According to the statistics provided by the Alliance, mutual help actions have taken place increasingly in the four years since the establishment of the Alliance. In the Alliance’s phrase, mutual help is embodied in the “self-management” and “self-risk control” during community construction. “Self-management” mutual help mainly assists the enterprise that is doing well in business but cannot borrow from the bank due to its short history, information asymmetry or other reasons. In this case, the member enterprises of the Alliance will fund it, and the helped will donate the “interest” to the credit power fund pool for collective incentives; or the member enterprises can help it with other resources including the source of goods, technology, distribution channel and consulting etc. Moreover, mutual help can also be realized through the business interaction among member enterprises, such as forming a supply chain of supply, production and distribution through the upstream and downstream cooperation. For example, Guo, a member of the third branch of the third community, runs a tire firm. He has been engaged in tire sales for seven years, having accumulated rich experience and done well. In January 2015, in order to expand sales scale and apply for a new agency for a tire manufacturer, he acquired a loan of RMB 1 million through the Alliance. Guo was not enthusiastic in Alliance activities in the beginning, as he thought that the Alliance was no different from other organizations, paid only lip

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service to so-called rescue and mutual help and would not help members if members meet real difficulties. Therefore, he psychologically rejected the daily member communication activities of the Alliance and seldom participated in these activities. In November 2015, Guo was in urgent need some raw materials for business. After raising funds all around, he still lacked RMB 200 thousand. Without any other means, he thought about the Alliance. He had a try and went to the President, but to his surprise, the Alliance had rapidly raised the funds through internal help by members on the same day and helped him out by offering a 15-day “rescue”. Guo repaid the borrowing after 15 days and donated RMB 2,000 of interest to the credit power fund pool. After the event, Guo became more active in Alliance activities and took the initiative to communicate with the Alliance and members. Sun, a member from the second branch of the third community, has run the business of food sales for more than ten years. However, he endured heavy losses of RMB 12 million when running a restaurant in 2011, severely affecting the funds for the principal business of food sales in 2013. He had applied for a loan to several banks but been rejected due to his poor cash flow. In 2014, the Alliance conducted an in-depth investigation on Sun’s company and carried out demonstration with members of the same trade. They all agreed that Sun had clear business ideas and good operating performance, so, after comprehensive assessment, they advised Zhengzhou Commercial Bank release a loan of RMB 1.5 million. Sun had lived up to everyone’s expectation and had a good business performance after borrowing. By the end of 2016, the second year of the loan, the capital turnover had returned to normal. Sun had also registered his own brand of entrusted processing. After two years of operation, the operating income of the enterprise had increased from over RMB 10 million before the loan in 2014 to over RMB 30 million. Wang from the third branch of the third community was running the business of food wholesale originally. He acquired a loan of RMB 2 million from the bank through the Alliance in November 2014. In 2015, his company started expansion and business transformation from sales to the combination of production and sales. The production line was officially put into operation in 2016. However, many new work procedures should be added to change original food sales to food production

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+ food sales, including the procurement of raw materials. In a community gathering, Wang mentioned that he was quite busy choosing the raw material suppliers recently. Unexpectedly, community members did him a big favor. Yang, another member of the Alliance, was running grain and oil wholesale in Wanbang Grain and Oil Market, and recommended the flour supplier of this market to Wang. Another member enterprise engaged in printing provided Wang with publicity print and recommended the packing supplier. In the gathering, many small entrepreneurs made suggestions for Wang’s cake production, including the brand name of the cake, employees’ wage system and so on. At present, Wang’s enterprise is working well. Meng’s company from the fourth branch of the second community is mainly engaged in the production and sales of polyurethane products and insulation construction products, which are mainly applied to cold storages, heating pipelines and insulating warehouses etc. The company acquired a loan of RMB 2 million through the Alliance in January 2014. In a self-organized management activity of the community known as “Approaching Sanyi”, the Alliance workers found out that the building materials company of Member A and the energy-saving materials company of Member B in the same branch with Meng were both engaged in the research, development, production and sales of the insulation materials for external walls and both needed polyurethane products. The three members were finally brought together by Alliance workers. After fully examining the business condition and product information of the enterprise, A and B both accepted the products and prices of Meng’s company. They considered that the transportation would be more convenient and the comprehensive cost would be lower as all their companies were in Zhengzhou. Finally, the three companies reached the cooperation intention. Meng would supply raw materials to A and B, and in case of too many orders of A, B would be entrusted for processing and production, which would promote the business development of B. Moreover, as assisted by Alliance managers, the three companies have paid and recovered funds timely and stably and have maintained a good business relationship until now.

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In addition to “self-management”, “self-risk control” mutual help is also provided, especially to the enterprises subject to repayment problems. This kind of help is more like rescue or “correction”. When some enterprises cannot make repayment timely due to their business issues, the Alliance President, branch president, self-discipline members will get involved, help these enterprises carry out merger and reorganization to get rid of difficulties and get a new life, protect their asset to assure their further operation, have all members step in to help and arrange work for them to guarantee their survival. As mentioned above, the enterprises ranking the bottom 20% in risk rating will be rescued when certain conditions are met. In the four years since the establishment of the Alliance, the economic downturn has produced a shock to SMEs, so several cases of rescue have emerged. For example, a member from the eighth branch of the second community runs a toy trading company mainly engaged in wholesales of children’s products and toys. He acquired a loan over RMB 1 million from the bank through the Alliance. When applying for the loan renewal in 2016, the Alliance found that the enterprise had a three-household joint-guaranteed loan in another bank. Due to the loan withdrawal by the other bank,9 the enterprise suffered from a shortage of funds. To make repayments, the entrepreneur frequently applied for private shortterm borrowings, but he still failed to fully repay the interest. Then he took the initiative to inform the Alliance of his difficulties. After deeply investigating the enterprise’s operation status, the community managers discovered that the enterprise was subject to excessive debts and receivables, high operating cost, negative-profit stores, void inventory (including 25% stocks of no value and 30% of low value), single sales channels and sales stagnation etc. The Alliance further checked the enterprise’s assets and found that the entrepreneur had three houses, including two fully-paid houses that had been mortgaged for loans and one house bought on mortgage for which RMB 1 million had been paid. His total inventory value was RMB 1 million. The Community held a meeting to discuss the solutions to these problems. All agreed that the enterprise 9 Loan

withdrawal means that the bank rejects to renew the loan upon the maturity and recovery of the loan. For most one-year loans to enterprises, Chinese banks will generally renew the loan if the enterprise has a good performance and needs the fund in the long run.

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had made some mistakes in the operation mode and management. Three correction suggestions were put forward. First, they suggested lower the debts first, which meant to sell the houses to reduce bank debts and the annual interest. Second, they suggested the enterprise reduce the operating cost, close the negative-profit stores, lower the personnel cost and transfer the ownership of the commercial house bought on mortgage to the Alliance to avoid the housing loan to lower the cost. Third, they suggested it adopt the O2O business model and recommended Youzan Mall for the expansion of sales. According to the comprehensive rescue proposal of the Alliance, the enterprise has closed the negative-profit stores and reduced staff members from 36 to 10, and is transferring the commercial house bought on mortgage to the Alliance and selling products in Youzan Mall. Based on the significant reduction of the cost, the asset exchange service offered by the Alliance (i.e. the transfer of the house ownership) and the change of the sales model, the overall business of the enterprise begins to recover. In this process, community managers have frequently communicated with the enterprise, provided consulting services anytime the enterprise needs and helps the enterprise correct the operation ideas. However, those enterprises that do not take advice or make adjustments will be eliminated by the Alliance. Xue from a branch of the third community ran an enterprise which was engaged in cosmetics sales and served as the Henan agent of a certain brand. In December 2014, he acquired a bank loan of RMB 1 million through the Alliance. In a community activity, the community manager saw that Xue had little communication with other members and looked anxious. So the manager asked about the operation status of Xue’s company, and Xue did not provide the details. The community manager sensed something wrong, and immediately reported to the Alliance and requested the risk control personnel to step in. After investigation and verification, the Alliance found that there was no cash flow in Xue’s company’s account, but Xue was expanding his business and intended to decorate the store of the “Chinese brand”. The primary business was shrinking and the store sales were declining due to the company’s blind expansion. The self-discipline member of the community immediately dissuaded him from expansion but Xue rejected the suggestions

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for several times. The review committee of the community discussed and decided to ask the member to prepay the loan and withdraw the Alliance steadily. The Alliance checked this enterprise afterwards and found that it became insolvent at the beginning of 2016. By summarizing the “self-management” and “self-risk control” cases above, it can be seen that the community management system of the Alliance has started playing its role as expected. The mutual help among member enterprises can improve their business performances, lower their operation risks and reduce the credit defaulting risks. Meanwhile, when an enterprise encounters any financial problem, the community will help it perform debt restructuring and offer the opportunity for the enterprise to correct its “business” mistakes. The community also firmly weeds out any enterprise that cannot be saved and leaves it alone. The final method seems to be cruel. However, from the perspective of economics, by eliminating the enterprises inappropriate for continued operation, it can facilitate the rational allocation of social resources and lower the social cost. Knowing how to give up properly is exactly the difference between the Alliance and the Grameen Bank in Bangladesh. 3. What Is the Value of “3+1 Integrity Alliance” for Banks Commercial banks are more cautious against the borrowing demands of the micro, small and medium-sized enterprises mainly for following reasons: information asymmetry, lack of collateral, higher operation risks of SMEs and their profits not enough to cover risks. If there are such organizations that can effectively solve the problems of information asymmetry and even guarantee for the banks and lower the operation risks of the enterprises, the worries of banks about the loans to SMEs can be dispelled. “3+1 Integrity Alliance” has mainly cooperated with Zhengzhou Commercial Bank Jincheng Sub-branch since its establishment. Then, how does President Su feel? In the summer of 2013, Professor Li interviewed President Su on her opinions about the Alliance. President Su said frankly that she considered the Alliance as a pilot project and wished “3+1 Integrity Alliance” great success. According to the agreement at that time, the Alliance would undertake the loan repayment, so the bank would be free from any

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trouble if the Alliance could work well. However, President Su was a little unconfident of the community management mode, as she doubted whether these entrepreneurs who were strangers to each other would really help each other. Therefore, she depended on the 15% deposit as the guarantee. When interviewing President Su again in the beginning of 2015, she was more confident evidently and believed that this might be a successful experiment. In the third interview with Su at the end of 2016, she nearly became the spokesman of “3+1 Integrity Alliance” model. She was familiar to the terms created by the Alliance and hoped that this model could be expanded to other branches of Zhengzhou Commercial Bank. President Su said: SMEs are the first to bear the brunt of the slowdown of economic growth in China and are exposed to increasing risks of profit decline. The banks dedicated to SME loans are suffering from an evident rise of the bad debt rate. However, the quality of the SME loans which are carried out based on our cooperation with “3+1 Integrity Alliance” have not been affected at all. “3+1 Integrity Alliance” has done many work for us, and now we have entrusted the after-loan management of all SMEs to the Alliance. My pressure has been significantly relieved since the loan quality can be assured. The model of “3+1 Integrity Alliance” is also favored by another bank dedicated to the loans to SMEs, Zhongyuan Bank Kaifeng Sub-branch. This bank and the Alliance are discussing cooperation. According to Xing Yong, as “3+1 Integrity Alliance” gradually transfers its focus to after-loan management (operation mode 2.0) and the further improvement of the Alliance’s community management system, the model of “3+1 Integrity Alliance” will be spread in a wider range and attract more banks for cooperation sooner or later. Xing Yong is proud of the success of the Alliance and is confident about the future. He will spare no effort to build “3+1 Integrity Alliance” into a professional service agency providing after-loan risk control services against SME loans for banks. 4. Can “3+1 Integrity Alliance” Bear the Economic Downturn? The economic downturn beginning in 2013 has produced an enormous impact on all enterprises, particularly SMEs. A large number of enterprises who struggled in the business world depending on their own

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strengths have been driven out of the market due to market shrinking. In some regions, the SME loan guarantee organizations went bankrupt and SMEs suffered from the debt crisis.10 The over a hundred member enterprises of “3+1 Integrity Alliance” have an average sales volume between RMB 30 million and 40 million, the business scopes of which cover more than 20 industries including clothes trade, beef and mutton sales, building materials and electronics etc. All these industries have been affected by economic downturn and industrial competition to varying degrees. Can “3+1 Integrity Alliance” that provides mutual help and guarantee services to SME financing endure the economic recession? This is the most severe test for the “3+1 Integrity Alliance” system in this period. Economic downturn continues in 2017. A total of 282 member enterprises have acquired loans through the Alliance, among which 76 enterprises have quitted or been eliminated by the Alliance for different reasons. The “problem members” excluding 12 enterprises quitting as they do not need loans any longer account for 23% of the total 282 enterprises. If calculated based on the accumulated loan amount, the bank loans acquired by the members through the Alliance’s guarantee system between 2014 and 2016 amounted to RMB 740 million, and the problem loans reached approximately RMB 96 million, taking up 12.97% of the total amount. Among the problem enterprises, 11 accepted rescue, 7 received the Alliance’s suggestion on mistake correction, 19 had poor performance, 18 repaid the loan and quitted the Alliance, and 9 escaped. If the loans of escaped companies were considered as the bad debts, these loans that the Alliance repaid with the deposit occupied about 1.8% of the accumulated loan amount (which can be understood as the bad debt rate), much lower than the deposit rate of 15%. Meanwhile, the credit power fund of the Alliance had offered interest subsidy of RMB 1.6342 million to 431 enterprises in total from the end of 2014 when it was established to the end of 2016. Apparently, the community risk control mechanism of “3+1 Integrity Alliance”

10 For

example, the debt crisis of Huishan Diary in Shandong in March 2017 greatly impacted the financing of SMEs in this region.

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Caixin China PMI (monthly)

GDP quarter-on-quarter growth (2011-2016)

(a) Caixin China Manufacturing Purchasing Managers’ Index (monthly)

(b) GDP quarter-on-quarter growth

Fig. 7 Economic downturn in China

showed great impact resistance in the face of the three-year economic recession (see Fig. 7). To fight against economic downturn, “3+1 Integrity Alliance” has made persistent efforts, worked steadily, improved the community construction system, done its best to help SMEs hold together and resist risks. Xing’s team believes that banks’ demands of intensive after-loan management for SMEs will increase with the economic downturn. What “3+1 Integrity Alliance” shall do is to improve its model to usher in the bright future. If winter comes, can spring be far behind?

Appendix 1: Measures for Credit Point Management of “3+1 Integrity Alliance” No.

Item

1.

Provide the bank statement before the 8th day of each month

Type of points Bonus points

Points and amount

Remarks

10~25 points

When providing the bank statement of the lending bank, 5 points are added if the amount is lower than RMB 500 thousand, 10 points added if more than RMB 500 thousand (incl.), 15 points added if more than RMB 1 million (incl.), 20 points added if more than RMB 1.5 million (incl.), and 25 points added if more than RMB 2 million (incl.)

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Item

Type of points

Points and amount

Minus points

5~10 points/time

Minus points

10 points/time

2.

Support the business of the lending bank

Bonus points

Above 10 points

3.

Repay principal and interest timely, no overdue repayment Recommend new members to “3+1 Integrity Alliance”

Minus points

–

Bonus points

20 points/person

Bonus points

5~20 points/time

Bonus and minus points

Cumulative points

4.

5.

6.

Provide other members’ risk information Actively cooperate with the Alliance’s activities

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Remarks When providing the bank statement of other banks, 1 point is added if smaller than RMB 1 million, 3 points added if more than RMB 1 million (incl.), and 5 points added if more than RMB 3 million (incl.) If failing to provide the statement before the 8th day every month, 5–10 points/time are subtracted If providing false statements as verified, 10 points/time are subtracted If POS machine is installed, 10 points are added (only for once). When depositing money in the cooperative bank, 5 points are added if the amount is smaller than RMB 500 thousand and 10 points added if more than RMB 500 thousand 5–10 points/time are subtracted for each time of overdue payment of monthly interest.

If the member recommends new customers to “3+1 Integrity Alliance”, 1 point/person is added; and if the recommended customer successfully acquires a loan, 19 points are added If providing other members’ risk information, 5–20 points/time are added upon verification 3 points are added for each activity the member attends

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Item

Type of points

Points and amount

7.

Assure normal monitoring

Minus points

5 points/time

8.

Inform the major changes of the enterprise

Bonus and minus points

5~10 points/time

9.

Support the credit power fund

Direct interest subsidy

RMB 100 thousand—subsidy of RMB 400 RMB 200 thousand—subsidy of RMB 800 RMB 300 thousand—subsidy of RMB 1,200 RMB 400 thousand—subsidy of RMB 1,600 RMB 500 thousand—subsidy of RMB 2,000 RMB 600 thousand—subsidy of RMB 2,400

Remarks If the member fails to participate the activity organized by the Alliance for two consecutive months during the loan period, 1 point is subtracted each time If any artificial closing, damage or disturbance of the Alliance’s monitoring, 5 points/time are subtracted If reforming the Alliance worker of any major change of the enterprise (such as decoration, address change, abnormal day-off, change of the primary business, initiation of the major project, change of the operation avenue and the change of equity, investment and debt etc.) within the specified period (at least 15 working days in advance), 10 points are added; and if not, 5 points are subtracted Grade 1: mutual-help amount RMB 500 thousand (incl.)—RMB 1 million, ranking top 3 from high to low; Grade 2: mutual-hep amt RMB 100 thousand (incl.)—500 thousand, ranking Top 2 from high to low; and if the member that enjoys interest subsidy provides mutual help, the member shall enjoy double interest subsidies according to the Measures for Credit Power Fund Management

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Item

Type of points

Points and amount

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Remarks

RMB 700 thousand—subsidy of RMB 2,800 RMB 800 thousand—subsidy of RMB 3,200 RMB 900 thousand—subsidy of RMB 3,600 RMB 1 million—subsidy of RMB 4,000

Data source “3+1 Integrity Alliance”, December 2015

Appendix 2: Summary of the Interview with Goodness · Integrity Fraternity Members of “3+1 Integrity Alliance” Time: Feburary 4, 2015 Place: Zhengzhou, Henan Province Interviewees: 1. Entrepreneur Ms. Tian Fengmei,11 the person in charge of Zhengzhou Tongyao Trading Co., Ltd. Branch President of Children’s Garments Industry of “3+1 Integrity Alliance” Goodness · Integrity Fraternity. Non-local. 2. Entrepreneur Mr. Yang Laibin, person in charge of Zhengzhou Yusheng Grain & Oil Business Department. Branch President of Grain & Oil Market of “3+1 Integrity Alliance”. Non-local. I. Interview with Ms. Tian 11 Ms.

Tian is a very typical example of “3+1 Integrity Alliance” members. Ms. Tian’s company was running well when she joined the Alliance and had actively participated in the Alliance’s activities and even functioned as the manager, having helped others a lot. However, Tian’s business failed in 2016, and “3+1 Integrity Alliance” assisted her in asset reorganization. With the collective help of the Alliance, Ms. Tian has started her second start-up. She has a deep understanding of the “mutual help” in “3+1 Integrity Alliance”.

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Q: What do you think of the loan joint-guarantee service of “3+1 Integrity Alliance”? A: “3+1 Integrity Alliance” is better than the simple joint-guarantee organizations. We are a small company with only more than 40 employees. We mainly sell children’s toys and sometimes provide the processing service. We have maintained frequent contacts with banks since out establishment until now. Banks ignored us at first and only started to accept us as a customer after we acquired fixed assets such as the house. However, the fixed asset-mortgaged loan was not enough at all. Joint-guarantee loans started to emerge since 2012, by which 3 to 5 households were tied together for the loans. I had participated in some five-household joint-guarantee loans, and even joined the market joint-guarantee loans, where 30 or 50 to hundreds of households went to the bank together to apply for a loan. We were managed by nobody from the start of the loan to the repayment, and all of us had to bear the responsibility together in case of any problem. I remembered clearly. I participated in a collective loan (joint-guarantee loan) organized by our market. An employee of a company joining this loan wanted to resign and asked to get the salary on that day (this company generally released salary on the 10th day of each month). The entrepreneur told him that he could rest assured and leave, as the accountant would transfer the salary to the employee’s card on the 10th day, but the employee refused and twice stabbed the entrepreneur. The entrepreneur died and his wife was not in Zhengzhou, so nobody repaid the loan. Our deposits were all withheld. There was no one care us or tell us what to do. We were only told that something happened to one enterprise and the deposits were withheld. I have seen many cases like this around. Moreover, if three friends apply for a loan based on joint guarantee, they all hope everyone is well. However, they have no idea that whether others have loans from other banks, whether this situation will produce risks against the joint-guarantee loans, and how big the risks are. Even friends cannot control such risks and do not know what to do with this case. “3+1 Integrity Alliance” has solved these problems for us. We finally have the feeling of being served in bank loans thanks to “3+1 Integrity Alliance”.

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We had maintained frequent contacts with banks depending on our own. However, we had no control and could only listen to others; we knew nothing about the details and were not sure of what was happening; and from borrowing to repayment, we were left alone without anybody to care, including the bank. Furthermore, if the bank identified any problem, its first response was to cancel the loan and ask us to repay the loan first. Then the bank would ignore us, as it only took care of its own interest. However, “3+1 Integrity Alliance” is not the case. President Xing told me that the Alliance bore a simple goal, that was to help micro, small and medium-sized enterprises. To be honest, I did not understand his meaning at the beginning, but what the Alliance has done for us makes me feel warm and moved. Q: What do you think about this “home” of “3+1 Integrity Alliance”? A: “3+1 Integrity Alliance” helps us find many family members even beyond our real family. We, entrepreneurs, are going straight ahead alone without knowing the risks ahead no matter how confident and successful we are. If we fall into traps or encounter setbacks, nobody can help us. Even our real families and followers know nothing about why we fail. After doing business for a long time, we will see that families cannot help us though they want to. What they only can do is to offer us three meals or find us a job. However, when running a business, we are considering not only our interests but also the future or our employees. Any difficulty arising in this course cannot be solved by our families. Hence we need another family made up by entrepreneurs. Xing brought us together as family members. We are going on ahead hand in hand, and help whoever drop out. Despite our different goals, we can make progress together at present. This is great. It is no exaggeration that the business world is full of difficulties and traps. Though we have our competitors, companions and followers, all risks and responsibilities are borne by ourselves. We have found a family that can really help each other in the Alliance.

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Q: Would you like to do things for “3+1 Integrity Alliance” though no direct benefit in return? A: Yes, I do. I became the Fraternity branch president as recommended by others and later served as a member of the self-discipline committee. I am very willing to spend extra time on doing things for the Alliance. In my opinion, the core of “3+1 Integrity Alliance” is to reduce everyone’s risks to the minimum. We will pull together and assist every enterprise of the Alliance. I think this is the core, though I may not have a deep understanding of many technical details (i.e. the rules of credit points). I agree and I am willing to make my own contribution to support the further development of our Alliance. Frankly, my mission is to truly understand enterprises and help them when they are in difficulties. Whenever they encounter any problem, we will support them within our capabilities. However, when any enterprise is subject to a very big issue, we cannot help and we will not follow along, but at least, we have tried our best and have a clear conscience. This is what we selfdiscipline committee does. In this group of people who are going on ahead together, some will blow a whistle, some will conduct investigation, and some are responsible for rescue. This is my understanding, so I am willing to make my contribution to this organization. II. Interview with Mr. Yang Q: You have been in this industry for years, haven’t you? When did you join “3+1 Integrity Alliance”? A: I have been engaged in edible oil business since 2000. I was one of the earliest enterprises that joined the Alliance. Q: You must be very active in Alliance, or you would not become the branch president. Can you talk about your feelings and opinions? A: At least I recognize the model of this Alliance a lot. The Alliance is more human than the so-called guarantee companies. We can kindly communicate with Alliance workers without any obstacle. This is great. This model also solves the borrowing problem of nonlocals including me as we have nothing to mortgage.

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In general, the grain and oil industry demands capital as much as millions of RMB. This industry has a low profit margin and depends on the scale. It is difficult to expand the scale merely relying on the primitive accumulation, so borrowing is needed. I had dealt with banks before, and the communication with them was difficult. Some banks even asked for kickbacks, but they felt dissatisfied for a small account and might have no courage to ask for a large amount. Therefore, it often took a very long time, even over a month, to apply for a loan. With the Alliance, the borrowing becomes much easier, the risks are lower, and the communications with banks are smoother. We worried about the cycle for loan renewal before, but it actually does not take long. The customers selected by the Alliance are generally of high quality, leaving us relieved. Q: What are the risks in this industry? As a branch president and an industry expert, how will you help the Alliance with risk control? A: The risk mainly resists in the oil and grain price fluctuation. When the market is in good conditions, I will store more goods; and when the market is gloomy, I will sell more goods and reduce my storage. Through years of operation, I can better grasp the law of the industry. Our branch has twelve or thirteen members, who all run business in the same market for more than ten years and are very familiar with each other. Some members are even relatives or fellow-villagers. Since we are in a small circle, we can simply find out the practical conditions of others by asking another person. It is very easy for us to know the business conditions of others.

Case Comments The financing difficulty of SMEs is a widespread problem. SMEs are exposed to higher risks than micro enterprises and large enterprises. In view of SMEs’ profit even lower than that of micro enterprises and insufficient collaterals, SMEs face greater difficulties in financing compared

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to large and micro enterprises, resulting in the phenomenon of “SMEs disappear”. Nevertheless, SMEs always play an important role in creating jobs, increasing taxes and supporting the growth of national economy, in either developed or developing countries, including China. While the large enterprises mainly based on state-owned economy seize two thirds of credit resources, SMEs create over 80% jobs and contribute over 60% of the GDP. The financial service to SMEs not only constitutes an important part of financial equality, but also effectively promotes economic development. However, how to solve the financing difficulties of SMEs? Many practical explorations have been conducted in this respect by now. Most practices in China center on three relevant subjects, the government, financial institution and SME, and are mainly performed through guarantee, interest subsidy and credit system innovation etc. In this process, the unique credit risk control model of commercial banks against SME loans as well as various guarantee models including commercial guarantee, government guarantee and SME mutual guarantee have been formed. In this case, the author summarized a new SME loan management model, which helps SMEs build a community through a nonprofit non-governmental organization and achieves the self-organization, self-management, self-risk control and self-development of the SME borrowers by means of community construction. This kind of community organizations that center on mutual help and cooperation are more seen in rural areas. Adhering to mutual help in production, fund and sales, they solve the obstacles in individual economic behaviors through collective mutual-help. The five-people group model of the Grameen Bank in Bangladesh is a typical application of community construction in microfinance. One factor contributing to the greater popularity of community organizations of mutual help and cooperation in rural areas lies in the close blood ties, kinship and distance of community members. Their close relationships make this model more likely successfully in rural areas. However, for the entrepreneurs running small or medium-sized enterprises coming from all over the country, it is indeed a great challenge to achieve credit risk control over community members through community construction.

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With the honesty culture as the key link and the exquisite incentive and restraint mechanism and the organizational management structure as the tool, “3+1 Integrity Alliance” has effectively realized the community construction and management of SMEs. By the mutual help and self-supervision of Alliance members, it promotes common development, reduces the operating risk, enables the members go through difficulties through mutual help, and realizes the internal, active and preventive credit risk control in contrast to the external, passive and delayed risk control of traditional banks. In the whole process, the rational incentive and restraint mechanism including even the elimination mechanism is the most important, and only with this mechanism can the enthusiasm of Alliance members be effectively mobilized.The institutional design of “3+1 Integrity Alliance” fully embodies the great wisdom of Chinese traditional culture. Commentator: Chen Yan12

References Meghana A, Asli D, Thorsten B, Small and Medium Enterprises Across the Globe: A New Database (August 2003). World Bank Policy Research Working Paper No. 3127. Available at SSRN: http://ssrn.com/abstract=636547. Thorsten B, Asli D, Small and Medium-size Enterprises: Access to Finance as a Growth Constraint, “Journal of Banking & Finance”, 2006, 30:2931–2943. Muhammad Yunus, Banker to the Poor, Beijing: SDX Joint Publishing Company, 2015.

12 Associate

Professor of Finance, Beijing Union University. E-mail: [email protected].

4 Big Bank, Small Finance: The Practice of China Minsheng Bank Jiahong Shi and Lin Zhou

1

Is It Feasible for Big Banks to Run Small Finance?

The essence of the financial activity is to manage, control and even operate risks. When conducting business, a financial institution is mainly faced with the credit risk (default risk), interest rate risk, market risk, operation risk, technical risk, policy risk and some other risks. The Members of this case study group include Jiahong Shi, Lin Zhou and Luo Shen. This report is prepared based on the data collected on site and interview record and reviewed by China Minsheng Bank. We would like to express our gratitude to the small and microfinancial business department of China Minsheng Bank for its vigorous support to our case study and report writing.

J. Shi (B) Renmin University of China, Beijing, China e-mail: [email protected] L. Zhou Fudan University, Shanghai, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_4

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biggest risk for financial institutions dedicated to credit business rests is the credit risk, including those engaged in small and micro finance. Since credit business is the main part of small and micro finance, the credit risk is naturally the major business risk of the institutions engaged in small and micro finance. The core of credit risk management and control is to measure and dispose of the risk. Measuring a risk requires the evaluation of the size of the credit and the ability to collect and analyze a large amount of information that reflects the customer’s repayment ability. To dispose of a risk, the financial institution shall defuse, hedge and insure the risks it bears and protect itself from any loss incurred by risk taking. To achieve both goals, the rational pricing, rational investment structure and proper management in the investment period (during and after the loan) are required. In the banks’ traditional loan business, the measurement of the credit risk of large and medium-sized enterprises mainly depends on the borrower’s relatively standard financial statements, previous credit records and traceable warehouse and shipping documents as well as the field investigation on the companies. It is easier for banks to acquire the data of large companies since they are highly transparent, normative and verifiable. Considering the small number of large enterprises applying for loans, field investigation produces less workload. However, the traditional measuring methods of the credit risk are no longer applicable when micro and small enterprises and self-employed individuals become the customers of financial services instead of large enterprises. The large number of small and micro enterprises are widely distributed, and they feature a short operation period, an insufficient credit record and non-standard financial information or even no financial information. Most of the information that reflects the borrower’s possibility of repayment is non-financial information or soft information. If the commercial banks acquire the information and measure the risk of small and micro enterprises according to traditional standards, the reliability of the information will decrease, and the cost will sharply rise. In particular, the cost of dishonesty of small and micro enterprises is not high considering the backward construction of the social credit system,

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which further increases the moral risk encountered by financial institutions. See the comparison of the credit risk features between large and medium enterprises and small and micro enterprises in Table 1. In regard to pre-loan review of small and micro loans, small and micro finance practitioners at home and abroad have summarized a series of valuable credit risk evaluation methods. For example, Zhejiang Tailong Commercial Bank examines three forms (water charge form, electric charge form and tax form) as well as the moral quality, products and collaterals; International Project Consult GmbH has proposed a set of IPC technologies that comprehensively evaluate credit risks based on the financial and non-financial information of small and micro enterprises; and Wells Fargo and Hibernia Corporation have developed their small and micro credit rating models etc. These methods mainly pick up information through close communication and the connections of neighbors, relatives and families and requires a great deal of manpower. Therefore, financial institutions must be “local” enough to acquire sufficient soft information and fragmented information. Apparently, regional financial institutions have an advantage in acquiring such kind of information. An effective method for risk control after the release of small and micro loans is to establish the joint guarantee relations among borrowers, have borrowers in the organization restrain each other and thus perform Table 1 Comparison of the credit risk features between large and medium enterprises and small and micro enterprises Credit risk features

Large and medium enterprises

Small and micro enterprises

Information features

Little soft information

Many soft information Heterogenous and noncomparable information Incomplete and fragmented information No normative financial information Lower Higher

Homogeneous and comparable information Complete information

Normative financial information Default risk Moral risk

Higher Lower

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after-loan risk monitoring and risk control. By adopting this method, it can restrain the borrower’s behaviors and lower the default risk, and meanwhile measure the risks. The group lending model of the Grameen Bank in Bangladesh founded by Professor Yunus is a typical representative of such method. Another method is to achieve after-loan risk control by assigning loan officers to regularly pay a return visit to customers and conduct frequent field monitoring. These two methods also depend on local connections. Regional financial institutions can easily organize a borrowers’ network and directly monitor and visit customers, thus featuring advantages in after-loan risk management. Along with the increasingly fierce competition in traditional finance, more banks intend to explore new businesses, and small and micro finance has become a blue ocean that attracts the attention of all kinds of banks. However, considering the credit risk features of small and micro enterprises described above, the commercial banks expert in traditional company credit business, particularly the national commercial banks, will inevitably face the following challenges: First, the risk control methods vary between the loans for small and micro enterprises and for traditional company loans. As mentioned above, for large and medium enterprises, banks can get useful information from their financial statements and previous credit evaluations and carry out after-loan management through one-to-one investigation and monitoring. However, as to the small and micro enterprises of complex and changeable conditions, banks can hardly acquire useful information records or accurately identify their financial information on assets and income etc. If continuing to adopt the traditional customer credit review and after-loan management method to manage the credit risk, the commercial banks will pay more but get less. Second, the labor cost of the big commercial bank is very high, so block credit trades are appropriate for big banks. When the amount of a single lower is reduced to a small amount like RMB 1 million, only the total amount of 100 loans can be compared to the amount of a single “traditional corporate banking service” (the loan for big companies), but the cost of a single small loan is not simply 1/100 of the traditional

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business. Therefore, the big banks’ credit risk control method applied to traditional businesses make no sense for small loans due to the rise of the cost. Regional financial institutions manage credit risks depending on labor-intensive work, which is apparently unacceptable to big banks of high labor cost. According to 2014 data from National Bureau of Statistics, the annual per-capita wage of all sectors in China was RMB 49,900, compared to RMB 108,200 for the financial industry (banks and non-banking financial institutions), RMB 240,100 for the four major state-owned commercial banks and RMB 372,400 for 8 national listed joint-stock listed banks1 (see Fig. 1). It can be seen that the labor cost of commercial banks is much higher than that of non-banking financial institutions; the labor cost of national big commercial banks is higher than that of regional medium and small-sized commercial banks; and the labor cost of joint-stock commercial banks is higher than that of the four major state-owned commercial banks. Hence big banks cannot directly adopt the labor-intensive strategy to run small and micro finance. RMB 10,000

All industries in China

Financial industry

Four major state-owned banks

National joint-stock banks

CMBC

Fig. 1 Per-capita labor cost of Chinese Commercial Banks in 2014 (Data source National Bureau of Statistics and annual reports of listed companies)

1 According

to the annual reports of listed companies, the 8 national listed joint-stock banks include China Merchants Bank, SPD Bank, China CITIC Bank, Huaxia Bank, China Everbright Bank, China Minsheng Bank, Industrial Bank and China Guangfa Bank.

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Third, big commercial banks show no regional advantages and cannot establish an effective interaction and symbiotic relationship. An important feature of small and micro finance lies in that the bank establishes close relations with customers, closely supervises the changes of customers’ business conditions and repayment abilities, conducts realtime risk monitoring and offers necessary help to customers to achieve common development. However, big banks that are far away from the grassroots find it very difficult to establish such relationships. First, they have no enough outlets at the grassroots or the “local forces”; and second, it is impractical for big banks of high per-capita cost to increase outlets and develop local forces. Therefore, big banks engaged in small and micro finance meet more difficulties in risk control in view of their competitive disadvantages in no locality and no interaction and symbiotic relationship with enterprises. It seems big commercial banks are naturally inferior to other small and micro financial institutions in terms of the micro and small loan business due to their insufficient experience, high labor cost and difficulty to interact with small and micro customers. Then, how can big banks exploit their own advantages, deal with the challenges brought by small-amount loans and effectively manage the credit risk of small and micro finance? Ranking the 13th among Chinese national commercial banks in terms of the asset size, CMBC has entered the field of small and micro finance and regarded it as the bank’s major development strategy since 2009. Before that, CMBC showed no difference with other national commercial banks, but the competitive pressure pushed it to step in this blue ocean. How does CMBC control the credit risk of small and micro finance? Has CMBC found the effective way for a big bank to run small and micro finance? We hope the answers to these questions can be found by analyzing the cases of CMBC credit risk control of small and micro finance.

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2

Construct the Small and Micro Finance Risk Control System

2.1

History

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Founded in 1996, China Minsheng Bank was the first national jointstock commercial bank in China. By the end of 2014, the total assets of the bank exceeded RMB 4,000 billion, ranking the 13th in China. It is a national large commercial bank in name and in fact. In 2007, in order to avoid homogeneous competitive pressure and adapt to the upcoming market-oriented reform of interest rate, CMBC, based on its own strategic thinking of China’s economic transformation and rise of private economy, made an important strategic adjustment: adhering to the development plan for the next five years of “being a bank for private enterprises, for small and micro enterprises and for highend customers”. Since 2009, CMBC has further established small and micro finance as its major development strategy and released a series of financial products including Shangmaotong (literally means trade helper) targeting small and micro enterprises.2 Since then, CMBC has officially entered the field of small and micro finance. CMBC was previously dedicated to serving the large enterprise customers and high-end customers of top class, but now it puts part of its strengths to the strange field of small and micro finance. Then, how has it dealt with the credit risk control for small and micro finance? Looking back into CMBC’s history, we can see that its risk management concept has undergone dramatic changes throughout the constant “trial-and-error” practices. 2 Shangmaotong

was the earliest small and micro-finance product launched by CMBC. It provides a package of financial services for individual businesses and small and micro enterprise owners including the RMB credit for production, investment or other operating activities, deposit and withdrawal service and consumption credit etc. This product offers loans lower than RMB 5 million to business operators such as small and medium-sized enterprise owners and individual businesses. Shangmaotong is not implemented through the loan card of the company. The borrowing is provided based on the personal credit record as the personal business loan, which can only be applied to the business scope specified in the company’s business license. Shangmaotong accepts multiple guarantee methods including house mortgage, joint guarantee and credit guarantee etc. and can be repaid at any time if within a certain amount and period, which will be calculated based on the actual amount and days of loan use.

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At first, there were few competitors in this blue ocean. CMBC completely copied the traditional loan risk management method of Chinese banks, i.e. the mortgage loan, but it soon discovered that many small and micro enterprises barely had any asset to mortgage. Then, CMBC turned to guarantee companies for credit enhancement, but found out that quite a few guarantee companies could not bear the risks of small and micro enterprises at all after practice. Since then, CMBC has started considering the necessity of “mortgage loan” for small and micro loans, adjusted the basic guidelines for risk control of small and micro enterprise loans, gradually given up mortgage loans, developed from “collateral worship”, to “weak guarantee” and finally to complete abandonment of collateral worship, and gradually released various credit products. CMBC has gradually understood and adapted to small and micro finance and transformed its concepts, systems and methods correspondingly. It has finally started its journey in this blue ocean of small and micro finance after painstaking reforms. A new challenge for CMBC is how to manage risks after cancelling mortgage guarantee. It launched the joint-guarantee model and soon discovered the “risk contagion” issue of this model. Once a problem arises, the whole group will fall into debt predicament, making the issue more difficult and costly to solve. Therefore, CMBC further released the promotion association in a form of cooperative, and the core of which is the cooperative mutual fund under the association. The mutual fund with no restriction on customer type and quantity is set up under a branch. Customers joining this fund shall pay the risk deposit (10% of the credit line) and risk provision (1% of the credit line). The former will be fully returned upon the full repayment of the loan, and the latter will be used by the bank for risk provision.3 Joint guarantee, cooperative mutual fund and risk compensation fund all depend on the collective strengths of the borrowers to carry out afterloan risks and achieve “risk self-compensation”. In the whole process of credit risk management, the after-loan risk control measures are just a 3 In

2014, CMBC Beijing Branch further reformed the cooperative mutual fund model and replaced it with the risk compensation fund. The fund members shall pay 2% of the credit line as risk provision and no longer pay 10% deposit. By doing this, it reduces the borrower’s capital cost and expands the bank’s market share.

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part of credit risk management and does not involve the pre-loan risk evaluation, risk pricing and portfolio management, which are essential to the credit risk control of a big commercial bank. We find that CMBC has adopted four distinctive “tactics” for credit risk control after entering the small and micro finance sector. First, measure the risks of certain industries and choose the industries for running business; second, adopt the “law of large numbers” for portfolio investment; third, confirm the price to cover the risk; and fourth, apply mass marketing to active customers. Among the four tactics, the first evidently shows the style of a big bank, as CMBC can give full play to its advantages in research and analysis, determine the types of customers through industrial positioning based on macro and industrial analysis and thus reduce the loan defaults incurred by industrial risks. The second and the third tactic realize risk hedging of small and micro loan portfolio and help determine the rational loan interest rate. The fourth tactic mainly reflects CMBC’s consideration of labor cost, i.e. to target the customer base through mass marketing. Numerous small and micro enterprises are included in each group of customers reached by the marketing, and the workload of oneon-one visits and the labor cost are significantly reduced as a result of the “wholesale trading”. These four tactics all suggest how a big bank makes best use of the advantages and bypasses the disadvantages when conducting small and micro loans. However, a problem behind is that whether the wholesale trading has certain “systematic risks”, and whether the severe information asymmetry of small and micro enterprises impede reasonable pricing. In 2011, in view of the increasingly fierce horizontal competition and the complexity of small and micro finance, in order to better realize the sustainable profits and risk control of Shangmaotong, CMBC officially released the Version 2.0 small and micro finance, striving to upgrade small and micro finance business in terms of products, services, social responsibilities and risk control etc. In Version 2.0, the traditional credit risk control mainly depending on experience control and artificial monitoring is replaced by the data analysis model-based risk control, which reviews and monitors small and micro loans online by means of the “credit factory”. The risk control concept of small and micro finance

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Version 2.0 reflects the application of big data in risk monitoring and CMBC’s determination to further lower labor cost. Whether the labor cost can be reduced may be the key to the effectiveness of risk control of Version 2.0. In 2013, CMBC launched the product of “Xiaoweibao”, which realizes the multi-channel business acceptance and online counter services through mobile operation and mobile sales. Therefore, it can not only shorten the time to transact businesses but also acquire data information useful for risk management. Moreover, CMBC facilitated the construction of the decision engine of “credit factory”, further improved the credit risk rating model, and advanced the information technologyoriented development of small and micro finance business. In 2014, the risk control of CMBC’s small and micro finance business continued the digital and information-oriented development. To acquire more data information on risk control, CMBC established the small and micro product lines for payment settlement, financing, wealth management and Internet finance, constructed seven special risk control models, developed the new risk information management tool of vertical search engine and achieved the whole-process risk control from pre-loan review, loan approval and after-loan monitoring by means of data analysis. By reviewing the development course of CMBC small and micro finance credit risk control, we can clearly see that CMBC, since it stepped in the field of small and micro finance, has put great efforts to avoid the big bank’s disadvantages in small loan, give full play to the advantages of the big bank in outstanding research capacity and great ability of data collection and analysis, and advance the information technology-oriented development of risk control. In this way, the risk control method will be inevitably transformed from experience control and artificial monitoring to digital analysis and automatic monitoring, while the focus of the risk control process will be transferred from the front management to middle and backstage control.

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Basic Principles for Risk Control

Dong Wenbiao, the former Chairman of CMBC, put forward the basic principles for risk control when small and micro finance was made the important development strategy. The principles include: determining the hedging risk based on the “law of large numbers”, covering the risk with reasonable pricing and reducing risks and controlling the cost through batch transactions. CMBC believes that these basic principles have innovated the risk control of small and micro finance and discarded traditional loans’ two-dimensional pricing method (“customer rating” and “debt rating”). These basic principles are fully embodied in CMBC’s important product, Shangmaotong.

2.2.1 Investment Principle of the “Law of Large Numbers” As explained by CMBC, the “law of large numbers” means that the actual loan loss will be closer to the expected loan loss if the sample size is large enough in the loan asset pool and the single asset size is small. When observing this principle during investments, CMBC can control the loan loss rate below the expected value and thus determine a more reasonable interest rate price to cover risks. Three conditions shall be satisfied to assure the tenability of the “law of large numbers”. First, the sample size in the asset pool is large enough; second, the single loan amount is so small that the risk incurred by a single loan exerts no significant influence on the average loan risk on the whole; and third, the position risks of the asset portfolio show weak correlation and can hedge the non-systematic risks. If the merchants show strong correlation in loan defaults, 1,000 small-amount loans have no difference with a large-amount loan, which dissatisfies the condition of the “law of large numbers”. Shangmaotong follows this principle in investments: The business department chooses the regional industries and business district industrial chains for investments based on the investment strategy of the headquarters and small and micro finance business department, makes multiple small-amount investments and attempts to control the loan default rate within the expected range.

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2.2.2 Pricing Principle of “Price Covering Risks” The principle of price covering risks helped the bank survive and develop, but has been neglected under the interest rate regulation of China. CMBC can make an easy profit by running traditional company businesses and do not need to think much about pricing; but for small and micro finance of high risks, CMBC must turn its mind back to pricing. Therefore, the pricing principle of price covering risks means that the interest rate price of small and micro loans shall cover not only the capital cost and operation cost but also the expected risk loss as explained by the “law of large numbers”. Based on the pricing strategy of “profit covering risks”, the pricing model of Shangmaotong business is: P = R + CC + OC + RC where P suggests the interest rate of Shangmaotong loans; R refers to the expected target profit rate; CC means the capital cost rate of the bank; OC indicates the operation cost rate of the bank; and RC is the risk cost which can be further decomposed into the follows: RC of Shangmaotong = asset portfolio expected loss rate GEL + risk premium of the business cycle MR + district risk premium DR + industrial risk premium IR + pricing adjustment based on loan design TP The asset portfolio expected loss rate is the average loan loss of Shangmaotong making large-scale portfolio investments based on the “law of large numbers”. Where the conditions for the “law of large numbers” are satisfied, the special risks of loans can be effectively hedged, and the average loan loss of the asset portfolio will be closer to the expected loan loss. Therefore, the overall expected loss rate of Shangmaotong asset portfolio can be used as a substitute for the expected loss rate of a single loan to reduce the difficulty of pricing model development. The risk premium cost above is calculated based on a large amount of raw data.

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The pricing based on price covering risks reflects the principle of profit and risk matching. Under this pricing strategy, as long as the nonperforming loan ratio falls into the range estimated with the “law of large numbers”, CMBC will assure that the price can cover risks. CMBC is tolerant to risky loans with flexible price changes, which may be a factor contributing to the rise of CMBC’s non-performing rate of small and micro loans in recent years. However, this pricing strategy also extremely depends on the accurate calculation of risks. The inaccurate estimation of the expected asset portfolio loss rate may result in systematic risks beyond the model prediction. In this case, CMBC may be exposed to losses in this industry or region. The huge bad debts of steel trade just took place in CMBC not long ago in 2012, inevitably raising our doubts about the effect of such kind of risk control. The advantage of big commercial banks in diversifying systematic risks can indeed protect small and micro finance from a fatal blow, but cannot completely prevent the banks’ losses caused by systematic risks. It is worth exploring how CMBC shall assure its accuracy of macro research in the face of economic downturn. Another fact which cannot be denied is that small and micro finance is serving a huge group of no normative financial information or credit record, so it is difficult and costly to determine the expected loss rate by acquiring information through the traditional method. In this case, the reliability of the pricing model of “profit covering risks” is affected. How to acquire customers’ accurate information at a low cost? This is a severe challenge faced by CMBC.

2.2.3 Transaction Principle of Large Batch and Small Amount By adhering to the principle of batch transaction, Shangmaotong of CMBC can avoid the bank’s disadvantage in high labor cost and meanwhile provide large-scale and large-number loans based on the “law of large numbers”. The batch transaction principle is embodied in Shangmaotong’s tactic of “business district and industrial chain”. The tactic of business district means that CMBC offers different credit products to small and micro customers of different levels and categories in the target business district; and the industrial chain consists of a core enterprise and

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Average recovery rate

the small merchants serving the core enterprise. This tactic reflects the characteristics of batch transactions. Depending on the “business district & industrial chain” credit management, CMBC reduces the workload of credit risk control, lowers the labor cost, enhances the adhesiveness among enterprises and facilitates cluster development. However, will the homogeny of the group be strengthened and will the systematic risks be increased when small and micro enterprises are gathered by business district or industrial chain (so-called customer integration)? This is an issue that CMBC must face squarely. The urban commercial cooperative and mutual fund model released by 2013 may help solve this problem, because the management rules of the cooperative significantly increase the diversity of members and the quantity of enterprises. The meaningful small-amount transaction principle of Shangmaotong reduces the average loan amount of each borrower and increases the number of loans, in order to achieve two goals: first, investing in the portfolio pool to hedge risks; and second, lowering the default rate of a single loan. According to the study, CMBC has discovered the inverse relationship between the rate of recovery of non-performing debts and the loan limit (see Fig. 2). Though the reduction of the single loan amount increases the workload and management difficulty, it improves investment safety.

Non-performing loan amount (RMB 10,000) Fig. 2 Relationship between the recovery rate of non-performing debts and the loan limit (Data source CMBC materials)

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And another relevant loan principle is: “Never impose excessive debts on customers.” According to CMBC, it is necessary to leave an appropriate margin for unforeseen circumstances when determining the loan limit, in order to assure that the borrower still has the repayment ability even the borrower loses all the loan funds due to the investment failure. This “customer protection” principle reduces the loan limit and meanwhile lowers the default risk. Since 2009, CMBC has adhered to the small-amount loan principle, targeted customers of lower levels and reduced the average loan amount per customer, entirely different from traditional commercial banks which always pursue large-amount loans. After small and micro finance Version 2.0 was released by CMBC in 2011, the decrease of the single loan amount has been further intensified (see Fig. 3). Figure 3 displays CMBC’s strategy of “lowering single loan amount” and reveals the significant increase of the number of loan customers. From 2009 to 2011, the number of small and micro loan customers had maintained stable at a low level; and the number started rising year by year since 2011 when small and micro finance 2.0 was launched. The loan balance per customer gradually declined from 2012 to 2014, and

Number of asset customers

Loan balance per customer (RMB yuan)

Fig. 3 Small and micro loan size of CMBC 2009–2014 (Data source CMBC annual reports)

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was RMB 1.55 million by 2014. Therefore, these loans are more “small loans” instead of “micro loans” based on the general practice of small and micro finance.

2.3

Risk Control Structure

2.3.1 Basic Structure Within CMBC, the risk control of small and micro finance business is carried out jointly by the risk management committee under the board of directors, small and micro finance department and different branches. The management is divided into three levels, strategic decision-making, business management and business execution The first is the strategic decision-making level. Like all other commercial banks, CMBC has set up a specialized department for credit risk control. The risk management committee under the bank’s board of directors is the highest authority of credit risk management of the bank, responsible for determining the overall business direction of the whole year, risk preference and risk management strategy. Small and micro finance risk control constitutes only a part of the risk management committee’s tasks. Under the overall planning of the committee, in March and April every year, CMBC headquarters risk management department, credit review department, asset monitoring department, legal compliance department and asset protection department etc. will cooperate to formulate the overall investment strategy of small and micro finance, control the systematic risks on the whole and prevent the large batches of defaults in small and micro finance business caused by the economic cycle, industry or district issues. Relevant departments of the headquarters will strategically manage investment risks by means of macro-economic study, “business district & industrial chain” research and the risk probability of the calculation based on the “law of large numbers”. This task may not be performed once a year, as the frequency may be adjusted according to the major changes to the macroeconomic environment etc. of the year

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Since enterprises show great correlations within a cluster (industry or district etc.), their defaults are uniformly distributed. Hence it is of great significance to well manage the cluster based on appropriate risk management policies and clarify the targeted industry or district. In 2009, small and micro finance of CMBC focused on business district management and manufacturing loans; faced with the economic downturn in China in 2012, the bank adopted new policies to make structural adjustments of small and micro loans and transferred the focus to the weak-business cycle industries including the service industry, hi-tech industry and retail industry etc.; and in 2014, small and micro finance upgraded its choice from the traditional “basic necessities of life” to great consumption and modern service industry and gradually turned its target from business district-centered economy to “O2O” platform economy and industrial chain integration. Apparently, the decisions on investment strategies of small and micro finance play an essential role for a big commercial bank that has abundant funds, operates in different regions and industries and can run large-batch businesses. Big banks enjoy a great advantage in research based on their strong research strengths and extensive information channels, by which they can acquire useful research results. Meanwhile, this is exactly the disadvantage of small regional banks and small financial institutions. The second is the business management level. The small and micro finance department of the head office and branches at the lower level, as per the organizational structure of CMBC, all have the function of business management. Based on the annual investment strategy formulated by the head office, the small and micro finance department under the head office shall combine industrial and regional considerations to prepare more detailed investment strategies, such as focusing on agricultural development in northeast China, stressing on infrastructure construction in west China, exiting textile manufacturing in east China and encouraging loans to cultural and creative industries and service industries in Beijing. Before 2013, the management system had not specified or clarified the administration privileges of branches. After the upgrading of small and micro finance business (small and micro finance Version 2.0) in 2013, the management power of branches has been

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significantly promoted, making branches the real business management level (see below). The third is the business execution level. As a level that finally implements the strategy and releases loans, the business execution level is engaged in the practical operation of business departments. For CMBC, the business execution level is made up by sub-branches. Even in the industries and districts of high-quality customers, the default situation cannot be completely avoided by means of policy and business planning. According to CMBC research, the loan default rate may reach as high as 5–6% if risk control merely relies on planning. Therefore, the credit management of individual cases is required to solve customers’ defaults. At the business execution level, the credit risk control focuses on individual cases and performs management before, during and after the loan against the default risk of each customer. In this process, the risk control department at the business management level is responsible for providing the tool of risk quantification to the credit department of branches as technical support. The branches follow the guidance of investment policies and risk quantification data and complete specific operations within a specified range according to practical conditions and their work experience. Before and during the loan, the sub-branches shall choose the borrowers in line with the branches’ investment strategies, review the loans and determine the collateral requirements, credit line and price based on the customer risk score provide by the superior offices. After the loan, branch workers shall make use of the risk control supporting system and follow and check the borrowers’ business conditions during the term of loans on the basis of the credit data from the People’s Bank of China and the industrial and commercial information from the court, thus to determine whether artificial follow-up and monitoring is needed. The above-mentioned overall structure and process of small and micro loan credit risk control of CMBC are shown in Fig. 4.

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Strategic decisionmaking level

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Formulate the overall risk policy Estimate the risk probability based on the law of large numbers Clarify the annual guiding opinions and goals

Follow the guidance of overall development strategy Business Small and micro finance management department of the head level office and branches

Formulate the specific development strategies for different regions and industries Combine the industrial and regional portfolio

Business execution level

Small and micro finance Construct the credit rating file department of the Provide risk quantification technology and head office technical support Sub-branches

Carry out the specific task of credit tracking

Fig. 4 Structure and process of small and micro loan credit risk control of CMBC (Data source Project summary of Renmin University of China on CMBC Small and Micro Finance Department)

2.3.2 Structural Reform CMBC adopts the head office-branch system. By 2014, it had established 39 branches and 1,021 offices.4 The overall structure of small and micro finance credit risk control of CMBC, a national commercial bank, gives fully play to its advantage in the organizational structure. If the head office is considered as the upper level, the provincial and municipal branches as the middle level and the prefecture and county sub-branches as the bottom level, the risk control power can be summarized as follows: The upper level (the risk management committee under the board of directors and the business management department of the head office) shall formulate the investment strategy and risk control principles, adjust strategic details and be responsible for all activities related to strategic decisions on risk control; and the middle and bottom level shall prepare and implement specific plans and complete the risk control activities at the business management level and business execution level (see Fig. 5).

4 Offices

here include the head office, 1st-level branches, branch business departments, 2nd-level branches, sub-branches and all kinds of branch offices.

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Oversight Committee

Strategic Development and Investment Management Committee

Shareholders’ Meeting

Office of Strategic Development and Investment Management Committee

Board of Supervisors Strategic Development and Investment Management Committee Investment Management Office

Risk Management Committee Nomination & Remuneration Committee

Office of the Board of Supervisors

Regional Audit Center

Audit Department

Strategic Development and Investment Management Committee Village Bank Management Department

Related Party Transaction Control Committee

Board of Directors Secretary of the Board of Directors

Nomination Committee

Office of the Board of Directors

Office of the Risk Management Committee

Remuneration & Examination Committee

Audit Committee Corporate Banking Management Committee Financial Director

Bank President

Assets & Liabilities Management Committee Risk Management Committee Retail Banking Management Committee

Product Innovation Management Committee

Consumer Rights Protection Committee

Information Technology Management Committee

Corporate Banking Department

Real Estate Finance Division

Retail Banking Department

Institutional Finance Department

Energy Finance Division

Credit Card Center

Office

Assets and Liabilities Management Department

Human Resources Department

Finance & Accounting Department

Development Planning Department

Operation Management Department Asset Custody Department

Traffic Finance Division

Internet Finance Department Technology Development Department

Bill Business Department

Risk Management Department Metallurgy Finance Division

Information Management Center Private Banking Division

Credit Review Department Brand Management Department

Asset Management Department

Cultural Industry Finance Division

Assets Monitoring Department Small and Micro Finance Department

Discipline Inspection and Supervision Office

Legal Compliance Department Investment Banking Department

Party-Masses Department Modern Agriculture Finance Division Asset Preservation Department

Financial Institutions Department

Health Industry Finance Division

Security Department

Minsheng Training College

Institution Management Department Trading Finance Division Minsheng Culture International Exchange Center Financial Market Department Secretariat of Social Responsibility Management Committee 39 Domestic Branches

Hong Kong Branch

1,021 Domestic Branch Offices

Minsheng Leasing 51.03%

Minsheng Fund 63.33%

29 Minsheng Village Banks

Minsheng Capital Management 40%

Fig. 5 CMBC organizational structure (Data source Annual reports of CMBC)

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As a matter of fact, before 2013, CMBC exercised the “two-level (head office and branches) management and one-level (sub-branches) operation” for small and micro finance business. The business department of the head office directly involves the specific businesses and has the right to approve projects and customers. However, this management method was “top-heavy”, where the head office had too much power, the power of the branches overlapped that of the head office and the sub-branches, as the lowest level, played a too small role. Small and micro finance serves small and bottom-level customers of a large quantity, wide distribution, big changes and great differences, so rapid and flexible response and disposing capacity are required in credit services to these customers and corresponding credit risk control. As the big banks are naturally farther to small and micro enterprises at the bottom level, though their branch offices can get closer to customers, they are not as “local” as regional financial institutions. Their loan approval and management system specially designed for key customers shows natural defects when it is applied to small and micro customers. In 2013, the small and micro finance 2.0 reform of CMBC was deepened comprehensively, which adjusted the management structure, followed the policy of “focusing on small and micro finance, establishing cooperation between both wings and making branches and sub-branches bigger and stronger”, reduced the scope of the head office’s authority and strengthened the branches’ power. The power of the head office changed from approving projects and customers to approving plans, while the previous power of approving projects and customers was assigned to the branches at the lower level, i.e. “making branches stronger”. It shall be noted that the power of sub-branches was not directly strengthened in the management reform of Version 2.0. CMBC only stressed on making sub-branches bigger, but not stronger. As a concrete manifestation, sub-branches were not given a higher authority in the reform of power decentralization of credit approval, suggesting that CMBC did not completely recognize the previous operation mode of sub-branches. Before small and micro finance 2.0 reform, CMBC sub-branches were exposed to decentralized operation, and market exploration and customer management mainly depended on loan officers’ relations and experience. However, this mode might lead to the

Centralized asset man agement

Centralized operation

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Aftersale service

Fig. 6 Branches are the core of business management and credit risk management in Version 2.0 small and micro finance (Data source CMBC Small and Micro Finance Blue Book)

principal-agent issues in a big bank of multiple levels, since loan officers might privatize customer resources and face other moral risks. By strengthening branches, CMBC centers on the branches to launch the “credit factory” mode popular in foreign large-sized small and micro finance institutions. Characterized by standardization, modularization and large scale, the mode requires branches responsible for credit products design, marketing planning and quality control as the core level of the factory and requires sub-branches promoting the implementation of policies and providing after-sale services to customers as the execution level. In Version 2.0, branches function as the core of business operation and credit risk control (see Fig. 6).

2.3.3 Change and New Challenge After around six years of exploration, CMBC started to approach the “credit factory” mode of business management and credit risk management. As a matter of fact, the “credit factory” mode is more appropriate to the small and micro finance business of CMBC, considering CMBC’s disadvantages in high labor cost, multi-level management and offices far

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away from small and micro-sized enterprises (not geographically) and its advantages in macro study, investment portfolio allocation and quantitative analysis etc. CMBC did not adopt this model at the beginning for its limited data acquisition ability or other reasons. Now, upon the return to the “credit factory model”, CMBC shall satisfy the higher requirements on credit data collection, analysis and evaluation and greater technical skills. Then, can CMBC successfully tackle this new challenge?

3

Risk Control 2.0—Credit Risk Control System Depending on Information Technology

Since the transformation of small and micro finance 2.0, the risk control of individual cases has been completely transformed from field observation and empirical judgment to the approval decision and loan tracking based on the credit data analysis model. However, for the sake of the accuracy of model rating, the risk control of small and micro finance shall become customer-specific. The key rests in the gathering of the underlying data. Only accurate and independent data can assure the effectiveness of the model. In view of the particularity and complexity of small and micro customers, the financial indicators such as the asset and income commonly used in formal loans do not completely apply to the decisions on small and micro credit. Hence exploring new information source and assuring information accuracy become the basic requirements of small and micro finance risk control. Along with the comprehensive upgrading of small and micro finance business and the focus of credit risk control being transferred to the technical model, CMBC must enhance its own strength in information acquisition and data analysis and processing.

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The Key—Full Access to Data

For the convenience of analysis, customers’ information is classified into static information and dynamic information by the way of information generation. Static information generally refers to the relatively stable information generated in a long period that unveils customers’ basic attributes; and dynamic information is the information that is generated recently and may be expose to rapid changes, revealing the characteristics of customers’ recent behaviors. CMBC mainly accesses to customers’ information through internal generation and external exchange (purchase). The following sections will discuss how CMBC gathers risk control data, which is divided into internal and external information based on the access to information.

3.1.1 Internal Information The internal information here can be interpreted as the customer information that CMBC gathers depends on its own strength. (1) Access to Internal Static Information Internal static information mainly consists of customers’ basic information and certificate images etc. Basic information includes customers’ identity information such as the age, gender, income, working years, family assets and liabilities and whether family members are in the court’s credit blacklist etc.; and also customers’ business information such as the enterprise’s registered capital, loan card information, sales volume, net profit, assets and liabilities and business scope etc. The scope of information covers not only the owners of small and micro enterprises and their family members. The corresponding certificate images left when customers apply for small and micro loans are all preserved. Considering the particularity of small and micro customers and their incomplete financial information, more static information is better.

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Internal static information is mainly acquired by credit workers of CMBC during pre-loan examination and loan review. With the credit process of Shangmaotong as an instance, the acquisition and review of internal static information is shown below (Fig. 7). As indicated in Fig. 7, after acquiring above information, bank workers will upload all the information to the corresponding information platform for the analysis and use by the whole bank. It can be seen that CMBC shows nearly no difference to other financial institutions in terms of the content of and access to internal static information. (2) Access to Internal Dynamic Information Commercial banks are engaged in some businesses that other small and micro financial institutions cannot do, like deposit, payment and settlement. These businesses provide banks with more operating incomes as well as more customer information, and enable commercial banks to track customers’ transactions more easily. Since small and micro enterprises lack safe financial systems and financial reports, transaction records have become the most important information source to help banks understand the enterprises’ latest operation status and financial position. For national commercial banks of high labor cost and weak local connections, their understanding of customers’ dynamic information including Sales manager Customer development

Sub-branch Loan amount approval

Account manager Field investigation

Sub-branch Loan purpose examination

Investigation report post Writing investigation reports

Branch Final review

Credit executive of the sub-branch Information acquisition Branch Secondary review Information check

Sub-branch Check the collateral

Branch Review

Sub-branch Release the loan

Sub-branch Online after-loan monitoring

Fig. 7 CMBC Shangmaotong credit management flowchart and the process of internal static information acquisition

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the transaction records will significantly facilitate their relatively weak after-loan monitoring. The internal dynamic information inquired by CMBC is exactly the transaction records of small and micro enterprise customers’ CMBC accounts. CMBC has attached great importance to the transaction records of these accounts, which is demonstrated by its design of small and micro financial products. Shangmaotong: According to the rules of Shangmaotong, any customer applying for a loan must open a personal settlement account and an enterprise settlement account in CMBC. Moreover, any customer applying for a credit loan or enterprise joint-guarantee loan must regard CMBC as the major settlement bank and undertake that the settled amount in CMBC shall be no lower than three times of the credit line. In this way, it can attract more savings and meanwhile provide the company’s transaction record for the bank’s after-loan monitoring. Xiaoweibao: With mobile terminals (such as iPad) as the carrier, Xiaoweibao, essentially an App, combines the mobile Internet, data analysis technology and small and micro finance services and provides the multi-functional mobile sales platform to the small and micro finance sales team. Relying on Xiaoweibao, CMBC carries out regular activities for small and micro enterprise customers under the theme of “Voices of Small & Micro Enterprises in Monthly Activities” and establishes the small and micro enterprise customer care system, including various activities targeting small and micro enterprise owners and their spouse and children. This customer care system supports the credit risk control from two perspectives. First, it can acquire multi-aspect information of small and micro enterprises and their families, directly offering information for after-loan monitoring. Second, it may attract customers to use CMBC as the major settlement bank, indirectly providing transaction information for after-loan monitoring. Shanglong Card and Leshouyin POS: Shanglong Card Set is CMBC’s brand new bank card product released to small and micro enterprise customers, applicable for small and micro enterprise owners and their families, business partners and employees. Shanglong Card users can pay flexibly with the enterprise account or the personal account or deposit or withdraw money at any time. Leshouyin POS is provided by CMBC to

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small and micro enterprises, by which they can pay and receive money at any time. If customers make payment with a CMBC card, the preferential of no service charge can be enjoyed. Leshouyin POS and Shanglong card not only retain customers and increase customers’ savings, but also bring precious information resources. By monitoring the transactions of Shanglong Card, CMBC can acquire the enterprise’s transactions, its upstream and downstream enterprises’ transactions and the enterprise owner’s household income and expenditure etc. As the common POS settlement system, Leshouyin will send the information on each transaction of the enterprise to the bank terminal and help the realization of after-loan monitoring. It is an exceptional advantage of big commercial banks in small and micro finance that they can acquire internal dynamic information. Compared to other micro loan institutions, commercial banks can track customers’ transaction records more easily and make use of such data for after-loan tracking and monitoring. However, to acquire and use the data on credit customers’ transactions, the precondition is to have customers choose CMBC as the major payment and settlement bank; while the prerequisite to this is to develop small and micro finance services in addition to small and micro loans. The direct deposit derivation rate (i.e. the ratio of loan customers’ deposit amount to the total amount of Shangmaotong loans) is used to reflect how often small and micro enterprise customers having acquired loans from CMBC will use other services of CMBC (such as deposit). According to the data by the end of 2010, the direct deposit derivation rate of Shangmaotong was merely 16.90%. Hence CMBC still has a long way to go before appropriately using credit information to achieve after-loan risk control.

3.1.2 External Information (1) Access to External Static Information The external static information acquired by CMBC is mainly the industry and commerce information as well as court information of small and micro enterprise customers. Industry and commerce information is

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mainly about the basic conditions of the small and micro enterprises registered in the industry and commerce system including the registration number and registered capital etc.; and court information comprises the information on the natural person or enterprise’s relevant law cases, by which CMBC can get to know the lawsuits related to the small and micro enterprise owners applying for loans or their enterprises. In addition, CMBC can also access to customers’ personal credit information and enterprise credit records from the credit center of the People’s Bank of China, as long as the customers resorted to personal or enterprise borrowings in banks. External information mainly comes from the credit center of the People’s Bank of China as well as the bureau of industry and commerce and court at the branches’ place. CMBC may acquire the information through purchase or exchange. From the current effect it can be seen that these data can be smoothly and successfully used to support the loans, but they are too limited to fully reflect the credit conditions of small and micro enterprise customers. Therefore, CMBC plans to further cooperate with industry and commerce and tax departments etc. in the future to assure the smooth information channel. (2) Access to External Dynamic Information The bank’s external dynamic information mainly refers to the dynamic information related to credit rating in customers’ business and life which cannot be acquired in the internal workflow of the bank, specifically including dynamic information on social contact, consumption, cash income and expenditure and business etc. This non-financial dynamic information makes up for the insufficient financial information of small and micro enterprise customers to a great extent. It constantly describes customers’ status and helps the bank accurately identify customers’ credit risk. CMBC can depend on its own Internet and mobile Internet technology to access to information on customers’ “on-line” activities (including social activities, consumption and entertainment activities etc.) without any technical barrier. It now uses the program developed independently by itself to cooperate with enterprises and departments possessing relevant data and acquire

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the information source. For example, it is negotiating with UnionPay on cooperation. The access to the bank’s external information somehow reveals the characteristics of a big commercial bank. As a high fixed cost is required to purchase information or establish the platform for the utilization of such information, only financial institutions that can provide a large amount of small and micro loans can pay the cost and make profit. In addition, in the current Internet age, some small and micro financial institutions such as Ant Finance and WeBank can even evaluate borrowers’ credit status based on non-traditional data like borrowers’ online consumption and social performance. By comparison, the external dynamic information acquired by CMBC is still confined to that directly related to customers’ credit record. Therefore, CMBC may need to further study and improve itself in terms of the acquisition and utilization of external information.

3.2

The Core—Effective Use of Data

The access to abundant data only constitutes the first step to realize the “credit factory” risk control model. The effect of this risk control model is directly influenced by data analysis, credit evaluation and the use efficiency of the analysis results, i.e. how to systematically apply the analysis results to the whole process including the review, release and monitoring of loans. During the reform of small and micro finance Version 2.0, CMBC has established the risk control technical system centering on the two “engines”, respectively the vertical search engine, mainly responsible for the data storage, inquiry and push, and the decision making engine, responsible for the generation and push of different scoring indexes during the loan management. Figure 8 displays the connection between the two major engine systems and the business system.

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Follow the requirements on the decision making engine to generate derivative indexes

Collect and save customers’ internal information and compliance information on site send the information to the cloud platform for small and micro finance through the desktop or tablet computer

Front office Vertical search engine

Accept the application and conduct field investigation

Conduct batch query on external information and create and send risk indexes

Decision making engine

After-sale system

Read and update customers’ electronic archives

Provide indexes to the models of application scoring, behavior scoring and credit scoring etc.

. Review system

Acquire information from customers’ electronic archives Fully understand the applicant’s information and risk warnings Combine the suggestions on parameters from models in review and approval

Fig. 8 Diagram of the working principle of CMBC “Vertical Search Engine” and “Decision Making Engine” (Data source CMBC risk management technology)

3.2.1 Vertical Search Engine The vertical search engine is essentially an information integrated query software. The workers who have the access can input the customer’s basic information such as the certificate number and application number in the desktop or tablet computer installed with this search engine, to acquire the information archives (including information inside or outside the bank) of the loan applicant and realize electronic management and monitoring during the whole loan process. The vertical search engine supports the business system mainly with five services, comprehensive query, information push, electronic archives, data analysis and sales support. The core among the services is the comprehensive query. Through the comprehensive query service provided by the engine, the following information can be found: • Customer’s basic information: Customer’s basic information includes the basic information of small and micro enterprise owners as well as their family members and enterprises, and also provides the automatic blacklist examination of the entities related to the credit business. • Industry and commerce information: The industry and commerce information displays the basic conditions of small and micro enterprises registered in the industry and commerce system in details, including the registration number and registered capital etc.

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• Bank information: The bank information gathers the information on credit transactions, settlement transactions, financial assets and the use of core products of the applicant, applicant’s spouse and associated enterprises at CMBC in one page. Workers can inquire about the applicant’s assets and credit use etc. at CMBC through this page. • Court information: The court information also provides the natural person or enterprise’s relevant industry and commerce information and enables workers to get to know the lawsuits related to the small and micro enterprise owner applying for the loan or the small and micro enterprise etc. • Certificate images: Uploaded by front-line workers, the certificate images record the image data acquired when customers apply for each small and micro loans at CMBC. • After-sale check: After-sale check includes behavior scoring and afterloan check, which mainly provides credit support to customers applying for loans for the second time. No credit record of after-sale check can be found if the customer is applying for the loan at CMBC for the first time. • Credit comments: Workers can see the brief summary of customers’ credit conditions at the credit comments page, which exhibits customers’ loan records, guarantee structure, loan-release status in previous five years and due balance of loans in the next five years etc. in a graph form. Workers can use information in this page for in-loan and after-loan monitoring and tracking. Account transactions: Account transactions are acquired based on the monitoring of customers’ accounts at CMBC. Workers can get to know the transactions of small and micro enterprises settled at CMBC, and, on this basis, understand customers’ sales incomes and transaction behaviors, analyze customers’ low and peak seasons, transaction counterparties, transaction time and transaction type and supervise borrowers’ suspicious large-amount transactions, suspicious counterparties and suspicious short-term transactions etc. Among above-mentioned information, customers’ basic information and certificate images are internal static information; after-sale check, credit comments and account transactions are part of the bank’s internal

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dynamic information; and industry and commerce information and court information fall into the category of the bank’s external information. The vertical search engine (Fig. 9) not only helps with pre-loan decision making, but also supports the decisions on providing loans to customers who apply for loans for the second time through after-sale

Fig. 9 Credit comments page of the vertical search engine (Data source CMBC risk control technology)

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check information (behavior scoring and after-loan check) and support business and management personnel’s monitoring and tracking with credit comments. The vertical search engine enables the bank to acquire customers’ information in a comprehensive, real-time, transparent and complete manner and assures the real-time query and tracking of customers’ conditions by relevant departments and workers of the bank. This engine plays an essential role in utilizing information technology to perform credit risk control. The quality of the vertical search engine largely depends on the complete and timely access to information, which is exactly the issue that CMBC must address.

3.2.2 Decision Making Engine Based on the massive raw information provided by the vertical search engine, the decision making engine utilizes the specific data analysis technology and analysis model to analyze data and creates derivative indexes, to provide a basis for various scoring models and support the four major decisions for the loan: approval, guarantee method, interest rate and credit line. The scoring system of the decision making engine mainly depends on Logistic regression analysis and the data of 0–1 categorical variables. At present, the decision making engine mainly supports the following decision making programs and models: • Policy and limit management: It includes the basic conditions for approval, policy requirements on credit and industrial and regional risk limits and so on. • Application scoring model: This model is divided into seven categories by the enterprise type, provision of collaterals, business district and industrial chain. When the customer applies for a loan, the decision making engine will apply the appropriate model automatically according to the business type and support the decision making with the scoring model.

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• Credit scoring model: It introduces the credit system of the People’s Bank of China to evaluate customers’ liabilities, liability structure and performance. • Two-dimensional risk rating: Create 8 × 8 cross decision matrix based on the customer’s application score and credit score, perform hierarchical management on customers and adopt different credit policies for different customers. For instance, provide weak-guarantee products to customers of high credit level, and increase guarantee requirements or reduce the credit line for customers of middle and low credit level. • Two-dimensional adjustment coefficient: In regard to the loan limit, CMBC calculates the customer’s basic loan limit based on the customer’s income and assets, then identifies the customer’s qualification with the two-dimensional loan limit adjustment coefficient and determines the loan amount according to the customer’s qualification. As to loan pricing, CMBC determines the basic interest rates of different branches and different products based on the regional characteristics of the branch, horizontal competition and guarantee method, and then decides each customer’s loan price with the two-dimensional pricing adjustment coefficient. • Behavior scoring model: It is directly applied to early warning, collection, credit renewal and pricing strategy etc. Figure 10 shows the role of the decision making engine in loan approval. Moreover, CMBC plans to resort to the capital management method specified in Basel II, combines the application scoring and credit scoring system in one regression model and adopt the significant variable construction scoring criteria, to further improve the accuracy of the model. With the release of the decision making engine, CMBC small and micro finance business has made great progress towards the goal of credit factory. This engine makes loan decisions more scientific and rational, improves the quality of risk control, reduces the working difficulty of grassroots banks and saves labor cost. By launching the two engines, CMBC has obtained a relatively advanced information technology and data analysis capability necessary

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Customer access Twodimensional risk rating Application scoring

Policy compliance inspection

Judgment

Guarantee requirements Twodimensional loan limit adjustment coefficient

Credit line

Twodimensional pricing adjustment coefficient

Credit pricing

Unqualified Credit scoring Withdrawal

Fig. 10 Role of CMBC decision making engine in loan approval (Data source CMBC risk management technology)

for risk control and established the basic platform, but the final results still depend on the quantity and quality of the information acquired. By now, the credit risk control system of CMBC small and micro finance Version 2.0 is approaching the scope of big data. The two major supporting systems also embody the unique characteristics of big commercial bank engaged in small and micro finance. Only financial institutions of sufficient funds can afford the high fixed cost of system construction; but they cannot depend on loan officers to carry out risk control during and after the loan like most micro loan agencies due to their high labor cost. By comparison, the informationbased risk control platform reduces the variable cost (i.e. labor cost) of a single loan and meanwhile transfers the focus of risk control from afterloan monitoring to pre-loan and loan review. It makes use of the bank’s special data on transactions, which can solve the issue of insufficient after-loan monitoring of commercial banks to a certain extent. Therefore, we believe that it is nearly necessary for big commercial banks to construct the information-based risk control system.

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How Is the Effect of Small and Micro Finance Credit Risk Control?

Risk control is the lifeline of financial business. Since the day CMBC stepped in the blue ocean of small and micro finance, it has never stopped the exploration and reform of the credit risk control model. Then, how is the effect? As we have no access to the specific cost and revenue of CMBC small and micro finance business, we can only try to understand its operation status through the bank’s annual reports released publicly.

4.1

Scale of Small and Micro Finance

The loans and advances of CMBC are divided into company loans and retail (personal) loans. Retail loans include the small and micro enterprise loans, credit card overdraft and housing loans etc. At present, the focus of CMBC small and micro finance business is still the small and micro enterprise loans. The total amount of loans of CMBC had steadily risen from 2009 to 2014. The proportion of retail loans in total loans had increased from 16.49% in 2009 to 36.11% in 2014; and the proportion of small and micro enterprise loans in total retail loans had grown from 6.10% in 2009 to 62.65% in 2014. In 2014, the small and micro enterprise loans had occupied as high as 22.63% of total loans (see Fig. 11). Therefore, it can be concluded that the development of CMBC retail loans between 2009 and 2013 mainly relied on the increase of small and micro enterprise loans. Though the development speed of credit card overdraft, another form of retail business, had exceeded that of small and micro enterprise loans between 2013 and 2014, small and micro enterprise loans still play an extremely important part in retail loans and even total loans, which reflects the strategic positioning of CMBC, “being a bank for private enterprises, for small and micro enterprises and for high-end customers”.

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RMB million

Total loan balance

Retail loan balance

Credit loan balance

Small and micro loan balance

Fig. 11 CMBC retail loan balance, credit loan balance and small and micro loan balance 2009–2014 (Data source CMBC annual reports)

4.2

Revenue of Small and Micro Finance

The revenue of small and micro loans is not expressly disclosed in CMBC annual reports. However, considering that the small and micro loans of CMBC have accounted for over 50% of retail loans since 2010, we can roughly judge the changing trend of the average rate of return of small and micro loans (average rate of return = interest income/average loan balance) based on the changes of the average rate of return of retail loans. According to the data in annual reports, the rate of return of CMBC retail loans was lower than that of company loans between 2008 and 2011, but has been increasingly higher than the rate of return of company loans since 2011. Since small and micro loans account for over 50% of retail loans, it can be estimated that the average rate of return of small and micro loans has been significantly rising compared to traditional businesses (see Fig. 12).

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Average rate of return of company loans

Average rate of return of retail loans

Fig. 12 Changing trend of the rate of revenue of loans of CMBC 2008–2014 (Data source CMBC annual reports)

The return of rate of loans only suggests the relationship between the interest income and the loan size, but cannot tell us the specific cost and risks of small and micro loans and company loans. Therefore, the answer of “whether the price of small and micro loans can cover the opportunity cost” cannot be answered. Nevertheless, Fig. 12 shows that the small and micro loan business of CMBC is becoming more mature, so we are optimistic about the profit prospects of CMBC small and micro finance.

4.3

Effect of Credit Risk Control

The risk control effect of small and micro loans may be judged by the changes of the non-performing loan ratio, which can be acquired from the annual reports of CMBC and is shown in Fig. 13. According to Fig. 13, the non-performing loan rate of CMBC small and micro loans has been rising more quickly than the non-performing loan rates of all loans of CMBC and all loans of the whole industry. The rate was only 0.09%, far lower than the rates of CMBC and the whole industry in 2010, but had increased to 1.17% in 2014, approximately equal to the non-performing loan rate of the whole industry. The

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Non-performing loan rate of all commercial banks in China Non-performing loan rate of CMBC Non-performing loan rate of CMBC small and micro loans

Fig. 13 Changing trend of CMBC non-performing loan rate 2010–2014 (Data source CMBC annual reports)

increase of the rate has been even accelerated since 2013. The first factor contributing to this situation is economic recession. Economic depression has been intensified nationwide since 2013, and medium, small and micro-sized enterprises have suffered from its impact the most. However, have the 2.0 transformation of CMBC small and micro finance implemented since 2011 and the comprehensive upgrading of the risk control system protected CMBC from part of small and micro loan losses caused by economic downturn to a certain extent? The answer can hardly be found from current data, so we cannot jump to conclusion on the effect of CMBC risk control system construction. Moreover, have the tolerance to the non-performing loan rate of smallamount loans been increased to a certain extent due to the basic principle of CMBC small and micro loan risk control, “profit covering risks”? We can judge the effect of this principle from the fact that the rate of return of CMBC retail loans is higher than that of general loans. Another notable phenomenon is that the non-performing loan rate of CMBC small and micro loans started rising at a higher speed compared to the rate of overall loans since 2011. Also in the meantime, the number of small and micro loan customers has increased sharply from 150,000 in 2011 to approximately 1 million by the end of 2012. Therefore, considering the investment principle of the “law of large numbers” and “price

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covering risks”, these numbers suggest that CMBC has created these changes deliberately. Nevertheless, the final effect of risk control shall be embodied in the variance of the net profit of small and micro finance businesses. Therefore, no analysis or conclusion can be given in this respect in view of the data availability.

Case Comments This case summarizes CMBC’s experience in credit risk control of small and micro finance business, in order to explore the risk control features, advantages and challenges of big commercial banks running small and micro finance business. To sum up, this case has clearly delivered four points. First, big banks have their own strengths and weaknesses in small and micro finance risk control (see Table 2). In regard to small and micro finance, compared to other small and micro finance institutions, big banks show certain disadvantages in risk control, including high labor cost, geographically long distance to customers and multiple levels of management etc., leading to the long process of credit, slow response to risks and insufficient after-loan monitoring. Meanwhile, in this respect, big banks also enjoy its own advantages such as the strong research capability (macro research, industrial research, data analysis and modelling capability), strong ability in cross-region and cross-industry investment and standard management. These advantages are embodied in the follows as to credit risk control: the ability to organize cross-region cross-industry investments to offset systematic risks; the ability to judge macro and industrial risks accurately and formulate investment strategies; and the ability to make investments according to the “law of large numbers” and determine the price based on the principle of “profit covering risks”. Giving consideration to both advantages and disadvantages, CMBC finds a mature credit risk control model, “credit factory”. Second, CMBC has given full play to its advantages as a big bank in risk control. The small and micro finance risk control model of CMBC has been upgraded from Version 1.0 to 2.0. In Version 1.0, CMBC’s

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Table 2 Big commercial banks’ strengths and weaknesses in risk control compared to small financial institutions Big commercial banks

Small financial institutions

Variable cost

High employee salary and large number of customers lead to too high cost of after-loan monitoring

Organizational structure

The complex organizational structure results in the long loan process and sometimes slow response to risks

Customer interaction

The interaction with customers is confined to fund borrowing. Cannot involve customers’ social life to acquire useful soft information

Fixed cost

Big commercial banks can bear the high cost for extensive research and the construction of the technical platform

Low labor cost makes it affordable to complete loan release and monitoring through field investigation and experience operation The simple organizational structure enables the rapid execution of loans and quick response to risks Have local connections, understand customers and apply credit incentives or other risk control measures according to the features of local customers Small financial institutions have insufficient funds to support a strong research team to conduct macro and industrial analysis and data mining

Type Commercial banks’ weaknesses

Commercial banks’ strengths

(continued)

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Table 2 (continued) Type Risk spreading

Full service

Research ability

Big commercial banks

Small financial institutions

National commercial banks can spread risks through the rational industrial and regional allocation of loans and rational pricing strategies Provide deposit, payment, settlement and other relevant financial services, to acquire customers’ relevant data for risk control

Small regional financial institutions are exposed to high risks and can hardly spread risks

Strong ability in quantitative analysis, modelling and research and development

Regional non-banking financial institutions provide only loan services, and cannot acquire information on customers’ transactions Weak ability in macro and industrial research, data analysis and modelling research

credit risk monitoring and evaluation over credit programs mainly relies on field investigation and empirical judgments, and the loan release and risk control are implemented through the two-level management and one-level operation system. In this case, the approval procedures are complex, and grassroots banks go their own ways without coordination, so the bank mainly relies on the borrowers’ networks or organizations such as joint guarantee, mutual guarantee and cooperatives to control and defuse risks. This method of “experience-based risk evaluation and joint guarantee-based risk control” is essentially similar to that of regional

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financial institutions like the Grameen Bank in Bangladesh. The advantages of big banks other than the investment strategy of the “law of large numbers” and the pricing strategy have not been utilized. Quantitative changes have taken place in Version 2.0 of CMBC small and micro finance risk control model compared to Version 1.0 (see Fig. 14). Version 2.0 regards the branches, the intermediate level, as the center of the business management model and transfers its focus of risk monitoring and evaluation to the backstage. Also in Version 2.0, the head office has established the vertical search engine and decision making engine for real-time monitoring and evaluation of credit risks, which generate indicators through the quantitative analysis model and assist the credit managers making decisions on credit management. Apparently, Version 2.0 stresses more on the acquisition and analysis of data, attaches greater importance to the role of information technology and Internet technology in all-round and real-time monitoring of credit risks and pays more attention to centralized analysis, centralized decision making and centralized management. Hence it reveals the characteristics of “credit factory”, i.e. the standard, modular and scaled management. According to CMBC, the credit factory shall concentrate its businesses at an appropriate level, the branches. Small and micro finance version

Version 1.0

Identification, Evaluation and Detection of Credit Risk Field investigation and empirical judgment Fragmented and nonstandard Quantified and standard

Version 2.0

Fig. 14 2.0

Vertical search engine + decision making engine

Loan decision making

Investment law of large numbers, pricing principle of price covering risks and principle of small-amount and diversified investments (“law of no excessive debt”)

Organizational structure of credit management and risk management

Two-level decision making (head office + branches), onelevel operation (sub-branches)

Making branches stronger, making sub-branches bigger

Management characteristics

Dependence of risk control on borrowers’ mutual-aid organizations

Decentralized operation at the grassroots level

Strong

Credit factory – centralized decision making and modular management

Weak

Upgrading of CMBC risk control model—comparison of Version 1.0 and

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Third, the key to the effect of the risk control model lies in the ability to acquire big data. The risk control effect of small and micro finance Version 2.0 depends on CMBC’s ability to acquire data. Why did not CMBC directly play its strengths as a big bank and choose the model of credit factory when it started small and micro finance business? A possible reason is the great difficulty to acquire abundant information on small and micro enterprises’ credit. Along with the progress of information technology and experience accumulation, CMBC has begun to emphasize the risk control model of credit factory. Despite the great research capability and data analysis and modelling ability of the big bank, the access to data is still the biggest challenge. By now CMBC has combined the data acquisition in every link of small and micro finance business. It acquires customers’ transaction records through the deposit, payment and settlement services etc., and has opened the channels to acquire external information, such as the “hard information” from the tax authority, the “dynamic hard information” from UnionPay and the dynamic information from other big data enterprises. It is an inevitable trend for commercial banks to utilize information technology to realize credit risk control, and thus to develop small and micro finance business. The effect of such risk control is significantly influenced by the completeness and accuracy of the underlying information acquired. Fourth, the risk control model shall be exposed to constant adjustments. The effectiveness of Version 2.0 risk control model also depends on the accuracy of the model in addition to big data. Hence CMBC shall make use of the model to analyze and judge the information on macroeconomy, business district and industrial chain to avoid systematic risks, use the model to evaluate enterprises’ credit information and formulate Shangmaotong’s pricing strategy where “profit covers risks”, and use it to conduct risk evaluation against the enterprises’ after-loan performance. After-loan monitoring is of particular significance due to high risk and no collateral of small and micro enterprises. Therefore, CMBC shall establish high-quality analysis models, and correct and adjust the models constantly according to data, thus to assure data accuracy.

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CMBC is still exploring its small and micro finance credit risk control model. Though the effect of this model cannot be comprehensively evaluated by now, the experiences and lessons accumulated by CMBC in previous years constitute significant references for other big commercial banks that are engaged in small and micro finance. Commentator: Zhang Ning5

References A Brief Discussion on the Development of Small and Micro Finance Business and Suggestions, “CMBC Information Management Center”, 2014. Bottleneck in the Current Development of Small and Micro Loans and Exploration of New Business Models, “CMBC Information Management Center”, 2014. On the Improvement of Small and Micro Finance Risk Control Based on the Exploration of Small and Micro Enterprise Customers’ Credit Data, “CMBC Information Management Center”, 2014.

5 PhD

of the Department of Finance, Renmin Business School, Senior Research Fellow of Institute of Finance and Securities, Renmin University of China.

Part III Digital Inclusive Finance

Inclusive finance mainly provides specific groups with “micro” financial services, including micro credit, micro payment, micro insurance and micro finance. Apparently, multi-batch transactions of smallamount financial products do not comply with the requirements of the economies of scale. Considering the fixed cost of formalities, the profit of small retail financial transactions is much lower than that of block transactions if the price is not increased. However, if the price is raised, the cost for specific groups (vulnerable groups) to acquire financial services is also increased, running contrary to the service nature of inclusive finance. How to solve this problem? One way is to lower the cost of financial transactions through technological improvement, thus to guarantee the profits of financial institutions and provide affordable financial services to specific groups. Digital inclusive finance is the inclusive financial services based on digital information technology. This part chooses four cases, which display how to improve inclusive financial services with the support of digital technology respectively from three perspectives, the digital information-oriented reform of traditional small and micro financial institutions, digital financial institutions and digital finance + traditional finance.

5 Information Reform and Efficiency Improvement of Traditional Micro Finance: Case of CD Finance Lianyun Zeng and Jiahong Shi

Inclusive finance means that everyone has the access to and can effectively use appropriate financial services, which shall be provided in a responsible and sustainable manner in a well-regulated environment. As digital information technology is widely applied to every aspect of economy and society, the constant efficiency improvement of information acquisition, dissemination and processing is pushing forward the unprecedented reform of many industries including finance. In particular, the spreading of Internet technologies and smart phones have made an extensive influence on rural production and life and gradually changed farmers’ living habits. Traditional inclusive finance institutions have to change and innovate themselves, in order to play their advantages L. Zeng (B) · J. Shi Renmin University of China, Beijing, China e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_5

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and stay competitive in this reform, and provide targeted customers with financial services responsibly and sustainably With the information reform of CD Finance micro credit busines as an instance, this case discusses digital information technology and efficiency improvement of traditional small and micro finance. What follows will analyze the background, goal and current status of information reform of CD Finance micro credit business and explore the effect of the reform based on questionnaire investigation and financial data etc.

1

Why Should CD Finance Carry Out Information Reform?

Growing out of China Foundation for Poverty Alleviation (CFPA) microcredit project department, CD Finance is a social enterprise dedicated to rural grassroots financial services. It launched the pilot project of microcredit poverty alleviation in poor rural areas in 1996 and was transformed to be a company in 2008, since when it has been dedicated to the implementation and management of microcredit poverty alleviation pilot project. CD Finance provides “mortgage-free, no-kickback, simpleformalities and door-to-door-service” microloans. By June 2016, CD Finance microcredit covered 2,832 towns, 185 counties in 18 provinces and had 2,677 employees and over 350,000 active customers; and its loan balance exceeded RMB 3.7 billion, but the average loan amount in the whole history was merely RMB 10,782.8.1 Despite such great achievements, CD Finance still faces the imminent task of information reform, which is mainly propelled by following internal pressures and external opportunities.

1 * Introduction to CD Finance [2018.01.31], http://www.cfpamf.org.cn/company_introduction# intl.

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Internal Pressures

The complete process of CD Finance to release and recover a loan before information reform before information reform was as follows (Fig. 1): If any borrowing need, the customer would contact the loan officer of local CD Finance branch office by telephone and made an appointment of the loan officer’s visit. When visiting the customer for the first time, the loan officer would explain to the customer the loan system and carried out pre-loan training, and the customer should fill in the loan application form. Then the loan officer would visit the customer’s neighbors, friends or local village cadres and old customers to have a better understanding of the new customer. After going back home, the loan officer would upload the photos of materials to the customer management system through the computer and submitted the paper documents to the branch office at the regular meeting held semi-monthly. The supervisor would call the customer to assure that the customer really comprehended the pre-loan training content, and the branch office director would be responsible for final approval. CD Finance head office would automatically extract information of part of personal loans in the system for credit qualification and compliance review, but would not conduct pre-review of group lending. Upon the approval of loan application, the loan officer would visit the customer’s home for the second time, when they would sign the loan contract and the loan would be released in cash. Afterwards, the loan officer would recover the loan at the customer’s every month. From the above introduction it can be seen that CD Finance already had an information system suited to the original business model. Therefore, information reform here includes the upgrading of the existing information system. CD Finance regards the middle and low-income families in poor rural areas that can hardly acquire loans from financial institutions as target customers. Its average loan amount of about RMB 10,000 in the twentyyear history clearly distinguishes its target customers from customers of rural cooperatives, Postal Savings Bank of China and other rural financial institutions, and clearly gives prominence to its fundamental goal of poverty alleviation. Different from poverty alleviation through donations, CD Finance, as a social enterprise, must fulfill its social mission

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Inside the Office

Customer’s Loan Process

Outside the Office

Call the loan officer if any loan demand

Accept the pre-loan training during the loan officer’s first visit to the customer’s home and fill in the loan application form

Conduct field investigation to understand the actual conditions of the customer

Telephone follow-up by the supervisor Approval by the branch office director

Go home and upload the customer’s information through computer

Spot check of some personal loans by the head office Loan approved in 2-3 days

The supervisor withdraws money at the bank Sign the loan contract during the loan officer’s second visit

Receive the loan

Release the cash during the visit The loan officer uploads the contract information through computer

Make monthly repayment

Collect cash at the customer’s every month and deposit in the bank

Fig. 1 Loan workflow of CD finance microcredit business before information reform

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and meanwhile maintain sustainable development. High requirements have been proposed on cost control, as CD Finance shall determine an appropriate interest rate to extend loan services to target customers and shall also guarantee sufficient interest income to cover the cost and achieve sustainable inclusive finance practice. At present, CD Finance provides two kinds of credit loan products, group loan and personal loan, and the single loan limit of both is below RMB 100 thousand. The term of loan is one year in most cases. Farmers can choose not to repay the loan in the first two months and make equal repayment of principal and interest in the following 10 months; or choose pay a little money in the first 11 months and fully repay the loan in the last month. Before information reform, the loan officer should collect the customer’s repayment in cash at the customer’s home every month, suggesting that the loan officer should visit the customer’s at least 10 times for the recovery of each loan. In regard to the organizational structure, the branch office of CD Finance generally consists of one director, several supervisors and loan officers. Supervisors are responsible for managing credit officers, carrying out peripheral investigation on risk control, expanding the market and handling emergencies; and loan officers are responsible for contacting customers, registering customers’ information and releasing and recovering loans etc. Since the loans of CD Finance have been released and recovered in cash before information reform, the posts of accountant and cashier have been set up to manage cash and record the cash receipts and payments, and some cashers also serve as supervisors concurrently. Figure 2 shows the organizational structure of general branch offices of CD Finance, and the sizes of these offices may vary. Branch office director

Supervisor

Supervisor

Accountant

Cashier (or concurrently serving as the supervisor)

Back office Front office

Loan officer

Loan officer Loan officer

Loan officer Loan officer Loan officer

Loan officer

Loan officer Loan officer

Fig. 2 Organizational structure of general CD finance general branch offices

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As the mortgage-free loan method is adopted, the risk control of CD Finance depends on joint guarantee and branch office workers’ understanding of local society. Front-line loan officers of branch offices play a key role in risk control. Before information reform, most loans were released right upon the approval by branch offices, and, in the aspect of risk control, the head office was only responsible for regular compliance examination and could not timely control the national business network. Three problems were found in the business flow and organizational structure of CD Finance explained above, which should be urgently solved through information reform. 1. Cash release and recovery at the customer’s home—high cash risk and labor and property cost CD Finance provided the door-to-door cash release and recovery service for every customer. With the increase of the number of CD Finance customers, the cash risk and the labor and property cost of this service became increasingly higher. Cash risk consisted of four parts, cash withdrawal, cash delivery, cash safekeeping and counterfeit money. Risk of cash withdrawal: Before each time of loan release, back office workers needed to make an appointment in advance and wrote a check to withdraw cash from the bank. However, in rural areas, particularly remote towns and villages, bank working times were often irregular and the cash that could be withdrawn each time was severely restricted, which might be even smaller than the amount needed on that day. As a result, the loan officer might be unable to release the loan to the customer as scheduled and break his/her promise. Risk of cash delivery: The back-office worker needed to bring a large amount of money, RMB 800 thousand to the maximum, to the customer’s home to release a loan. When recovering a loan, the credit officer also needed to bring a large amount of money to the bank of the town or village for depositing. The delivery of large-amount money might induce moral risks and potential safety danger. On one hand, the officer might be lured and violate laws and rules when carrying money

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of several hundred thousand yuan. For the release of a loan, the backoffice worker would carry the money and be accompanied by the loan officer, but this measure could not completely avoid moral risks. On the other hand, cash in transit might be stolen or robbed. When recovering a loan, the female credit officer might even need to be accompanied by her family members to assure cash security. Risk of cash safekeeping: According to the rules of CD Finance, the credit officer should deposit the cash in the bank account of CD Finance after recovering the loan. However, since customers often live in relatively remote places, so the loan officer might miss the working hours of the banks at the town after having recovered the repayable. Moreover, if one day a large number of loans were going to be released, the back-office workers might need to go out early to release loans. Considering the fixed working hours of banks, the workers might need to withdraw part of cash one day ahead. In this case, the branch office had to keep a lot of cash overnight, which was exposed to huge security issues. Risk of counterfeit money: CD Finance received cash, so counterfeit money could be a problem. However, to safeguard the mutual trust with customers, the loan officer did not bring a currency detector along in most cases. Though the loan officer emphasized “the customers know nothing about it and do not mean to deceive us”, the losses resulting from counterfeit money were borne by the branch office. This risk should not be ignored. Labor and property cost consisted of front office time cost, back office time cost and property cost. Front office time cost: The loan officer should recover the loan at the customer’s place each month. It meant that the loan officer must visit the customer at least 10 times for loan recovery. As customers of CD Finance mostly live in distant places, loan recovery in cash wasted massive frontoffice workers’ time. Meanwhile, loan release in cash also wasted the loan officer’s time. It took over half an hour for customers to count and check money, seriously affecting the loan officer’s work efficiency. Back office time cost: As CD Finance released and recovered loans in cash, each branch office should set up the position of accountant and cashier, who should keep cash journal every day, prepare and check the statements by the end of each month and be responsible for bills

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including the writing and approval of checks, cash reservation adjustment, issue of the certificate of receipts and payments and cash release and arrangement etc. The heavy and trivial work also caused a high labor cost of CD Finance. Property cost: When the loan officer deposited money at the bank or released or recovered the loan at the customer’s place, the oil consumption and car wear-out were borne by the loan officer. Though this cost was not directly assumed by CD Finance, CD Finance should provide the subsidy to the loan officer in a form of performance pay and indirectly pay the cost. 2. Information input by hand—heavy task of information input After getting back home, the loan officer should input customers’ information in computer, compress photos of signing of the day and upload them to the management system of CD Finance. After the first visit to the customer’s home, the loan officer should upload the customer’s loan application form; and after the second visit, the loan officer should upload the loan contract signed by the customer. Such information input by hand cost 1 to 2 hours every day on average, burdening the loan officer who had already been busy with heavy tasks. 3. Compliance check—hiding many risks The loan officer spent a lot of time and energy on uploading customer information and photos to CD Finance system. However, the head office would not check the compliance of such information ahead or approve the loan. Before information reform, the head office of CD Finance only regularly examined the compliance of the branch offices’ procedures at the end of the loan term, when the loans had already been completed. Therefore, the head office failed to control the risk by means of compliance check or timely involve in the credit activities of CD Finance. The compliance check not only lagged behind, but was also exposed to many risks, including the loan officer’s operation risk and moral risk as well as the customer’s credit risk intensified by the late compliance check.

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Loan officer’s operation risk: The loan officer should input the information on the customer’s bank card number and ID card number etc. in the computer by hand and then upload to the system, but the head office only examined such information after a long period in most cases. Moreover, the loan officer should upload many materials like site photos and customers’ information to the system during the process of loan release, and might be easily exposed to operation errors such as the missing of materials etc., which could hardly be timely discovered by the head office. Loan officer’s moral risk: CD Finance adopts the loan officer model by which the risks are in the front office. The review and loan release are not completely separated. Before information reform, the loan officer was solely responsible for nearly the whole process of credit. It means that the loan officer had the opportunity to seek personal gains by violating against rules. Though the head office conducted a spot check on personal loans and the branch office director and supervisor could supervise the loan officer’s work, the supervisor might collude with loan officer as the branch office of CD Finance was generally small. Therefore, CD Finance requires a more rational process of loan release that involves the head office more. Customer’s credit risk intensified by the late compliance check: The rate of risky loans has been increased during the economic downturn. Before information reform, the branch offices were responsible for the default risks of all customers. The late compliance check by the head office could not effectively reduce the overdue loan rate. As a matter of fact, the original workflow and information system could satisfy the demands of business development and risk management when CD Finance was small. However, along with the rapid business development in recent years, the risk and cost issues in original workflow have become increasingly prominent. Meanwhile, considering the constant size expansion, CD Finance needs to and has the capability to upgrade and transform the existing information system and build a better background supporting system.

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External Environment and Opportunities

Before information reform, microcredit business of CD Finance had been spread in rural areas for as long as 19 years. According to the customer satisfaction survey in 2015, customers’ satisfaction with CD Finance’s service efficiency reached 98.8%, and 92% customers had acquired loans within seven days, much more rapid than that of other traditional financial institutions in rural areas. Hence the microcredit business model of CD Finance is essentially a successful model that has been tested by time. The business pain points mentioned above are actually caused by the increasing pressure from customers as well as the changes of technical and financial environment in rural areas. In the tide of Internet finance, CD Finance realized that information technology should be the transformation direction of traditional small and micro financial enterprises. The improvement of rural facility construction laid a solid foundation for the technical transformation of CD Finance.

1.2.1 Increasing Popularization of Rural Information Technology Laid Technical Foundation for Information Reform Information technology has been continuously widespread in rural areas in recent years. With the constant improvement of cost performance of mobile phones, the number of mobile phones owned by each rural household has grown rapidly and approached the level of urban households (see Fig. 3). When the Internet firstly emerged, computer was the necessary device for Internet connection. Later, the mobile phone can be used to connect the Internet conveniently, so the device cost for farmers to access to the Internet has been significantly lowered compared to the age of computer connection. Therefore, the popularization of the Internet and the proportion of farmers accessing the Internet through mobile phones have evidently increased (see Fig. 4). With the high penetration of mobile phones and mobile Internet, people can record and share texts, pictures and other forms of information any time

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Mobile phones

Number of mobile phones owned by every hundred rural households Number of mobile phones owned by every hundred urban households

Fig. 3 Number of mobile phones owned by every hundred urban households and rural households (Data source National Bureau of Statistics)

and any place. The efficiency of information collection, dissemination and processing has been greatly improved, laying the technical foundation for CD Finance to change its heavy tasks of manual information input in microcredit business and the late compliance check and technically supporting the promotion of mobile finance and the reform of microcredit repayment method.

1.2.2 Constant Improvement of Rural Financial Infrastructure Created Financial Environment for Information Reform The improvement of rural financial infrastructure is manifested by two aspects, first, the increasing coverage of real finance in rural areas such as offline outlets of financial institutions, second, the rising popularization of information-oriented financial services in rural areas such as the online bank, mobile bank and third-party payment etc. In October 2009, China Banking Regulatory Commission (CBRC) held the work promotion meeting on full coverage of basic financial services in villages and towns, aiming to explore the effective approach to

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Internet penetration rate in rural areas

Proportion of farmers accessing the Internet by mobile phone in rural netizens

Fig. 4 Internet penetration rate in rural areas and proportion of farmers accessing the internet by mobile phone (Data source China Internet Network Information Center [CNNIC])

solve the equalization of rural financial services or other issues.2 After five years of efforts, the number of towns and villages lacking financial institutions had decreased from 2,945 by the end of 2009 to 1,570 by the end of 2014, and correspondingly, the proportion of such towns and villages in whole China had dropped from 7.21 to 3.89%. With regard to the penetration rate of information-oriented financial services in rural areas, the proportion of farmers using on-line payment had risen from 15.1% in 2009 to 35.2% in 2014, with an annual growth rate of approximately 4% (see Fig. 5). 2015–2016 was a peak period for online payment development. Though the data of rural areas have not been published, the national data3 showed that the penetration rate of online payment had developed from 46.9% by the end of 2014 to 64.1% by June 2016, and the growth rate was significantly higher than that in the five years before 2014.

2 * CBRC, Circular of CBRC General Office on Full Coverage of Basic Financial Services in Towns and Villages Lacking Financial Services, Y.J.B.F. [2011] No. 205. 3 *The data here include both rural and non-rural areas.

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Proportion of farmers using online payment in rural netizens

Proportion of towns and villages lacking financial institutions

Fig. 5 Improvement of rural financial infrastructure (Data source CNNIC, CBRC and National Bureau of Statistics)

With the rising penetrate rate of rural financial institutions and online payment in rural areas, an increasing number of farmers can save, withdrawal and transfer money conveniently, making it possible to change the cash recovery and release procedure of CD Finance.

2

How to Carry Out Information Reform?

In the face of the increasing cash risk and labor and property cost caused on door-to-door cash release and recovery, the heavy tasks of manual information input and the risks incurred by the late compliance check, as digital information technology is penetrating into economic society, CD Finance started the pilot reform of information management system since 2013 to standardize its operation. In this reform, it simplified the workflow, improved working efficiency, strengthened risk control in many ways and vigorously supported the chain development. By 2015, CD Finance had completed the information reform in all branch offices nationwide.

2.1

Main Measures for Information Reform

In 2015, CD Finance started the information reform in an all-round way, which included the centralized release and recovery of loans depending on the bank’s bank-corporate direct connection system and super online bank system, the optimization of the loan officer’s mobile client to

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automatically identify ID cards and bank cards, the construction of the risk review and approval team to strengthen risk management and the introduction of mobile office platform “Douxing” etc. Until May 2016, the information reform of CD Finance had been mainly performed in the following four aspects:

2.1.1 Centralized Withholding Payment With the increase of customers in recent years, the cash risk and labor and property cost caused by door-to-door services have become increasingly prominent. Therefore, CD Finance hopes to lower such risk and cost through centralized withholding payment based on the bankcorporate direct connection system and super on-line bank system of the bank. In July 2015, CD Finance officially started the implementation of centralized loan release. Before the reform, the loan was released in cash simultaneously when the customer signed the contract; but, by comparison, after the reform, the loan is directly released by the head office to the customer’s UnionPay card after the contract is signed by the customer and submitted to the head office for compliance check. Hence, the reform of centralized loan release has no reduced the loan officer’s visits to customers. However, the previous loan release by cash from cash withdrawal, delivery to release involved many risks and expenses. With the implementation of centralized loan release, the front-office loan officer no longer bears the risk of cash delivery, waits for customers to count money or spends time to check the authenticity of money; the back-office workers no longer undertake the work of cash reservation, check issue, management of complex cash journal or the issue and collation of loan vouchers etc.; and the original positions of accountant and cashier are cancelled, who are transferred to be the supervisor or interior specialist. Centralized loan recovery was also launched in July 2015. As customers need to deposit enough money in their bank cards for monthly repayment, they have to deposit or transfer money in advance. In this case, various situations may arise. For example, a few customers think it

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troublesome to entrust UnionPay to withhold the repayments, and some may ask the loan officers to recover the loan at their homes as they are busy in the repayment period. Therefore, it is a progressive process to realize loan recovery by means of UnionPay withholding. After one year of transition, UnionPay withholding repayments had been applied to 67% of group loans and 95% of personal loans. As agreed, a loan is recovered by 10 or 12 installments. In the past, loan officers spent much more time on recovering loans at customers’ homes than that on loan release. They were struggling with dozens of loan recovery tasks every day. With the gradual realization of recovery through bank card, the front-office loan officer only needs to check whether the money has been successfully withheld after the bank’s instruction is issued, communicate with customers as needed and leave the company for loan recovery if any issue with bank-card repayment. The loan officer does not need to recover the money at customers’ places tensely. The reduction of cash use significantly lowers the risk of cash delivery, cash safekeeping and counterfeit money and lightens the burdens on back-office workers such as cash journal keeping, verification of statements by the end of a month and gathering and sorting of receipt vouchers. Salesmen can use the time saved to maintain old customers and develop new customers.

2.1.2 Optimization of the Loan Officer’s Mobile Client By optimizing the loan officer’s mobile client, the loan officer’s efficiency of collecting, uploading and managing customers’ information can be improved. In 2013, CD Finance launched the pilot program to introduce the mobile client for loan officers, but most loan officers still used the computer. In 2015, CD Finance forced to abandon the computer end nationwide and required all loan officers adopting the mobile client to input, upload and management loan information. The mobile client of loan officers has been optimized for several times by now. The optimized version automatically identifies ID card and bank cards based the OCR technology and includes the modules of loan application, signing, review,

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recovery, customer management and statements etc., enabling loan officers to collect, upload and manage customer information any time and any place. When the loan officer visits the customer’s home for the first time, if the customer agrees with the terms of service, decides to apply for a loan and fills in the application form, the loan officer can add the basic information of the customer through the mobile APP, take photos of the customer’s ID card, bank card and the paper application form with signature and upload the photos to the system in real time. In this way, the manual information input is reduced and the accuracy is therefore enhanced. The supervisor can see the newly added customer information through the mobile APP in real time and timely call the customer to confirm on the customer’s willingness to borrow and repay money. When visiting the customer for the second time, the loan officer shall confirm on the completeness of information and sign the contract with the customer, and then upload the customer’s photos and contract to CD Finance management platform in real time (see the process of loan release after information reform in Fig. 6).

2.1.3 Involvement of Head Office Risk Management Department in the Whole Loan Process In 2015, CD Finance had established the risk review and approval team in the risk management department, which is responsible for risk control at the head-office level through credit access, material inspection and information, in order to control employees’ operation and moral risk as well as customers’ credit risk.

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Customer’s Loan Process

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After Information Reform

Call the branch office if any loan demand

Conduct field investigation to understand the actual conditions of the customer

Accept the pre-loan training during the loan officer’s first visit to the customer’s home and fill in the loan application form

Go home and upload the customer’s information through computer

Conduct field investigation to understand the actual conditions of the customer Upload the customer’s information at the customer’s through mobile phone

Telephone follow-up by the supervisor

Telephone follow-up by the supervisor Approval by the branch office director

Approval by the branch office director Spot check of some personal loans by the head office

Loan approved in 2-3 days

The head office can reject any personal loan

The supervisor withdraws money at the bank Sign the loan contract during the loan officer’s second visit

Release the cash during the visit

Receive the loan

Upload the contract information at the customer’s through mobile phone

Compliance check by the head office

Loan released to the customer’s bank card within 1-7 days after the visit

Go home and upload the contract information through computer

Collect cash at the customer’s every month and deposit in the bank

Make monthly repayment

Withholding collection of the bank card

Fig. 6 Comparison of the loan process before and after microcredit information reform of CD finance

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After the loan officer uploads the customer’s information and photos to CD Finance platform, the risk management department shall verify the compliance of such materials in real time. In case of the customer failing to comply with a certain condition for the loan, any missing of the information filled, inconsistency among the signatures or wrong people in photos etc., the department can timely communicate with the loan officer, and the loan officer will supplement material or conduct further investigation according to the actual situation. Between the two kinds of credit products of CD Finance, the release of the group loan is still subject to the final decision by the branch office director, and head office only performs compliance check; but the release of the personal loan is jointly decided by the branch office and the head office risk management department. The risk management department of the head office depends on the IPC microcredit technology to evaluate the personal-loan customer’s repayment ability in real time. The head office is entitled to reject the loan application submitted by the branch office if the customer is exposed to a high credit risk.

2.1.4 Popularization of Mobile Office Platform “Douxing” The mobile office platform “Douxing” aims to create conditions for employees to work and study any time and any place. In 2015, CD Finance successfully introduced “Douxing” mobile platform customized for employees, which consists of notice and announcement, CD Journal, CD College, online examination, colleague circle and some other modules. Employees can keep track of the company’s development dynamics through the notice and announcement and CD Journal, study and accept tests any time and any place through CD College and online examination module, and communicate with colleagues and cultivate teamwork through the colleague circle module. Compared to the past when employees could only study through emails and meetings, “Douxing” can improve the scope and frequency of employee trainings.

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Future Goals

By 2015, the technical team had grown from 20 to more than 40 people and had built and improved various business systems. In the future, CD Finance will further advance informatization and optimize the processes to achieve standard and efficient operation of the company. Meanwhile, CD Finance still adheres to its business as the core of information construction and has no plan to transform itself into a data-driven enterprise (such as a technology finance enterprise). To this end, CD Finance intends to carry out information reform in two aspects. First, continue to improve its internal information reform, depend on technology to optimize and simplify the process, lower the labor and material cost, improve risk management and connect with the supervision department, credit information system and financing systems based on information technology. Information construction information still sticks to the goal of the standard and efficient operation of the company. Second, cooperate with digital financial institutions for mutual benefit. In the wave of informatization, an increasing number of Internet financial enterprises are attaching great importance to rural credit market. The new big data-based financial organizations such as Ant Financial Services and JD Finance successively march into rural credit market. With a loan amount lower than that of rural credit cooperatives and rural commercial banks, such new financial institutions have their customer base overlapped with CD Finance’s target customers. Distinguished from these digital financial institutions, CD Finance is not a credit factory-type microloan company. Its unique feature lies in that the loan officer plays the guiding role, and local ground forces are indispensable for microloans. Therefore, seeing the rapid development of informatization, CD Finance and big data enterprises can cooperate with mutual benefit. Since November 2015, Ant Financial Services and CD Finance began working together. They have a complementary relationship in terms of the credit model: Ant Financial Services have abundant experience in big data, cloud computing and financial risk control; while CD Finance has loan officers who are familiar with customers and enjoys unique advantages in risk control and after-loan collection that Ant Financial Services lack. Information technology is only applied

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to serve the enterprise. Rather than transforming itself into an enterprise like Ant Financial Services and JD Finance, CD Finance depends on its own advantage in loan officers, utilizes these financial institutions’ information technology and achieve a win-win situation through cooperation. In the future, CD Finance may cooperate with more digital financial institutions in many aspects including channels, risk control, capital and big data etc., in order to jointly offer inclusive financial services to massive users in rural areas and help more people overcome poverty and achieve prosperity by means of “Internet + targeted poverty alleviation”.

3

How Are the Achievements of Information Reform?

The information reform of CD Finance has been implemented for a year. What we are most concerned about is undoubtedly the achievements of reform. Has the centralized withholding payment and receipt based on the bank-corporate direct connection system and super on-line bank system facilitated the loan officer’s work and lowered the risks caused by cash safekeeping? Have the mobile Apps indeed improved the salesmen’s working efficiency and helped them study financial knowledge more conveniently? Has information reform reduced the business cost of CD Finance in general and meanwhile improved its financial performance? How well do CD Finance employees and customers accept information reform? Since July 21, 2016, we started distributing questionnaires on the achievements of information reform to grassroots salesmen of CD Finance through WeChat. The questionnaires respectively investigate the front office (loan officers) and back office workers (supervisors) of CD Finance. By August 2, 2016, 1,103 questionnaires had been taken back, including 874 from front office workers and 229 from the back office. This conforms to the ratio between front office and back office personnel at the grassroots level of CD Finance. Moreover, we sent a team to CD Finance branch office in Quyang County, Baoding, Hebei Province for

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research and investigation on July 12, 2016, to conduct in-depth interviews with CD Finance employees and customers on their reaction to information reform. We will hereinafter analyze the achievements of CD Finance information reform from the perspective of financial performance, employees’ comments and customers’ attitude based on the financial performance, questionnaire investigation and field investigation.

3.1

Liberate Manpower and Explore Market

By liberating manpower, the information reform of CD Finance aims to lower the labor cost, and more importantly enable workers to spend more time on market exploration. The loan limit of CD Finance is much lower than that of rural commercial banks and rural cooperative banks, while the loan process is also more convenient. By comparison, private lending requires a higher interest rate and mainly targets male borrowers who “lead the family”. Therefore, CD Finance has few competitors in its business in rural areas as it offers a low loan limit and rational interest rate and targets female customers. Under these circumstances, to explore market, what CD Finance employees shall do is to make more residents get to know the business of CD Finance better through multiple communication channels, so more people can call to mind and seek help from CD Finance if any borrowing need. CD Finance depends on two ways to explore market. The first is the communication through word of mouth of customers; and the second is the direct publicity led by supervisors and assisted by loan officers. Supervisors and loan officers will publicize the characteristics and method of CD Finance loans among local residents through such traditional methods as road show, leaflets and posters and new methods like WeChat. Apparently, it is laborious work to explore market based on the approaches mentioned above. The manpower liberated by information reform shall be exactly used to explore market and attract more customers.

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Though business development is not the core work of loan officers, many officers find that they really benefit from information reform as they have more time to explore market and increase loan businesses (see Fig. 7). Objectively, the same conclusion can be drawn from the financial data published in CD Finance monthly brief reports. Figure 8 displays the average amount of loans transacted by a loan officer each month one year before and after the information reform; and Fig. 9 reveals the average amount of loans transacted by a branch office each month one year before and after the information reform. Considering the seasonal fluctuation of loans, such as more customers demanding loans at the beginning or the end of a year, the amount of loans of each month is Loan officers have more time for marketing Loan officers have therefore increased their business volumes Fig. 7 Loan officers’ feeling on business development based on information reform (Data source Analysis of investigation questionnaires of CD Finance) RMB 10,000

July

August September October November December January February

One year after information reform

March

April

May

June

one year before information reform

Fig. 8 Average monthly amount of loans released by a loan officer before and after the information reform (Data source Monthly brief reports of CD Finance)

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RMB 10,000

July

August

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October November

One year after information reform

December January February

March

April

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June

one year before information reform

Fig. 9 Average monthly amount of loans released by a branch office before and after the information reform (Data source Monthly brief reports of CD Finance)

generally compared to the same period in the previous year. It can be seen that after the information reform in July 2015, the amount of loans released by each loan officer or each branch office on average has risen sharply, which is even more evident in the months of business peak. It further demonstrates the important role of information reform in market exploration.

3.2

Improve Safety and Efficiency of Operation

3.2.1 Role of Centralized Withholding Payment Based on Corporate-Enterprise Direct Connection System and Super Online Bank System Before centralized withholding payment based on the corporateenterprise direct connection system and super online bank system was adopted, for each loan, the loan officer had to travel among the customer’s, bank and CD Finance branch office for a dozen times for loan release and recovery, and the supervisor also spends a lot of time

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due to the loan release and recovery in cash. Hence centralized withholding payment plays a significant role in liberating the manpower at the front and back office and improve working efficiency. Compared to loan release and recovery in cash when workers needed to safekeep a lot of cash, centralized withholding payment makes the loan release and recovery by CD Finance grassroots employees safer. We will first analyze the changes of loan officers before and after the centralized withholding payment based on the corporate-enterprise direct connection system and super online bank system was adopted. Figure 10 displays the average monthly quantity of loans released and received in cash by a loan officer before and after the information reform and the time spent on loan release and recovery in cash per month. Apparently, the most evident influence of centralized withholding payment on the loan officer lies in that the quantity of loans released and received in cash per month has been significantly reduced and the loan officer’s time spent on loan release and recovery has

171.3 loans Quantity of loans released and received in cash per month 23.7 loans

18.5 days

Before information reform After information reform

Time spent on loan release and recovery in cash per month 7.7 days

Fig. 10 Quantity of loans released and received in cash and time spent by the loan officer before and after information reform (Data source Analysis of investigation questionnaires of CD Finance)

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decreased. Since centralized recovery has not been fully realized in CD Finance, it still takes less than a day for the loan officer to release and recover loans in cash every month. Figure 11 shows loan officers’ intuitive feelings of centralized withholding payment based on the corporate-enterprise direct connection system and super online bank system. Most loan officers think that centralized withholding payment facilitates their work in many ways. More than 80% of the loan officers interviewed think that centralized withholding payment saves their oil consumption and their time to release and recover loans at the customers’ places; and over 90% loan officers interviewed think that centralized withholding payment saves their time to deposit and withdraw money at the bank. Loan officers pay more attention to the safety of cash during the loan release and recovery. More than 90% of loan officers believe that centralized withholding payment avoids the risk caused by the safekeeping of large amount of money and lowers the risk of receiving counterfeit money. Apparently, centralized withholding payment has facilitated the daily work of front-office and back-office employees and liberated the manpower of loan officers and supervisors. It also saves the financial resources consumed during the loan release and recovery, makes the process safer and significantly improves the working efficiency of grassroots employees. Reduce the risks incurred by the receipt of counterfeit money Reduce the safety issues caused by the safekeeping of a large amount of cash Reduce the oil consumption of vehicles Reduce the time spent on loan recovery and release at the customer’s Reduce the time spent on lining up to deposit and withdraw money at the bank

Fig. 11 Loan officers’ feeling of efficiency improvement based on centralized withholding payment (Data source Analysis of investigation questionnaires of CD Finance)

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3.2.2 Roles of Mobile Apps CD Finance has launched the credit management system App and the “Douxing” App for communication, training and study. Loan officers can use the mobile client to automatically identify ID cards and bank cards and upload most customer information at the customers’ places. Supervisors can use the mobile Apps to manage long officers’ customers. Moreover, front-office and back-office workers can all make use of “Douxing” to know company dynamics in real time and study all kinds of knowledge. Before the mobile Apps were launched by CD Finance, loan officers needed to sort out customers’ information after going back home and input the information in the computer by hand. Figure 12 shows the average time spent by the loan officer on this task every day before information reform. It can be seen that information reform has saved the loan officer about one hour every day, which has liberated manpower and improved efficiency indeed. In the meantime, supervisors have saved much time with mobile Apps and therefore have more time to do their own work. Figure 13 shows the time spent by supervisors on market exploration, material review and return visits to customers before and after information reform. As shown in the figure, after information reform, supervisors spend more time to develop the market and pay return visits to customers and less time to review materials, probably because they can manage customers’ information through mobile Apps in real time.

After information reform Before information reform Hours

Fig. 12 Time spent by the loan officer to sort out customers’ materials before information reform (Data source Analysis of investigation questionnaires of CD Finance)

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Hours

Before information reform After information reform

Market exploration

Materials review

Customer follow-up visits

Fig. 13 Time spent by supervisors on different tasks before and after information reform (Data source Analysis of investigation questionnaires of CD Finance)

Now, we can analyze the comprehensive attitude of front-office and back-office workers towards mobile Apps. Figure 14 describes loan officers and supervisors’ feelings of efficiency improvement based on the Apps. Before information reform, grassroots employees of CD Finance study relevant financial knowledge mainly through e-mails or the meetings held by branch offices. After information reform, most loan officers and supervisors agree that mobile Apps make study much more convenient. More than 70% loan officers believe that mobile Apps have reduced their time to input customers’ information. Paperless operation has been realized in CD Finance to a certain extent after information reform. Therefore, supervisors can manage customers’ information through mobile Apps and be saved from the trouble caused by the safekeeping of massive paper materials. Mobile Apps make knowledge study much more convenient than study through e-mails and meetings Reduce the time spent on inputting customer’s information Mobile Apps make knowledge study much more convenient than study through e-mails

Loan officer Supervisor

Reduce the troubles caused by the safekeeping of a great many paper materials of customers

Fig. 14 Loan officers and supervisors’ feelings of mobile apps (Data source Analysis of investigation questionnaires of CD Finance)

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Based on the subjective and objective situation of front-office and back-office workers, the information reform of CD Finance has indeed liberated manpower, saved financial resources, improved efficiency and made the whole process of loan release and recovery safer.

3.3

Advance Flat Management

Information reform enables the realization of centralized financial accounting and financial management. For this reason, branch offices have cancelled relevant financial positions. Before information reform, employees of financial positions must spend a lot of time and energy on writing cash journal and safekeeping materials for cash release etc. Figure 15 lists supervisors’ feelings of centralized withholding payment based on the bank-corporate direct connection system and super on-line bank system. Almost all supervisors consider that centralized withholding payment has reduced the issues caused by the safekeeping of cash. More than 70% of supervisors think centralized withholding payment saves accountants and cashiers the trouble to make account. The fact that not all supervisors choose this option may result from the fact that not all supervisors concurrently work as accountants or cashiers. The cancellation of relevant financial positions advances the flat management, makes the grassroots staffing of CD Finance more rational and assigns human resources to more valuable jobs. Reduce the safety issues caused by the safekeeping of a large amount of cash Save accountants and cashiers the trouble to make account

Fig. 15 Supervisors’ feelings of efficiency improvement based on centralized withholding payment by unionpay (Data source Analysis of investigation questionnaires of CD Finance)

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Improve the Risk Control Process

Before the information reform, the head office’s participation in the whole lending process was low and it only conducted regular examinations after the completion of loans, which were undoubtedly late and went against risk control. The information reform has improved CD Finance’s risk control process and lowered many risks of loan officers and customers. With regard to the loan officer’s operation risks, according to the questionnaires against grassroots workers, over 70% loan officers and supervisors suggest that mobile Apps can automatically identify the information of customers’ bank cards and ID cards and reduce the errors that may be made during manual information input. The information reform also involves the head office in real-time process of loan release. The head office conducts compliance check on customers’ materials timely. The interviewees agree that “Issues such as material missing can be soon discovered, which is better than the past when it takes a long time to find out the issues” (see Fig. 16). For the loan officer’s moral risk, customers can know their repayment progress through online banking and SMS prompt in time due to the bank-corporate direct connection system, which prevents the loan officer from embezzling customers’ funds during the loan recovery in cash. The full implementation of the mobile client, real-time positioning of loan officers and supervisors can be realized. Therefore, loan officers and supervisors upload relevant photos at the customers’ place to the head office for real-time review, which lowers the possibility of fraud by grassroots workers. Meanwhile, with the new system, the head office Reduce the errors that may be made during manual information input Issues such as material missing can be soon discovered

Fig. 16 Front-office and back-office workers’ feelings of compliance check (Data source Analysis of investigation questionnaires of CD Finance)

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risk management department can monitor the loan officer’s abnormal behavior in real time, thus lowering the loan officer’s moral risk. As to the customers’ credit risk, the head office risk management department involves the whole process of loans in real time and strengthens the credit qualification review over customers, particularly personal-loan customers. The withholding payment by UnionPay solves the issue of customers refusing to make repayment though they have money to certain extent.

3.5

Existing Problems in Current Reform

Information reform of CD Finance has acquired many achievements as mentioned above in the previous year. However, the reform is not perfect. The new system shall be further improved, and customers and loan officers still need more time to adapt to the new conditions. First, the new system shall be further improved. In field investigation and questionnaires, some loan officers and supervisors point out a series of problems in the new system, like study is not convenient enough in “Douxing” and the functions of mobile Apps are imperfect etc. As shown in Fig. 17, approximately 30% loan officers and half supervisors think that the mobile Apps do not work well, which demonstrates that the customer management mobile Apps for both front-office and back-office workers shall be further improved. Moreover, 35% loan officers say that the network signals are not good at the customers’ places, so they can only use mobile Apps after getting back home and cannot update customers’ materials in real time. Moreover, about half loan officers and supervisors point out the problems with centralized withholding payment by UnionPay. For example, the recovery or release of money is often delayed in the bank system. In this case, CD Finance workers have to explain the situation to customers or process the recovery or release of money manually. To solve the problems with the system, CD Finance has set up a QQ Group for the use of work clients. Loan officers and supervisors can report the problems they encounter to the IT department and get online answers in time. As an increasing number of questions will emerge and

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be solved, the mobile App system and centralized withholding payment system of CD Finance will be further improved in the future. Second, it takes time for employees and customers to adapt to new conditions. During the information reform of CD Finance, the centralized withholding payment based on the bank-corporate direct connection system was initiated in July 2015. Part of employees and customers cannot adapt to the new conditions due to the short transition time. Figure 18 displays employees and customers’ feelings of centralized withholding payment. Fewer than 10% employees feel troublesome to adapt to the new system and feel insecure without cash. While a few employees cannot adapt to centralized withholding payment by UnionPay, most of them accept this reform. More than 30% customers consider it too troublesome to deposit and withdraw money at the bank and therefore reject centralized withholding payment. Approximately 20% customers do not understand the working principle of UnionPay cards. For example, some customers think “it is too unsafe if you can deduct money from my card”. Over 60% CD Finance workers have an educational background of senior middle school or junior college, and about 95% customers are farmers. Hence we understand that employees Study with “Douxing” is not as convenient as study through e-mails and meetings The customer’s place has weak signals, and mobile Apps can only be used when getting back to the office Many functions of the mobile App system are imperfect and don’t work well

Loan officer Supervisor

Mobile App systems are too complex to adapt to The banking system is often exposed to late deduction or release etc. Study with “Douxing” is not as convenient as study through e-mails and meetings Many functions of the mobile App system are imperfect and don’t work well Mobile App systems are too complex to adapt to The banking system is often exposed to late deduction or release etc.

Fig. 17 Loan officers and supervisors’ feelings of the new system (Data source Analysis of investigation questionnaires of CD Finance)

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Feel troublesome to adapt to the new system Feel insecure without cash Customers consider it too troublesome to go to the bank and therefore reject UnionPay withholding payment Customers do not understand the working principle of UnionPay cards and therefore reject UnionPay withholding payment

Fig. 18 Employees and customers’ feelings of centralized withholding payment (Data source Analysis of investigation questionnaires of CD Finance)

and customers’ acceptance of centralized withholding payment needs to be further improved. Furthermore, we have interviewed some customers during our field investigation, and, to a certain extent, have learned the reasons for customers’ different feelings of centralized withholding payment (see Table 1). According to Table 1, customers’ different feelings of centralized withholding payment are largely associated with customers’ locations, living habits and ideas. Table 1 Reasons for people’s different feelings of centralized withholding payment Customers’ feelings

Convenient

Inconvenient

Centralized withholding payment based on the bank-corporate direct connection system Do not need to deposit money in the bank after acquiring the cash 1. Feel insecure without cash 2. Do not use the bank card often; live far away from the bank; need to withdraw money from the bank after receiving the loan 3. The loan may not arrive on time, affecting the use of the loan

Centralized withholding recovery based on the bank-corporate direct connection system Do not need to wait for the loan officer to visit at a fixed time every month 1. Do not understand why the money in their cards can be transferred without knowing the password 2. Feel troublesome to deposit money at the bank every month 3. Withholding deduction by the bank may fail sometimes

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It can be seen that among the existing problems with the current reform, i.e. the problem of the system and employees and customers’ adaption, some can be directly improved by CD Finance, such as strengthening the construction of the mobile App system and improving the access to the bank system; but some cannot be directly solved by CD Finance. For example, mobile Apps cannot be used due to the weak network signal and customers live too far away from the bank etc. As a matter of fact, the improvement of information technology conditions and financial infrastructure in rural areas constitute the important factors that drive the information reform of CD Finance. However, as reform has been advanced for a period, the information technology conditions and financial infrastructure may impede CD Finance’s informatization. Only upon the overall improvement of the technical environment and financial environment in rural areas can these issues faced by CD Finance be effectively and fundamentally solved.

4

Confidence and Effort

The achievements of CD Finance information reform have been proved through the case investigation and research. By adopting centralized withholding payment based on the bank-corporate direct connection system, optimizing loan officers’ mobile clients, having the risk management department of the head office involve the whole process of loan release and popularizing the mobile office and study platform etc., CD Finance has liberated manpower, saved financial resources, improved efficiency and optimized the process, and meanwhile has allocated more resources to market exploration and improved the risk control system. Though the information reform of CD Finance needs to be further improved in the future and it still takes time for employees and customers to adapt to new conditions, it has acquired great achievements by now, demonstrating that digital information technology can effectively serve the traditional inclusive finance institutions and help them achieve normative and efficient operation. The core competitiveness of CD Finance rests in its unique local ground forces. Loan officers’ familiarity with local society constitutes

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the great competitive advantage that other institutions can never obtain. In the meantime, CD Finance’s loan release model led by loan officers makes loan officers’ improper operation possible. Therefore, the digital information technology of CD Finance is applied to serve its core businesses, and has controlled the risks and significantly improved the efficiency of main businesses. With CD Finance as an instance, in the face of the tide of digital information and various market forces entering the inclusive finance area, traditional inclusive finance institutions shall first clearly identify their own characteristics and core competitiveness, keep pace with the development of digital information technology and introduce appropriate technology at the proper time to improve the process and efficiency. In addition, the support of technology and financial infrastructure is of equal significance. Informatization of CD Finance has been advanced by the changes of rural residents’ ideas, the rising penetration rate of rural information technology and the constant improvement of financial infrastructure. However, to comprehensive improve the availability of rural credit services and enable every rural resident to acquire proper financial services, the most fundamental factor is still infrastructure construction.

Case Comments In China, CD Finance is a financial institution that is the most similar to the Grameen Bank in Bangladesh. Deeply rooted in rural areas, CD Finance offers credit services to rural women to satisfy their microcredit demands emerging in their production, operation and daily consumption. As CD Finance provides short-term micro-credit products to low-income groups and depends on a business model of highly manual and frequent work (including frequent door-to-door visits and door-todoor loan recovery every month etc.), it is much more labor-intensive than big commercial banks. Therefore, its operating cost is much higher than that of traditional commercial banks. At present, CD Finance can make use of digital information technology to lower the labor cost,

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improve financial sustainability and reduce customers’ financing cost, which is exactly what CD Finance has been eager for in a long time. In this case, the author presents us with the great achievements of the information reform of CD Finance through the first-hand survey data. By adopting centralized withholding payment based on the bankcorporate direct connection system, optimizing loan officers’ mobile clients, having the risk management department of the head office involve the whole process of loan release and popularizing the mobile office and study platform etc., CD Finance has attained initial success including liberating manpower, saving financial resources, improving efficiency and optimizing the process. Informatization has truly benefited the company. These great achievements show that digital information technology can help traditional microcredit institutions solve the issues of high cost and low efficiency and realize normative and efficient operation, and, therefore, the new technology has good prospects for application. Apparently, it is a successful attempt of CD Finance to introduce digital information technology into the workshop-style microcredit business depending on manual operation. This attempt is of great significance not only to CD Finance, but also to similar traditional microcredit institutions that suffer from similar problems. We look forward to CD Finance continuing to forge ahead and providing more experience to peers. Commentator: Li Cun’gang4

Reference Asli, D, Leora, K. Measuring Financial Inclusion: The Global Findex Database. Policy Research Working Paper, No. 6025. https://openknowledge.worldb ank.org/handle/10986/6042. License: CC BY 3.0 IGO.

4 * PhD

candidate of the Department of Finance, Renmin Business School.

6 Defender “PPDAI”: Study on P2P Online Lending Platform PPDAI Yingxin Zhang

P2P online lending, the direct peer-to-peer lending depending on the Internet, has created a new lending model in the age of data and information. P2P online lending skirts professional intermediate agencies including banks and enables the direct contact between end borrowers and end lenders, forming a direct investment and financing model similar to stocks and bonds transactions. This financial innovation model, originating in the UK, entered China in 2006. Since then it presented extremely strong vitality and grew at a unique rate in the world. China has become the largest P2P online lending market worldwide. However, in view of the backward social credit information system

Members of this case study group include Yingxin Zhang, Lin Wang and Sheng Sisi. This report is prepared based on the data collected and interview record and has not been reviewed by PPDAI.

Y. Zhang (B) Renmin University of China, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_6

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and the lack of regulation in China, Chinese P2P online lending platforms have been exploring the question which development model can provide the best guarantee and help P2P online lending enterprises to rapidly develop at a correct direction. For the sake of rapid expansion, quite a few enterprises adopt the business model integrating “online” and “offline” business, offer loan guarantee means for investors such as advance payment and guarantee etc. In this way, they have rapidly increased their market shares and operating incomes. Seeing such chaos of competition, PPDAI persists in not crossing the bottom line and never offers advance payment or offline transaction. Under these circumstances, this oldest P2P online lending company once fell far behind peers in terms of the trade volume and had a low operating income. Why does PPDAI, a for-profit organization, insist on such a principle? What is the significance? We hope to answer the above questions through the in-depth study of PPDAI.

1

Background: P2P Online Lending

1.1

Origin of P2P Online Lending

P2P online lending refers to “the peer-to-peer microcredit transaction, which generally depends on professional e-commerce platforms to establish the debtor-creditor relationship between the borrower and lender and complete relevant transaction procedures”.1 “With the development of the Internet and the maturity of credit environment, the direct lending between individuals, the oldest financial model, exhibits its new vitality.”2 The Internet connection eliminates time and space constraints, enables numerous lenders and investors to build up connections beyond the region or their circle of acquaintances and immensely expands the scope of personal lending. In this context, P2P online lending based on the Internet arises at the right moment. In March 2005, the website “Zopa”, the first P2P online lending operator of the world, was born in the UK. 1 P2P

Finance, [2017-12-05]. http://baike.baidu.com/view/4821552.htm. Report on Internet Finance – To Rationality and Prosperity, course leader: Xie Ping.

2 2014

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The significance of P2P online lending lies in that it enables personal lending based on the Internet and, more importantly, realizes financial disintermediation. Without the support of traditional intermediaries, this subversive model directly connects the fund demanders and lenders and incorporates the large-scale lending beyond the circle of acquaintances in direct finance. With an extremely flexible transaction method, this intermediary-free lending transaction lowers the time cost and transaction cost, greatly improves the fund use efficiency and cuts profit in favor of both parties of the transaction—“Lenders enjoy more convenient credit financing channels and lower borrowing costs and investors acquire higher returns than bank deposits.”3 Once the Zopa model was released, it had immediately drawn wide attention and recognition and was soon copied in other countries. PPDAI introduced this model to China in 2007, and other similar platforms emerged one after another. The influence of such model has been constantly expanded, and the transaction volume has been growing. Based on the technical advantage, P2P online lending can rapidly match the fund supplier and demander and complete their transaction, regardless of the transaction amount. P2P also helps the borrower and lender break the limitations of region, social connections and fund in traditional lending and benefits much more people. The small and micro enterprises and individuals that cannot acquire traditional financial services due to the too high transaction cost can obtain convenient services through P2P online lending. Some scholars compared the data of American credit card company Experian and American P2P online lending platform Prosper, and found that the borrowers of Prosper mostly fell into the category of the group who have no easy access to traditional financial services due to low credit scores (Freedman and Jin, 2008). It suggests that P2P online lending serves more groups depending on the Internet and shows financial inclusiveness. Therefore, P2P online lending is considered as an important part of inclusive finance. In July 2015, the State Council listed Internet+ inclusive finance as one of the 11 key “Internet+” areas. As an Internet-based lending activity that serves small and micro enterprises and individuals, P2P online 3 Ibid.

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lending constitutes an important force for the development of inclusive finance in China.

1.2

Development of P2P Online Lending in China4

The year of 2013 is known as “the first year of Internet finance” of China. As a leader of Internet finance, P2P online lending has ushered in booming development and attracted the world’s attention. By now, “the accumulated transaction volume of ‘Lufax’, a P2P online lending platform in Shanghai, China, ranks first in the world”,5 and China has overtaken the US as the world’s biggest P2P transaction market. However, the number of P2P online lending platforms that are closed or run away on the halfway in China also leads the world. Since the latter half of 2013, a large batch of platforms have been closed or subject to a run. P2P online lending has suffered more and more queries, the development of which is called “barbaric growth”. According to 2014 Brief Report on P2P online lending in China published by the Home to Online Lending, the cumulative transaction volume of P2P online lending in the whole year of 2014 reached as high as RMB 382.9 billion, seeing a monthly growth rate of 10.99%. The cumulative transaction volume between January and July 2015 was RMB 383.129 billion (see Fig. 1). Only in July, the trade volume of the whole P2P online lending industry had attained RMB 82.509 billion, with a growth rate of 25.10% compared to the previous month and a growth by 3.8 times compared to the same period in the previous year. The cumulative transaction volume in the whole history of P2P online lending achieved RMB 766 billion. The number of P2P online lending platforms also rises rapidly. By the end of July 2015, 2136 platforms were still under normal operation, 5.32% higher than the previous month (see Fig. 2), including 217 new platforms opening in July.

4The data in this section come from the website of Home to Online Lending: http://www.wan gdaizhijia.com/. 5 China P2P Finance Impresses the Whole World . (2014-10-20) [2018-01-31]. http://news.163. com/14/1020/16/A910KL8P00014AEE.html.

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Nevertheless, along with the expansion of the transaction size and platform number, P2P platforms are frequently exposed to runs and bankruptcy. 109 platforms got into trouble in July 2015, by when the cumulative number of problematic platforms had reached 895. As the total number of P2P online lending platforms had attained 3031 (including problematic platforms), the problematic platforms accounted for 29.52% (see Fig. 3). Both the rapid development and the number of “run-away” incidents of P2P online lending in China created the world record. Such amalgamation of ice and fire of this industry reflects the disorder of the industrial development, which not only severely affects investors’ confidence but also results in the phenomenon of “bad money driving out good money”. Under these circumstances, many P2P platforms have huddled together and established the industry alliance for self-discipline, in order to draw a demarcation line with problematic platforms and seek the healthy and sustainable development of the industry. In addition, the industry appeals for norms, laws and regulations to renew the market’s confidence in the P2P industry.

RMB 100 million

January-July 2015

Year

Fig. 1 Transaction volumes of online lending in different years (Data source Home to Online Lending)

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Quantity of platforms

January-July 2015

Year

Fig. 2 Quantity of online lending platforms in different years (Data source Home to Online Lending)

Quantity of problematic platforms

January-July 2015

Year

Fig. 3 Quantity of problematic platforms in different years (Data source Home to Online Lending)

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According to the industry life cycle theory, the present P2P online lending industry in China is in the transition from the “birth stage” to “growth stage”, when the market keeps a rapid growth, “online lending platforms are mainly dedicated to exploring new users and occupying the market”,6 no clear regulation is imposed on the industry and the market barrier is so low that enterprises can enter and exit freely. As a result, P2P industry has good and bad mixed together now. The information of “regulation is coming” is constantly emerging, while nothing has come. The uncertain regulation policy is also one of the characteristics of the birth stage. However, the details of regulation were released in 2014, when the four red lines for P2P development were determined, the ten major principles for regulation were proposed and the P2P risk control mechanism was emphasized. In particular, the establishment of the inclusive finance department of the CBRC and the issue of Guiding Opinions on Promoting Internet Finance in 2015 will gradually restore the rationality of P2P industry and reorganize the industry, suggesting the transition from the growth stage to the maturity stage. Distinguished from the fierce competition in China, the P2P online lending market in the US is mainly dominated by Lending Club and Prosper, and Lending Club takes up about 80% market shares. On December 12, 2014, Lending Club was listed in New York Stock Exchange (NYSE) as the first P2P platform with a value of USD 5 billion. Apparently, it had completed its resource integration and established its mature development model. As a matter of fact, in the early stage, P2P online lending industry in China and the US “were at the same running line, as Lending Club was founded in 2007, the year when PPDAI emerged in Shanghai, China”.7 In 2008, Lending Club was registered at SEC and took the initiative to accept supervision, assuring the compliance of its business. However, until now no specific regulation standard has been formulated in China. Lending Club was successfully listed in 2014, while P2P platforms in China still has a long way to go. The development differences are closely 6 Wang

Jiazhuo, Xu Hongwei, 2013 Blue Book of China’s Online Lending Industry, Beijing, Intellectual Property Publishing House, 2014. 7 After Seven-year Development of China’s P2P Industry, Lack of Regulation Still Severely Hinders Its Development. (2014-08-21) [2017-12-05]. http://stock.sohu.com/20140821/n403638119.shtml.

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associated with different social environments in the wo countries. Many conditions cannot be yet satisfied in China. The P2P industry in China is growing in different soil.

1.3

Soil for P2P Online Lending in China

Compared to foreign countries, China leaves fertile soil for the growth of P2P online lending, where the vast space for market development is provided under the long-term financial regulation, and no specific management enables the barbaric growth of the industry. The P2P online lending industry is in a serious shortage of market infrastructure (laws, regulation and social credit system).

1.3.1 Lack of Perfect Information Disclosure System P2P online lending is essentially making a match between the fund supplier and demander, and in this process, it helps the investor accurately identify the lender’s credit risk. Therefore, information disclosure is the first principle. Based on the inference of “original sin” in finance, few lenders would be willing to disclose their full credit information truthfully. Mandatory rules are required. Information disclosure under mandatory rules can be realized by means of laws and regulations as well as a social credit information system. With regard to P2P online lending, European and American countries can give full play to their advantages in the robust judicial system and perfect credit information system. In the US, P2P online lending is considered as a kind of stock exchange and governed by the Securities and Exchange Commission (SEC). All P2P transaction platforms are required to fully disclose their risks by constantly publishing the loan issuance (i.e. income right certificate) instructions to explain the specific details of the borrower and allowing investors to access to the data through the data system and website. Moreover, all borrowers shall provide credit scores offered by credible credit information agencies as provided, thus to help investors rapidly and objectively measure the borrowers’ credit risk.

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However, the situation in China varies in two aspects. First, China has no sound credit evaluation provided by credible third-party credit agencies. The only authoritative credit organization in China, the credit information center of the People’s Bank of China, has refused to offer credit services to P2P online lending platforms for quite a long time after the emergence of P2P online lending platforms. Meanwhile, the credit records of this credit information center cover only 23% of national population, so its information is reliable but not substantial. Information disclosure in China only depends on the unverifiable information provided by the borrower, and the authenticity of such information relies on the borrower’s “consciousness”. Second, China has no clear regulatory organization that expressly provides the platforms’ information disclosure. The regulatory organization has not established the strict information disclosure system and punishment system. Due to the severely unsound information disclosure system, especially the lack of credible third-party credit force, P2P platforms cannot establish the automatic process of loan review and pricing and the corresponding risk control model. What they can do is to provide the credit comments (ratings) to investors for reference based on the information provided by the borrower, or offer risk guarantee to investors in other methods. As another serious consequence, the severely unsound information disclosure system encourages financial fraud in P2P online lending.

1.3.2 Lack of Regulation The P2P online lending industry in China is exposed to no entry threshold, no industrial standard and no legislation on regulation. Since no entry threshold, platforms can do business with only several thousand yuan; no industrial standard leads to the frequent occurrence of high-risk models such as capital mismatch and capital pooling; and due to no regulatory body or rule, no restriction is imposed on information disclosure and no punishment system is executed, so many platforms abscond with money, investors find it difficult to protect their rights and trials on fraud charges have no basis, resulting in a series of problems.

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In view of the huge differences between the development backgrounds in China and foreign countries, the P2P online lending industry in China presents a completely different look, manifested by the business model and the role of P2P platforms.

1.4

Variation of P2P Online Lending Models

1.4.1 Chinese Transformation of P2P There is an intermediary service provider in the P2P model—P2P online lending platform. It provides a direct and open transaction platform for the borrower and lender and frees the borrower from the complicatedprocedure bank loan that is hard to acquire. The platform plays the role of information intermediary in the transaction, “mainly providing the borrower and lender with information exchange, information value identification and other services that promote the completion of transactions”.8 In foreign countries, the platform is a middleman who does not involve the lending and mainly lives on the intermediary service fee and management fee. In this model, P2P platform is essentially an information agent (see Fig. 4). However, the imported product, P2P online lending, has varied since it was introduced to China due to the special environment in China and is no longer a simple platform of information intermediary. As China is exposed to a poor credit environment, severely backward credit information system construction and lack of regulation, the preconditions for the development of P2P online lending in Europe and the US are not equipped in China. Moreover, since domestic investors hold more conservative attitude towards finance and show low risk tolerance, in order to control the risk and obtain customers, most P2P online lending platforms in China have spent a lot of human and financial resources on building the sales and risk control team, to personally collect and verify borrowing information at the borrower’s or depend on local branch offices or outlets to seek potential high-quality borrowers. 8 Wang

Jiazhuo, Xu Hongwei, 2013 Blue Book of China’s Online Lending Industry, Beijing, Intellectual Property Publishing House, 2014.

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Investment Loan tender reviewed and released Investor

Credit rating P2P online lending platform

Borrower

Repayment of principal and interest

Fig. 4 P2P platform transaction process (Data source 2013 Blue Book of China’s Online Lending Industry)

In addition, to attract users, the platforms also launch the investor protection program and even give the principal guarantee commitment. Therefore, P2P online lending platforms have changed and are now playing the roles of information intermediary, credit intermediary and even capital intermediary. The security and guarantee model can rapidly increase the platform’s transaction volume. Hongling Capital created the principal advance payment model in 2009 and had led the P2P industry in China over a period relying on the guarantee model. Afterwards several segmentation methods have derived in the main links of P2P business. Figure 5 displays the details and differences in the main links of P2P online lending, which make up multiple business models. The main business models commonly applied in the industry include the traditional model, creditor’s right transfer model, security model and platform model.

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P : Investor

P: Borrower

Access to customers

Online

Offline

Combin -ed

Access to customers

Type of borrower

Third party

Ordinary individual

Small industrial and commercial household

SME owner

Online

Offline

Tendering method

Manual

Automa -tic

Regular financial management

2: Platform

Security mechanism

Matching method

Direct match

Creditor’s right transfer

Platform guarantee

Third-party guarantee

Product type

Risk margin

Credit loan

Mortgage loan

Guarantee loan

Fig. 5 Details in the main links of P2P online lending industry in China (Data source 2014 White Book of China’s Online Lending Industry)

1.4.2 Introduction to Main Models Traditional Model The traditional model (see Fig. 6) is the online-only peer-to-peer model. In this model, the borrower independently publishes the loan information and the investor independently chooses the lending program. The platform acts as a middleman and bears no responsibility for the transaction cost and after-loan capital management; and it does not provide any guarantee, leaving the default risk to the investor. Loan demand

Borrower

Repay principal and interest as scheduled after acquiring the loan

Investment tender

Interest return + principal Platform

Investor

Fig. 6 Traditional model (Data source 2014 White Book of China’s Online Lending Industry)

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The creditor’s right model (see Fig. 7) is also called the “many-to-many” model, which means that instead of both parties directly signing the debt contract, the fund is released to the capital demander through a thirdparty individual and then the third party transfers the creditor’s right to the investor. The third-party individual is highly associated with the P2P online lending platform, and is the core staff member of the platform in most cases. Then the P2P online lending platform will split the amount and period of the third-party individual’s creditor’s right into portfolios with fixed incomes and offer them to investors for choice.

Security Model The investors find it very difficult to accurately evaluate the borrower’s credit due to the imperfect credit system in China. Such lending risk is even further intensified in the virtual Internet. To cater to investors, the platform launches the security model to assure the investor’s capital security. The security model can be divided into the guarantee model and risk reserves model by the specific method (see Figs. 8, 9).

Investor

Financial products

Third-party individuals

RMB 10,000 one month RMB 30,000 one month RMB 40,000 three months

Borrower

RMB 500,000 a year Term splitting RMB 1 million a year Amount splitting

RMB 2 million a year

Fig. 7 Creditor’s right transfer model

9 Luo

Mingxiong, The Pure Platform Model and Creditor’s Right Transfer Model of the Three Major Models of P2P online lending, (2014-07-11) [2017-12-05]. http://stock.sohu.com/201 40711/n402104734.shtml.

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Loan demand

Investment tender

Repay principal and interest as scheduled Borrower after acquiring the loan

Interest return + principal

Platform

Investor

Third-party guarantee company

Fig. 8 Guarantee model (Data source 2014 White Book of China’s Online Lending Industry)

“The guarantee model is a lending model with fund guarantee service provided by the P2P platform to lower the investor’s risk. In such model, in case of any overdue-payment default, the investor can be compensated with the principal or interest by the guarantor, thus to lower or prevent the loss.”10 The guarantee model can be divided into two models by the source of guarantee fund, respectively guaranteed by the P2P online lending platform itself or a third party. “In the risk reserves model, the P2P platform sets up a capital account. If any overdue-payment default, the platform will use the capital in this account to pay the principal or interest for the borrower to protect the investor. The sources of funds include but are not limited to the platform’s fund, borrowing management fee and sharing of investors’ returns etc.”11

Platform Model With the development of P2P, the task of seeking borrowers has been gradually separated and assigned to some specialized microloan companies or guarantee companies. This is the platform model represented by 10 Wang Jiazhuo, Xu Hongwei, Intellectual Property Publishing 11 Wang Jiazhuo, Xu Hongwei, Intellectual Property Publishing

2013 Blue Book of China’s Online Lending Industry, Beijing, House, 2014. 2013 Blue Book of China’s Online Lending Industry, Beijing, House, 2014.

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Risk reserve account

Loan demand

Borrower

Repay principal and interest as scheduled after acquiring the loan

Investment tender

Interest return + principal

Platform

Investor

Fig. 9 Risk reserves model (Data source 2014 White Book of China’s Online Lending Industry)

Borrowing principal

Microloan institutions

Excellent investor

Final review of borrower customers

Development of high-quality Excellent borrower borrower customers

First review of recommended customers Monthly repayment of principal and interest

Guarantee institutions

Fig. 10 Platform model

Yooli.com (see Fig. 10). “The platform model means that the borrower comes from the microloan companies and guarantee companies the platform cooperates with, and the microloan companies and guarantee companies provide the guarantee. The platform does not involve the development of borrowers and principal advance payment.”12 As the most outstanding characteristic of the platform model, the investor no longer directly contacts the borrower, but directly contacts the lending institution.

12 Ibid.

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In general, the P2P online lending shall be established upon the mature personal credit construction. However, in view of the backward social credit system and the lack of financial regulation in China, most P2P online lending platforms have changed their business models and created diversified mixed models. While numerous P2P online lending platforms are competing to change their business models, only one still adheres to running business in the traditional model without providing any advance payment or guarantee. That is PPDAI, which introduced P2P online lending platforms to China. PPDAI is considered to be “too idealistic” and has developed slowly in recent years, and it has been surpassed by many newly-started platforms.13 Why does PPDAI always adhere to the traditional model? What is the significance? We hope we can get the answer through in-depth study.

2

Persistence of PPD

2.1

Development History and Operation Framework of PPDAI14

In 2005, Zopa model had been widely concerned and recognized by the whole world. In the meantime, Zhang Jun, the founder of PPDAI, discovered that small and micro enterprises in China met financing difficulties while many white collars had money but had no investment channel. Learning from the Zopa model, he established the first P2P platform in China, PPDAI, in 2006 jointly with three engineering students from Shanghai Jiao Tong University. In the initial entrepreneurship, they had seen the approach of the tide of Internet and finance combination. Or we can say, they were chosen by the P2P model. PPDAI had conducted multi-faceted exploration of its business model. In the early stage, their businesses were mainly introduced by 13 By now, among large-scale P2P platforms in China, only PPDAI is still adhering to the traditional model. Over 99% of platforms apply various forms of guarantee. 14 A Dialogue with Online Lending Operators: Issue 7, [2017-12-05], http://www.wdzj.com/zhu anti/dhwdr7.

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acquaintances and the business development showed a low efficiency. Then PPDAI turned to the open platform model, the embryo of the PPDAI model. Since it lacked data and could not find the appropriate methods of risk evaluation, PPDAI adopted offline investigation. However, offline investigation needed high cost but showed low efficiency, and the company had no enough experience to judge the credit risk. After half-year exploration and the full understanding of foreign online transactions, PPDAI decided to return to the pure online model in 2008, which is still used today without any change. As an iron law of PPDAI, the platform never makes advance payment for others. Hence, “pure online model without advance payment” has become a code word for PPDAI. See the development course of PPDAI in Fig. 11. In the pure online operation model without advance payment, the customer with borrowing need will firstly submit personal information, purpose of borrowing and other information to the platform; then, PPDAI will review the materials for authenticity, evaluate the default risk and publish the information that has passed the review on the platform for the customers with investment demands to choose; and the investor will finally determine the prospective borrower and loan In April 2015, PPDAI officially announced the completion of Series C financing, becoming the first P2P platform in China that acquires Series C financing In March 2015, officially released the “magic mirror risk control system”, the first risk control system really based on credit big data In Novem ber 2014, won the Best P2P online lending Platform in China of 2014 In March 2014, announced the completion of Series B financing, valuing tens of millions of US dollars In September 2012, acquired Series A venture investment of tens of millions of US dollars from Sequoia Capital In October 2011, was rewarded the “Prize of Most Innovative Model” and the “Prize of Best Contribution” in 2011 Information Service Forum In March 2009, the credit rating system, identification authentication system and anti-fraud system were officially released In April 2009, the free model was upgraded to be the charging model, and the business model was reported by CCTV and Forbes for the first time On June 10, 2007, PPDAI was founded in Shanghai as the first online lending enterprise in China

Fig. 11 Development course of PPDAI (Data source PPDAI Chronicle of Events from the PPDAI official website)

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amount. After both parties reach an agreement on the lending transaction, the borrower must repay principal and interest in accordance with the agreed repayment method and the investor acquire the corresponding return of investment. If the borrower breaches the contract, the platform will not offer any fund guarantee and the investor shall bear all default losses. In this model, PPDAI functions as a pure intermediary platform that publishes and examines information and offers no guarantee, presenting the original feature of P2P online lending.

2.2

Development Status of PPDAI

In the seven years of development, PPDAI has developed its transaction volume at a growth rate over 200% for five consecutive years. Its transaction volume exceeded RMB 1 billion in 2013 and 2.7 billion in 2014, seeing a growth by 158.36% (see Fig. 12). According to the report of the first quarter of 2015, the cumulative transaction volume of PPDAI in this quarter reached RMB 480 million, having grown by 274% compared to the same period in the previous year and setting a RMB 100 million yuan

Year

Fig. 12 PPDAI transaction volumes in different years (Data source IResearch report, PPDAI official website and the report of the 1st quarter, 2015)

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new monthly record of transaction volume. Meanwhile, the platform transactions become increasingly active. 1.65 million investments were successfully made and 90,000 loans were issued in the first quarter of 2015. PPDAI has spread its business to approximately 78% counties and municipalities in China, and the platform users are evenly distributed in a geographical sense. More than 6 million users have registered at PPDAI, becoming the largest online transaction user group in the industry. PPDAI is quite a success from the perspective of its own development. However, as the “first” P2P online lending platform in China and the “only” platform that maintains the original business model, how does PPDAI perform compared to the whole industry? Figure 13 compares the transaction volumes of the platforms representing different models. Hongling Capital adopts the guarantee model, and its transactions in 2014 valued RMB 14.774 billion. By cooperating with microloan companies, Yooli.com has attained a transaction volume of RMB 7.292 billion after two years of development. Renrendai.com, founded in April 2010, implements the risk reserves model and has reached a transaction volume of RMB 3.728 billion. Moreover, Yirendai, founded in RMB 100 million yuan PPDAI

Hongling Capital

Yooli.com

Renrendai

Year

Fig. 13 Transaction volumes of four major platforms in different years (Data source Yooli.com 2014 annual report, IResearch report, 2013 White Book of China’s P2P online lending Industry, Renrendai official website)

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2013, adopts the creditor’s right transfer model, and it has released loans totaling RMB 3 billion by February 2015 and reached a cumulative transaction volume over RMB 5 billion by May. By comparison, PPDAI, the first P2P platform in China with a seven-year history, only exceeded a transaction volume of RMB 2 billion by 2014, suggesting its weakening market attraction, rapidly declining status and slow development compared to other platforms. Some media describe it as the PPDAI that “never grows up”. Seeing the adverse development of PPDAI, other P2P platforms reflect on it and make corresponding changes. They have transformed their business models successively to attract more investors, with the transaction volume expanding rapidly in a short term. “PPDAI team is like a group of lonely idealists who are safeguarding their utopia.”15 However, their persistence has been admired and recognized by capital. In April 2015, PPDAI officially announced the completion of Series C financing, becoming the first P2P platform in China that acquires Series C financing. Investments always focus on the future. PPDAI has attracted institutional investments exactly for its sustainability and scalability. This may be the significance of their persistence.

3

Significance of the Persistence

While other famous online lending platforms in China undertake to make advance payment for customers through guarantee or other measures, PPDAI still adheres to its essence as a lending intermediary. Adhering not to involve in the transaction or compensate the principal, PPDAI seems to have fallen into the vicious cycle of “never growing up”. As a lonely defender, PPDAI has survived for seven years and won favor of the capital market. By now, it has successfully acquired Series C financing. Why are investors optimistic about PPDAI? What is its value? Hence we will analyze the significance of PPDAI’s persistence in terms of both economic significance and social significance.

15 Lin Mo, PPDAI History, (2014-02-21) [2017-12-05], http://www.iceo.com.cn/com2013/ 2014/0221/277828_4.shtml.

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Economic Value of the Persistence

3.1.1 Adhere to no Advance Payment The Real “Reassurance” The online advance payment-free model that PPDAI adheres to is the real future model of Internet finance and retains the original look of P2P. PPDAI does not involve in the transaction, while investors need to undertake the risks of overdue payment and bad debt. By doing this, the platform prevents the systematic risk caused by the advance payment, and shows strong vitality due to no operating risk.16 The obverse is the advance-payment model, by which the investor can acquire the principal guarantee from the platform or third-party guarantee company if the borrower breaches the contract. The undertaking of “principal advance payment” has won the favor of many investors as a “reassurance”, since the investors bear no risk and save considerable energy to judge the risks of the borrowers. This model responds to the focus question of the market and therefore rises rapidly. “Then, is such ‘reassurance’ really reassuring?”17 In 2009, Hongling Capital created the principal advance payment model. However, Zhou Shiping, the founder of Hongling Capital, said: “‘Principal advance payment’ is actually a ‘double-edged sword’. It secures the investor’s principal, but increases the risks of the website.” This is because the risks that are originally borne by the investors are transferred to the platforms or guarantee companies. When the platform fails to resist the risks and cannot afford the default losses, it will close 16 PPDAI

also launched a so-called principal security program on July 4, 2011, which proposed extremely harsh conditions. First, it guaranteed only the part of bad debt higher than the recovered loan instead of the whole loan. Second, it only offered guarantee to the investor who had successfully made investments for over 50 times and invested no more than RMB 5000 (and smaller than 1/3 of the subject amount) for each. Very few investors were qualified for this security program due to the harsh conditions. Within over eight months after this principal security program was released, only four users have triggered the conditions. This security program was essentially an incentive plan that encouraged investors to make diversified small-amount investments. 17 Ma Shujuan, The “Reassurance” of P2P Undertaking of “Principal Advance Payment” May Be Not Reassuring, (2013-06-13)[2017-12-05], http://www.wdzj.com/news/guonei/4797.html.

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down. In August 2014, the boss of Guangzhou Jinshanlian Paper was suspected of making off with money,18 which caused a bad debt of RMB 100 million of Hongling Capital. This bad-debt amount set a new record in P2P online lending. Due to the commitment of principal advance payment, Hongling Capital would cover the loss for the investors and therefore bore great financial pressure. In February 2015, the product “Anhui No. 4 bid” of Hongling Capital suffered a default loss of RMB 70 million due to the difficulty in capital turnover of Anhui Senhai Horticulture. Again, Hongling Capital had to cover this loss and repay the principal and interest. However, this promise cannot always be realized. For example, when the P2P online lending platform Daibang.com encountered a default loss as high as RMB 12.8 million, its CEO Yin Fei rejected to cover the loss, violating the promise of “repurchase the creditor’s rights under certain conditions” to investors specified in the risk disclosure on its website. Daibang.com breaching the undertaking was condemned by public opinion. Though this platform avoided the loss by refusing to fulfill the commitment, it may still be forced out as its reputation has been ruined. As proved by the above facts, the advance payment of the principal has significantly increased the platforms’ systematic risks, which may bring greater losses to investors eventually and therefore may not be the good way of security. Internet finance is still finance, since the Internet is only a means that does not change the essential attribute of finance. P2P online lending platforms must regard finance with awe. A healthy financial product shall rely on the perfect risk control system. Covering the loss for investors seems to save investors from the loss, but is essentially a major irresponsibility of the platform’s risk management. What the platforms shall do is to strengthen risk management and provide a safe investment environment for investors, otherwise they can never grow up really. Though PPDAI does not undertake to cover the principal of investors, it will not stop operation, go bankrupt or run away based on security improvement, and therefore the investors will not lose all the capital invested.

18 Investigation

of the Run-away of Guangzhou Jinshanlian Boss, (2014-09-03) [2018-01-31], http://finance.sina.com.cn/chanjing/gsnews/20140903/031320195699.shtml.201409014185 96190.html.

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As a pure information agent, PPDAI is dedicated to providing correct and full information to investors and helping investors make accurate judgments and complete the lending process. Investors can bring the risks under their control. By applying this healthier and more stable method, the platform loses the market in a short term, but wins the market in a long run. Sometimes the most “dangerous” place is the safest place. The real “reassurance” provided by the P2P online lending platform to investors is not advance payment, but is no advance payment.

Attract “Smart” Money Market is full of risks and uncertainties. Different people think and behave differently to respond to these risks. Without advance payment, PPDAI does not provide guarantee for the loan, and the investor bears the default risk. Therefore, rather than following the general trend, investors determine the capital use through judgment and calculation. According to PPDAI, “Each transaction of PPDAI is the wise choice of the investor, so the flowing capital in PPDAI is smart money”.19 In this sense, PPDAI has improved the investors’ ability to identify risks, who can make correct risk judgments based on the information disclosed by the borrower. However, in the advance payment model, investors will give up independent thinking or judgment but leave the option to the platform. They consider only about the rate of return of each investment but has no screening mechanism. In addition, high returns often lead to high risks. While investors are pursuing high returns, the platforms are faced with a greater possibility of overdue payment and default risk and suffer from greater pressure of covering losses for investors. Therefore, platforms need a stronger ability to control and resist risks. When the fund of the platform is insufficient to cover the bad debts, the capital chain will face a severe test. If the platform cannot protect itself, it can never safeguard the investors’ interests. 19 No Guarantee of PPDAI Tests the “IQ of Money” , (2013-05-05) [2017-12-05], http://www. wangdaizhijia.com/news/guonei/4338.html.

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With the advance payment-free model, PPDAI will select the investors with better investment knowledge and risk aversion awareness and filter out lazy investors. In other words, it attracts “smart money”. In a long run, the advance payment-free model will perform better and better in the future, but the advance payment model will be exposed to more and more problems.

Compliance Management Despite no explicit supervision regulation on the P2P online lending industry in the initial development stage, relevant departments have appealed for running P2P online lending platform as a simple transaction platform that matches the borrower and the lender on different occasions and set up some untouchable bottom lines. Therefore, practitioners of the industry are basically aware of the policy guidance even though no explicit law, but an increasing number of practitioners have crossed the line since the explicit law has not been released in so long a time. Until July 18, 2015, the long-expected Internet finance industry supervision policy was finally published. The ten ministries and commissions released The Guiding Opinions on Promoting the Healthy Development of Internet Finance. With the issue of the Guiding Opinions, the CBRC’s regulatory rules on P2P industry will soon come. According to the Guiding Opinions, individual online lending shall adhere to its role as a platform that provides intermediary services including information exchange, match and credit rating to investors and borrowers. The individual online lending institution shall clarify its essence as an information intermediary and provide information service to investors and borrowers for the lending without offering credit enhancement services or illegal fundraising. P2P platforms shall not provide guarantee for investors or make commitments for the principal or return of the borrowing. They shall not bear systematic risks or liquidity risks, involve in loan or entrusted investment business or provide self-guarantee or self-financing. In the meantime, the supreme people’s court published the judicial interpretation of private lending again after 24 years and clarified the essence of P2P online lending platforms as information intermediaries.

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Though the guarantee model by which the P2P platform introduces the third-party guarantee company has not been denied, the third-party guarantee company is required to be subject to the supervision under the Interim Measures for Risk Management of the Guarantee Institutions for SME Financing. The Interim Measures provide that the guarantee institution shall have the qualification as a financing guarantee company, its guarantee balance shall not exceed 5 times of its own paid-in capital in most cases and not exceed 10 times to the maximum, and the financing guarantee balance of a single guarantor shall be no more than 10% of its net assets. These regulations have kept a large number of guarantee companies off P2P online lending platforms. In this case, the P2P platforms depending on third-party guarantee for investors’ security will be under secured. At present, 99% P2P online lending platforms have incorporated advance payment, guarantee or credit enhancement in their operation models. With the release of supervision rules, a batch of platforms of illegal operation that have crossed the bottom line will be eliminated, or rectified, or forced out of the market. As it should be, the P2P platforms adhering to compliance management will usher in vigorous development. PPDAI adhering to its role as an information intermediary without advance payment or guarantee complies with these regulations. It refuses to undertake any systematic risk and policy risk and takes the most stable road. Meanwhile, it has cultivated a batch of investors insisting on rational investment and embodies the wisdom of PPDAI.

3.1.2 Adhere to Pure Online Model The P2P online lending platform model can also be divided into the pure online model and the model combining online and offline operation. The pure online model means that the channels of customer acquisition as well as the whole process including credit review, risk control, transaction and loan release etc. are all completed on the Internet. Most P2P platforms in China have gradually given up this model, mainly because of the imperfect credit information system in China and the

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difficulty to judge personal credit status. In this case, the online operation of most P2P platforms in China only makes up a part of the process of foreign P2P platforms, while user acquisition and credit review have been transformed to be offline tasks to varying degrees. For example, most platforms have directly established agencies or offices in different cities, and some platforms even apply the franchise mode and turn franchisees into local business operators. PPDAI refuses to do so. Not only adhering to no advance payment, it also adheres to the “pure online” model, i.e. completing the whole process online. Among all P2P online lending platforms in China now, only PPDAI expresses clearly that it does not run business offline. Why does PPDAI adhere to pure online business? We can examine the significance of such persistence from the perspectives of user acquisition and credit review.

Credit Review PPDAI conducts credit review based on the big-data risk control model online. However, the big-data risk control is only built upon the accumulation of data. As there is no credible third-party credit service, PPDAI faced the lack of data in the early stage. Even so, PPDAI still adhered to online risk control, since offline investigation did not perform well due to the too high cost and insufficient experience. In this period, the online credit review of PPDAI stressed more on qualitative analysis instead of quantitative analysis on the borrower’s information to avoid too many questions. Along with the increase of available public information and the accumulated customers’ information, PPDAI’s credit risk evaluation has gradually transformed from qualitative analysis to quantitative analysis. Different with PPDAI, many P2P platforms have transferred credit review to offline operation due to the lack of social credit services. They communicate with borrowers face to face to collect and verify the borrower’s information. By doing this, the risk can be controlled in a small scope. However, as this process involves many links and participants and has no uniform standard, it largely depends on the reviewers’ personal experience, which incurs high cost and questionable quality.

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Therefore, it is not the optimum model for P2P online lending platforms. Conversely, though the loan risk valuation depending on the data model may be inaccurate based on limited data, the system’s judgment on users’ risks will be increasingly accurate with the accumulation of users and transaction records. Apparently, the P2P online lending platform adhering to the pure online model shows its value in credit review.

User Acquisition The transaction scale of the P2P online lending platform is crucial, as the cost cannot be covered if no large scale. Seeing the rapid growth of Chinese residents’ savings and the large number of potential investors (lenders), the core to expand the platform’s transaction size is to develop the scale of borrowers and attract more borrowers as users. In the pure online model, the development of borrowers is also completed online. The key to attract borrowers is to spare no effort to provide low-cost and rapid financing services to borrowers. Compared to the offline model, the pure online model significantly lowers the labor cost (batch operation) and shortens the review time (the big-data analysis model can be used to rapidly complete the credit review, approval and release of loans). With the users’ positive cooperation, the whole process can be completed within several minutes on the PPDAI platform. Therefore, the pure online model satisfies customers’ low-cost high-efficiency financing demand and shows certain advantages in attracting customers. But its weakness lies in that it always passively waits for customers instead of taking the initiative to seek customers. In the offline model, P2P platform develops the market through the staff at local outlets. By taking the initiative, this model can explore customers with borrowing needs, particularly large-amount borrowers, and thus rapidly increase the business volume. The disadvantage of this model rests in the high labor cost, shop cost and management cost. To cover the rising operating cost, the platform shall increase the charges or increase the amount of a single loan to achieve the scale effect. However, as the amount of a single loan of P2P online lending is increased, the

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small and micro loans will be replaced by the middle and large-amount loans. Correspondingly, the small and micro finance will be transformed into traditional finance. Consequently, the P2P online lending platform needs to compete with traditional banks, and the increase of single loan amount will increase the credit risk of the platform. PPDAI refuses the offline model, thus avoiding the large offline team and high operating cost, and adheres to the pure online model to attract customers. By doing this, it controls its operating cost below 2%. With this low cost, the platform can cut profit in favor of the customers and then attract more customers. As the user base is expanding, the platform’s cost advantage will be promoted due to the scale effect, and thus a virtuous circle will be formed. Not only lowering the cost and speeding up loan release, the pure online customer-acquisition model also shows advantages in high customer retention and great marginal contribution of employees. Along with the increasingly fierce competition of the industry, rather than customer quantity, “customer retention has become the important indicator to measure a platform’s value”.20 Apparently, by comparing the online customer acquisition model of PPDAI and offline customer acquisition model, it can be seen that customers spontaneously choosing the platform would be more likely to be retained than the customers pulled in by marketing staff. Moreover, by adopting the systematic batch operation for the whole transaction process, the pure online model significantly reduces the quantity of employees and improves employees’ marginal contribution. By now, “each employee of PPDAI contributes a transaction amount of approximately RMB 500 thousand (the size of loans that the employee successfully matches) every month on average, while the monthly per-capita contribution of transaction volume in P2P companies mainly running business offline is about RMB 40 thousand”.21 Thus it can be seen that the PPDAI model is the most efficient P2P model. A large research and development cost will be necessarily

20 Etongdai:

User Retention Is the New Indicator for Investors to Choose the P2P Platform, (201412-09) [2017-12-05], http://www.prnews.cn/press_release/118967.htm. 21 P2P Model Debate: PPDAI Fervently Supporting Pure Online Model , (2014-03-10) [2017-1205], http://www.niwodai.com/view-xindai/article-fe602162536.html.

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Table 1 Core characteristics of online model and offline model Business User Transaction link acquisition Risk control cost Policy risk Online

Offline

1. Facilitate 1. Control user size transaction expansion data and and records and precision accumulate marketing; data and 2. User controllable data can experience; be traced 2. Improve and risk control accumulated; efficiency 3. Improve and long-term accuracy user and lower education employees’ and moral risk; retention 3. Be restricted by data size and dimension 1. Boost 1. Adopt the growth of low-risk the traditional transaction risk control amount in model; 2. a short Be term; 2. vulnerable Rapidly to moral persuade risks; 3. and Show a educate longer users cycle; 4. Cannot acquire the core data

1. Save labor cost and operating cost etc.; 2. Improve efficiency with batch operation and show scale effect

1. Do not cross the policy red line as a third-party platform

1. Involve too 1. May easily many cross the offline policy red links, incurs line by high cost illegal and injures attraction the of public inclusiveness deposit and of P2P illegal fund raising

Data source IResearch Consulting, Research Report on Typical Case Model of P2P Microloan

incurred to establish such a set of operation system, but the average cost allocated to each user will be very low if the transaction size expands. See the core characteristics of the online model and offline model in Table 1.

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According to the above analysis, the superiority of the online and offline model cannot be simply judged by the number of customers acquired. CreditEase which implements the offline model had reached a transaction size of RMB 30 billion in 2013, while the transaction amount of PPDAI adopting the online model was merely RMB 1 billion. In this sense, PPDAI was far inferior to CreditEase. However, the transaction cost of CreditEase would be necessarily higher than the cost of PPDAI. Such high transaction cost will drive the platform to be inclined to large-amount transactions and stay away from small-amount transactions, which does not comply with the government’s policy guidance and intensifies the operating risk. In the long run, we prefer the online business model used by PPDAI, as this model can give full play to the advantages of Internet-based platform transactions. It will last long if we persist. Adhering to no advance payment and to pure online operation model, PPDAI seems to develop slowly and be conservative. However, it is less vulnerable to external environment and depends on its own user retention and data accumulation. Despite the slow growth, it features the backwardness advantage. PPDAI chooses a “longer and more stable” road. With the persistence, PPDAI sees a promising future with backwardness advantage, which may be the value that investors cherish.

3.2

Social Significance of the Persistence

3.2.1 Practice Inclusive Finance The development of Internet technology has lowered the marginal cost of enterprise supply, changed the supply curve, subverted the economic 2/8 principle by having the supply curve and demand curve intersect at the far end and created huge profit margin for small business. Making use of the long tail effect, the P2P online lending industry is dedicated to serving the small and microfinance transactions of small and micro enterprises and individuals, who have been abandoned by traditional finance and are served by inclusive finance. Hence, P2P online lending is serving the object of inclusive finance and doing what inclusive finance does.

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PPDAI platform targets grassroots users and aims to help the group who have no credit system to borrow money, so its loans range between several thousand and tens of thousand yuan. In 2013, the average loan amount of a single transaction was RMB 11 thousand, and the amount of 87% loans was below RMB 10 thousand. This average single loan amount continued to decline in 2014. According to Zhang Jun, the average single loan amount of PPDAI in 2014 was about RMB 6000. Microfinance in China is a blue sea of a huge market. It can be calculated based on the data of the People’s Bank of China credit center that only 23% of the total population in China can acquire banks’ credit services. This figure demonstrates the broad market for microfinance. PPDAI became aware of such a huge market gap as early as 8 years ago and first entered this industry to offer microfinance services. While realizing its economic interest, PPDAI also financially serves the groups rejected by traditional finance and practices the concept of inclusive finance. More importantly, PPDAI controls its cost by adhering to the pure online business model. By doing this, it does not need to increase the loan limit or change its nature of serving the people under the cost pressure caused by offline operation, and it always adheres to serving customers of lower levels, particularly the social groups at the bottom of society. In the China Internet Conference & the First China Inclusive Finance Conference held on July 22, 2015, PPDAI was awarded the Excellent Case of “Internet+” Inclusive Finance of 201522 for its persistence in pure online individual mortgage-free microcredit services and practice of inclusive finance. PPDAI’s efforts on providing inclusive financial services were fully recognized.

3.2.2 Educate Investors The ecosystem of P2P online lending in China mainly involves four major participants: regulator, platform, borrower and investor. Among them, investors are unprofessional individuals. Individual investors in 22 China

Internet Conference Awarded PPDAI with Excellent Case Prize for Its 8-year Practice of Inclusive Finance, (2015-07-23) [2017-12-05], http://news.163.com/15/0723/17.AV7MR4Q10 0014AED.html.

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China are mostly exposed to a strong sense of speculation and poor risk awareness. PPDAI has put great efforts to increase investor’s risk awareness and educates investors to make rational investments. According to PPDAI CEO Zhang Jun, “We adheres to no guarantee to attract ‘smart money’. ‘Smart money’ can draw on the advantages and avoid disadvantages, do the correct things and benefit the company, industry and economy while earning profits.” PPDAI takes this opportunity to advocate a market mechanism, which educates investors to learn risk control and strictly controls the platform’s overall risks. For example, PPDAI launched a principal security program on July 4, 2011, which rewarded investors who had successfully made investments for over 50 times and invested no more than RMB 5000 (and smaller than 1/3 of the subject amount) for each loan (only compensating the part of the bad debt over the recovered loan). With this program, PPDAI educated and encouraged investors to make diversified small-amount investments. Diversification investing is the iron law in the investment community. Based on big data analysis, PPDAI found that when above conditions were satisfied, the investments would be diversified enough to minimize the losses. “Within over eight months after this principal security program was released, only four users have triggered the conditions”,23 which proves the achievements of the principle of diversified small-amount investments. According to Zhang Jun, compared to eight years ago when PPDAI assumed the task to popularize financial knowledge, now its mission has changed, that is to “cultivate users’ concept of diversified investments through the diversification investment conditions for principal security and make them smart and independent financial manager”. Figure 14 shows the single investment amounts of different platforms, which reflect the stronger sense of diversified investments of PPDAI investors. Investor education is a long-term, arduous task. When organizing the financial transaction activities of direct lending, P2P online lending platforms shall, on one hand, enable investors to understand the target of investment comprehensively and systematically and accurately master the 23 Subsequence

of the Principal Security Program, (2012-03-15) [2017-12-05], http://blog.sina. com.cn/s/blog_70f2dea10100y5ym.html.

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Yuan

PPDAI

Renrendai

Yooli.com

Hongling Capital

S-rong Online

Year

356 Yidai

Weidai

Renren Jucai

Gongshangdai

Wzdai

Xinxindai

LU.com

Hepai Online

Year

Fig. 14 Average single investment amount of 13 online lending platforms (Data source White Book of China’s P2P online lending Service Industry)

investment risk through complete information disclosure, and, on the other hand, advocate diversified investments of users and help users lower the risks and achieve self-protection. As a matter of fact, cultivating wise investors is more useful than any form of guarantee of the platform.

4

Support to the Persistence

As a lonely defender, PPDAI has gone through a tough time to practice the traditional P2P model in China. Its persistence has been supported by its pursuit of the ideal as well as the “hardware” foundation, i.e. the big data-based risk control system of PPDAI. PPDAI adhering to the pure online model suggests that it gives up verifying the authenticity of the borrower’s information offline. As there is no credible third-party credit information, offline verification is considered to be a good method to identify credit risks. However, PPDAI abandons this method considering the too high cost and personnel’s

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insufficient experience and adheres to the pure online model at the cost of slow expansion and relatively lower accuracy of risk identification. In the early stage of pure online operation, PPDAI offers the same credit line to all borrowers regardless of their credit ratings, i.e. RMB 3000 to the maximum for the first credit. After users’ credit information is accumulated for a period, the amount of loan release has gradually increased. At first, risk control prefers qualitative analysis, for example the credit of the married superior to the credit of the unmarried, and the credit of women superior to that of men. The Ministry of Public Security published data to society since 2009, and more open data can be acquired online. After having accumulated abundant data, PPDAI risk control adopts quantitative analysis. For quantitative analysis, the system can directly review and rate the credit and then determine the price and credit line on this basis. Users of low credit scores have lower credit line and higher interest rate. Quantitative risk control requires the support by big data and the corresponding credit risk control system. PPDAI adhering to the pure online model has been dedicated to the construction of big data-based risk control system. Through years of accumulation, “PPDAI has built its risk control system, made up by three subsystems, anti-fraud system, credit rating system and credit rating-based risk pricing system”.24

4.1

Big Data-Based Risk Control Ideas

In the loan transaction, the core of risk control is to identify and evaluate the borrower’s credit risk. In this regard, the basic idea of PPDAI (see Fig. 15) is to judge the borrower’s credit qualification based on some assumptions and then gradually improve the accuracy of assumptions and the model based on the data constantly accumulated. When a user defaults, the platform will collect as many dimensions of information as possible and assume that the borrowers with such characteristics are highrisk groups. Then, by accumulating the borrower’s information on loan release and repayment and examining the relation between the default 24 Hong

Ruoxin, Secrets of PPDAI Risk Control , (2014-08-19) [2017-12-05], http://www.yicai. com/news/4009094.html.

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Dynamic adjustment Data dimension Default

Data size Information acquisition

High risk assumption

Assumption verification Modelling

Fig. 15 Risk control ideas of PPDAI

and the borrower’s credit risk, the platform will further optimize the accuracy of the risk control model. The link mentioned above consists of three key parts. First, how many dimensions of the customer’s information can be acquired is largely associated with the level of information technology. Second, the data size is determined by the size of users. Only with users can the platform collect data and thus perform more accurate credit rating; and with more accurate credit rating, the platform can attract more users. This is a virtuous cycle, and the whole risk system is exposed to constant optimization in the dynamic adjustments of the virtuous cycle. The final key part is the analysis method, that is to establish the risk evaluation model to analyze the users’ credit status.

4.2

Source of Big Data

Big data constitute the foundation of PPDAI risk control system. Only with big data can the platform give full play to its top capability of data mining and analysis based on the first-class algorithm and enjoy the risk control advantage based on big data. PPDAI’s data are mainly provided by users or collected by the platform upon the users’ authorization. When submitting the application for borrowing on the PPDAI platform, the user shall provide the registration information and authorize the platform to search for the user’s personal information. PPDAI cooperates with a dozen of authoritative data centers nationwide including the ID card information query center of the Ministry of Public Security, industrial and commercial bureau and court, and also partner with

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e-commerce platforms such as Taobao and DHgate to share users’ information. Furthermore, PPDAI also utilizes the search engine to collect the fragmented information of the borrower scattered on the Internet, including the borrower’s network behavior trajectory, social network behavior and consumption record etc. To be specific, such information includes the borrower’s speeches at the BBS, number of Weibo followers, Weibo content, the accounts the borrower follows, the borrower’s habit to log in PPDAI website and the speed to fill in information at the registration etc. At present, more than 400 pieces of information on average, over 2000 at the most, are referred to for each borrower. During credit review, PPDAI will first verify the name and identity information of the borrower through the authoritative data centers and then utilize multidimensional information to examine and evaluate the credit based on the self-constructed risk control model. To accumulate a great amount of data, PPDAI attaches great importance to the expansion of the platform’s user size for the sake of data analysis. By June 30, 2015, PPDAI had accumulated 6.42 million customers, making it the platform of the largest user base in the whole industry.

4.3

Big Data-Based Risk Control System

With regard to the modelling method, PPDAI started from case analysis, then developed to regression analysis, and has finally established 18 models based on population segmentation. The segment models can be used to evaluate the credit of any kind of borrowers. According to PPDAI, this model shows a quite high accuracy. The low bad debt rate 1.7% of PPDAI platform is mainly attributed to this model. Figure 16 shows that PPDAI has established an effective risk control system, which significantly lowers the overdue rate of the platform. PPDAI officially published the risk control system “Magic Mirror Risk Control System”25 on March 24, 2015. According to PPDAI, the magic mirror risk control system leads the whole industry in three respects. 25The

First Chinese P2P Enterprise PPDAI Published “Magic Mirror” Big Data Risk Control System, (2015-03-25) [2017-12-05], http://stock.sohu.com/20150325/n410269190.shtaml.

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1st quarter 2015 Year

Fig. 16 Overdue rates of PPDAI platform (Data source PPDAI Report of the 1st quarter 2015)

It is the first in the industry that centers on the big data risk control model; the first in the industry that can forecast the default rate of each borrowing; and the first in the industry that determines the price based on risk evaluation. The magic mirror risk control system was developed based on 4 billion pieces of data based on 6 million registered users (including 15% active users). PPDAI updates and records over 2000 fields of information for each borrower in real time, screens the information and extracts the information dimensions and variables that can predict a person’s credit and finally generates the risk rating of each target. Based on big data technology, the system can offer the risk rating and predicted overdue rate for each borrowing and complete risk-based pricing according to the risk rating, to assure the return corresponding to the risk. As proved by months of test, the predictions have been basically consistent with the reality by now, and no reality goes beyond the scope of prediction (see Fig. 17). With the increase of users, more data of more dimensions can be utilized to improve the magic mirror risk control system, which can offer more accurate predictions and be more extensively applied. As a matter of fact, the platform was “forced” to develop the magic mirror risk control system. If there is a credible third-party credit rating system and credit

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Actual overdue rate

Predicted overdue rate

Fig. 17 Comparison between the actual overdue rates and predictions of the magic mirror risk control system (Data source PPDAI Report of the 1st quarter 2015)

services in China, the P2P online lending platforms that have struggled all along could spend more manpower and material resources to do what a platform should do and do a better job in P2P online lending. The fact that P2P online lending companies such as PPDAI have independently developed excellent risk evaluation systems is the glory of these companies, but also the sorrow of microfinance and inclusive finance undertakings. From the perspective of the whole society, the backward social credit system has increased the operating cost of microfinance and wasted social resources.

5

The Future of Persistence: Where Should PPDAI Go?

After eight years of persistence, all sufferings of the lonely defender PPDAI finally have their reward. However, in the fierce competition, where should this idealistic defender go? This question deserves our attention.

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249

Accumulate Richly and Break Forth Vastly

Despite the slow development of PPDAI in previous eight years, the transaction volume of PPDAI has rapidly expanded since 2015. According to the latest data of PPDAI, its sales volume has presented an exponential growth since 2015, with the monthly transaction volume increasing from RMB 200 million to 300 million in shortly 3 months. The monthly transaction volume in June exceeded RMB 300 million for the first time, setting a new record and having grown by 244% compared to the same period in the last year and by 24.56% compared to the previous month. The monthly growth rate far exceeded 8.19%, the monthly growth rate of the whole industry, and was also higher than the growth rates of Hongling Capital, Xinhehui and Ppmoney (respectively −6%, 20% and 2.8%).26 PPDAI is just starting to reap the rewards of long-term persistence. Remarkably, “The number of borrowing applications to PPDAI platform ranks the first in the industry. PPDAI received 1.43 million borrowing applications in the first half of the year and approved 250 thousand loans (i.e. successfully released one loan every minute), having grown by 248.6% compared to the same period of the last year. The number of loans released per month of PPDAI in the first half almost equaled to the sum of the top 3 platforms in the transaction volume rank of the industry.”27 (See Fig. 18) This highly efficient platform that can transact hundreds of thousands of borrowing applications is jointly supported by the powerful automatic operation system, magic mirror risk control system and accurate risk-based pricing system of PPDAI. The situation of PPDAI in June 2015 initially proved the capability and efficiency of the risk control system developed by PPDAI. “As to another data that lead the industry, PPDAI attracted over 230 thousand borrowers in the first half of 2015, accounting for 22% of the whole

26 Subsequence

of the Principal Security Program, (2012-03-15) [2017-12-05], http://blog.sina. com.cn/s/blog_70f2dea10100y5ym.html. 27 Semi-annual Report of PPDAI, (2015-08-19) [2017-12-05], http://www.ppdai.com/help/Med iaReports187.

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Loans

PPDAI

Transaction volumes of Top 3 platforms of the industry

Year

Fig. 18 Number of loans released per month by PPDAI in 2015 almost equaled to the sum of top 3 platforms of the industry (Data source Semi-annual Report of PPDAI, Home to Online Lending)

industry’s borrowers.”28 In addition, nearly half of the 60,000 loans matched in June were contributed by new customers, which demonstrated the achievements of PPDAI’s persistence in developing customers online. PPDAI has also released diversified financial products (rainbow financial program and quick investment instrument) to attract investors. The rainbow financial program issues tenders three times a week, and users can reserve the investment programs. The earning rate gradually increases with the term of investment. Each time the asset packages valuing about RMB 5 million are rapidly sold out within a few minutes. The quick investment instrument helps users diversify their investments efficiently. The investment foundation of investors will automatically diversify the investments into hundreds of tenders based on a certain algorithm. Based on the investment concept of diversified investments and the investment products constantly released, the online model without advance payment of PPDAI has become a symbol of sound investment and attracted more investors to the online lending market. 28 Semi-annual

iaReports187.

Report of PPDAI , (2015-08-19) [2017-12-05], http://www.ppdai.com/help/Med

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Internet Finance Group

We find that PPDAI, which runs a traditional P2P online lending platform adhering to no advance payment and the pure online model, is designing a blueprint beyond a pure P2P online lending platform for itself. PPDAI goes beyond the single P2P online lending business, and intends to develop credit, asset production and wealth management business etc. and gradually build itself into an Internet finance group. In the respect of assets, PPDAI plans to develop more borrowing products and serve loan users based on the application of consumption scenarios. For wealth management, PPDAI intends to satisfy the demands of online wealth management customers with more diversified asset allocation productions and perfect experience based on personal credit. As to big data credit, PPDAI has separated this system to independently offer credit services to society, thus to give better play to this system. The long-term planning of PPDAI may be a necessary result of its persistence. No matter how the business model changes, PPDAI always sticks to two core values, providing financial services based on the Internet and big data; and offering financial services in accordance with laws and regulations. Both core values constitute the foundation for PPDAI to achieve value increment.

Case Comments PPDAI is the only P2P online lending platform that has adhered to the traditional operation model (as an information intermediary) in eight years in China. In view of the seriously backward social credit information system and the severe lack of financial regulation in China, we can imagine how difficult it is to persist in the original form of P2P online lending. As the first platform that introduced P2P online lending to China, PPDAI has struggled hard to adhere to the traditional model when seeing the latecomers easily surpassing itself through model innovation.

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Following two conclusions can be drawn from the case of PPDAI: 1. The construction of the social credit information system is the lifeblood of the healthy development of P2P online lending platforms in China As defined in The Guiding Opinions on Promoting the Healthy Development of Internet Finance officially released by the ten ministries and commissions, the P2P online lending platform shall be an information intermediary that matches the borrower and the investor for personal direct lending. However, if the information intermediary is in serious shortage of information, the financial transactions it brings about are merely speculations rather than investment transactions. The most direct source of information is the credible third-party credit information agencies. American P2P online lending platforms develop rapidly because they can easily find the borrowers’ credit records. Their advanced social credit information system and the government’s public record information disclosure system assures that they can conveniently verify the information. In this sense, the social credit information service system and the disclosure system including the social credit information system and public record information et. constitute the infrastructure for the development of P2P online lending industry. However, this important infrastructure is almost non-existent in China. The development of China’s social credit information system is lagging far behind. After a decade of development, the People’s Bank of China has merely gathered the credit information of the commercial banks under its jurisdiction. It is the only national credit information center, but it is subject to poor information, small coverage and weak service awareness. As the subordinate organization to the state’s top financial regulatory authority, the credit information center has no clear idea about whether it is a government regulator or a credit service agency. As a consequence of such confusion, P2P online lending platforms cannot acquire credit services from the credit information center (rejected by the center) and have to find a way out on their own: they step on the red line and offer advance payment or guarantee to investors to cover the investment risks incurred by insufficient information; or they develop their credit systems slowly and bear the corresponding cost.

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Hence we can consider that massive violations that cross the policy red line in P2P platform business models in China largely result from the severely backward construction of social credit information system and the low efficiency of the credit information system led by the Chinese government. The P2P online lending industry pays a heavy price for the severely backward construction of the credit information system in China, i.e. the increase of the industrial cost and the industrial risk. If no major improvement of social credit information services, this industry that concentrates on microfinance sector and is dedicated to providing convenient financial services to small and micro enterprises and individuals will encounter subsistence crisis. In this sense, the social credit information system construction is considered as the lifeblood of P2P online lending platforms. Only by facilitating the construction of the social credit information system can the healthy development of the P2P online lending industry be guaranteed. 2. The legal and regulatory improvement constitute the guarantee for the healthy development of P2P online lending platforms In the eight years between 2007 when the first P2P online lending platform emerged in China to July 2015, the P2P online lending industry which is directly associated with money, investment and financing has been always exposed to legislative and regulatory vacuum. This is a very dangerous phenomenon. Finance without regulation will grow savagely and finally lead to bubble burst as well as financial and economic upheaval. Though the P2P online lending industry has not come to this yet, it has been developing in a dangerous environment. This industry has good and bad mixed together due to no regulation or punishment system, as anyone can easily register and operate a platform and abscond with investors’ money (so-called run-away). By the end of July 2017, the accumulated number of problematic platforms in China had reached 895, including 107 problematic platforms newly added in July. Among the problematic platforms, the platforms running away accounted for

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57.80%29 in that month. Under these circumstances, the public associate P2P online lending with fraud and run-away. Like all other sectors, legislation and regulation always lag behind the birth of new things. The regulatory authorities in China have not released laws and regulations soon because they need time to cautiously understand and observe Internet finance. However, the legislative and regulatory vacuum has greatly harmed the pioneers and entrepreneurs who really intend to do P2P online lending well as well as the adherent who sticks not to touching the policy red line such as PPDAI. First, the P2P online lending industry faces tremendous policy risks due to legislative and regulatory vacuum, since uncertain policies will induce short-sighted actions and speculations of practitioners; and second, the law-abiding companies suffer losses due to legislation and policy vacuum, and bad money will drive out good in the long run. Fortunately, the government’s industry guiding opinions have finally arrived after eight years of waiting, bringing hope to law-abiding companies such as PPDAI. While feeling relieved, we still regret that it takes so long a time for the government to observe an innovative financial phenomenon before establishing corresponding laws and rules. The efficiency is too low. In other countries who saw the rise of the industry at the same time such as the US, the explicit regulatory laws and rules were released within two years after the first P2P online lending platform emerged, compared to eight years spent in China. Does it really need such a long time? In the age of mass entrepreneurship and innovation, the low efficiency of the construction of the legislator and regulatory system may hinder social progress. Commentator: Yan Li

29 Monthly

Report on Online Lending Platform Number and Popularity in July 2015, (2015-08-01) [2017-12-05], http://bbs.wangdaizhijia.com/thread-552826-1-1.html.

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Reference Freedman S, Jin G Z. Do Social Networks Solve Information Problems for Peerto-peer Lending? Evidence from Prosper.com. Working Papers. University of Maryland, 2008.

7 “Digital Finance + Insurance + Leading Enterprise + E-Commerce”: Rural Inclusive Finance Case of Ant Financial Yingxin Zhang

1

Mongolian Sheep

For several consecutive years, the No. 1 central documents have pointed out that we should support new agricultural business entities and new agricultural service entities as the backbone of modern agriculture construction and advance agricultural modernization and large-scale operation in China. However, rural economy’s financial demand cannot be effectively satisfied by traditional finance due to the characteristics of rural economy. Rural supply chain is regarded as an effective organizational form to promote the realization of agricultural modernization in China. The rural supply chain finance that supports rural supply chain undoubtedly becomes an important means to promote agricultural largescale operation and modernization (Hu and Huang 2008; Song et al. 2012; Hu and Zheng 2013). On December 31, 2015, the State Council Y. Zhang (B) Renmin University of China, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_7

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published the Opinions on Implementing the New Concept of Development, Facilitating Agricultural Modernization and Achieving the Goal of Well -off Society in an All -round Way, which required supporting the innovation of agricultural business models and actively promoting the innovation of financial products, interest rate, term, limit, process and risk control etc., in order to further satisfy the financial demands of new agricultural business entities including family farms, large and specialized farmers, farmers’ cooperatives and agricultural leading enterprises. The government encourages financial institutions to solve obstacles through product innovation, model innovation and technical innovation to achieve real inclusive finance. At present, the new financial entities with e-commerce background, represented by Ant Financials under Alibaba Group, rely on their own superiority to actively involve the rural finance industry and has accumulated some experience. This case studies the development of Mongolian Sheep (the core enterprise in the supply chain), focuses on the capital demand of Mongolian Sheep farmers and combines the “digital finance + insurance + leading enterprise + e-commerce” cooperative model of Mongolian Sheep, Ant Financials and CIC Property, to discuss the value and significance of e-commerce in rural supply chain finance.

1.1

Strategic Development of Mongolian Sheep

1.1.1 About Mongolian Sheep Founded on May 3, 2012, with total equity of RMB 310 million, Mongolian Sheep is a modern, green, whole-industrial-chain company that integrates “ecological grassland construction, organic seed and forage processing, breeding of improved mutton sheep, large-scale base breeding, comprehensive animal husbandry, live animal trading center, production and deep processing and national terminal sales”. Based on the golden animal husbandry belt ranging between 36° and 49° north latitude, Mongolian Sheep has built seven major industrial base groups from west to east and, depending on the large-scale, standard and industrialized development model and the brand new Internet thinking, has become the key leading enterprise in the industrialization of agriculture

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and animal husbandry and the leading development enterprise in poverty alleviation of Inner Mongolia Autonomous Region, a China space affiliate and a standing council member of China Meat Association.1 In 2014, Mongolian Sheep obtained a sales income over RMB 1.2 billion, slaughtered more than 650,000 sheep and earned a net profit over RMB 40 million, showing a significant growth compared to 2013. In 2015, It slaughtered more than 1.15 million sheep, produced over 13,000 tons of refined and segmented mutton products, exceeded a sales volume of RMB 1.8 billion and acquired a profit over RMB 70 million, ranking first in the industry in terms of production and sales volume for three consecutive years. Figure 1 describes the operating income and assets of Mongolian Sheep between 2012 and 2014. After two years of preparation, Mongolian Sheep has started its rapid growth since 2014 and seen a sharp rise in operating income, which was 1.7 times of the figure in 2013. It has also realized the rapid development of total assets and net assets by means of resource allocation and merger. RMB ten million

Operating income

Total assets

Net assets

RMB hundred million

Fig. 1 Operating income and assets of Mongolian Sheep (Note The line chart in the figure corresponds to the coordinate axis on the left and the bar chart corresponds to that on the right)

1 Introduction

on the official website of Mongolian Sheep, www.mengyang.com.cn.

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1.1.2 Green Whole-Industrial Chain Operation Model of Mongolian Sheep Mongolian Sheep has been leading the new upgrading of agriculture and animal husbandry based on the idea of industrialized development. Since its foundation in 2012, Mongolian Sheep has planned for and completed the construction of whole-industrial-chain operation—“reproduction, breeding, trading, processing, marketing” (see Fig. 2). Reproduction: Mongolian Sheep raises high-quality breeding sheep for reproduction and combines large-scale planting and processing of organic feeds and forage, thus to assure the sheep quality at source. Breeding: Mongolian Sheep breeds sheep in a standard, intensive and large-scale manner and applies the “five standards” system2 and “six uniform standards”3 to guarantee scientific and standard mutton sheep breeding. Moreover, Mongolian Sheep has also established the “sunshine pasture” to create Based on the golden animal husbandry belt ranging between 36° – 49° north latitude, raise high-quality breeding sheep for reproduction to assure the sheep quality at source

With large-scale meadow construction and organic feed and forage processing, Mongolian Sheep offers good treatment to each sheep

Keep a foothold in Inner Mongolia and spread the influence to every corner of China based on Internet thinking + shops + strategic customers and traditional channels

Create the sunshine pasture and comprehensive animal husbandry innovatively that adheres to zero pollution, zero radiation and scientific breeding

Introduce international advanced production lines and standards (GMP) for production to guarantee the safety and nutrition of products

Fig. 2 Whole-industrial-chain operation model of Mongolian Sheep

2The five-standards system refers to the system of reproduction, epidemic prevention, breeding, safety management and breeding sheep farm standards. 3The six uniform standards consist of the uniform breed improvement, uniform feed standard, uniform technical services, uniform feeding standard, uniform epidemic prevention system and uniform slaughter standard.

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the comprehensive circular economic system. Trading: Mongolian Sheep has constructed the live animal trading center as a convenient trading channel and platform for farmers and herdsmen and built the first electronic information trading platform serving farmers and herdsmen in China, Liuchu Wangwang, by which mutton cooperatives and mutton sheep farmers from several prefecture-level cities can enjoy the services of market information query and transaction etc. By doing this, Mongolian Sheep helps farmers and herdsmen explore the market and find business opportunities. Processing: Mongolian Sheep applies international advanced technology and equipment for standard deep-processing and has led the mutton sheep industry to initiate GMP standard production specification. It applies the high production safety standards in pharmaceutical industry to the whole production process of Mongolian Sheep products and guarantees product quality through every strict procedure. The present rich varieties of Mongolian Sheep products can satisfy different consumer demands. Marketing: With the sales network covering the whole country, Mongolian Sheep adopts the online and offline interactive marketing. By now, Mongolian Sheep has been stationed in over 2,000 large supermarkets nationwide and established cooperation with about a hundred famous restaurant chain enterprises in China. In the meantime, Mongolian Sheep’s products are also sold in famous e-commerce platforms including Tmall, JD and WeChat Mall. In the early stage of development, both the front end and back end of Mongolian Sheep’s industrial chain are exposed to decentralized development. At that time, Mongolian Sheep depended on the market to buy back sheep from retail farmers like casting nets, slaughtered the sheep and created various products, and then sold them to the terminal markets. Thereafter, Mongolian Sheep has followed the development direction of increasing the proportion of self-raised sheep, industrialized its front-end business, established its own demonstration breeding bases, assigned the task of breeding to cooperatives (large farmers) and finally formed the cooperation model of “company + base + cooperative”. Self-raised sheep meet the demand of enterprise survival and development and can effectively prevent the occurrence of adverse incidents like Sanlu supplied toxic milk to Yili and Mengniu and Shanghai Hust Food supplied expired inferior milk to KFC etc. In order to really

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control the sheep supply at source and establish the safe and controllable sheep supply guarantee system, Mongolian Sheep has proposed the “six uniform standards” and rendered the whole set of improved varieties breeding and veterinary services, to enable the perfect upstream links for the production and processing of high-quality mutton products. Though food safety is incorporated in every link of the industrial chain, any issue in breeding will ruin the food safety of production and processing.

1.1.3 Strategic Planning of Mongolian Sheep In the future, Mongolian Sheep will continue to expand its production size and influence, promote its brand development and contribute to the expansion and strengthening of Chinese mutton enterprises and the improvement of herdsmen and common people’s life. Figure 3 presents the Mongolian Sheep’s sales income plan in the next 3–5 years, which is expected to start development since 2016 and reach a sales income of RMB 10 billion by 2019. “Expansion” is the only way which must be passed before the realization of the future strategic planning. However, before expansion, “stability” is the necessary requirement, i.e. how to acquire the stable source of sheep every year to guarantee the continuity of enterprise production. This goal can be realized by establishing long-term partnership with farmers. During the long-term repeated games between both parties, if the farmers seek nothing but profits and break the previous

Year of development Goal: RMB 10 billion Year of development Goal: RMB 7.5 billion Year of development Goal: RMB 5 billion Year of development Goal: RMB 3 billion Year of breakthrough Goal: RMB 1.5 billion

Fig. 3 Strategic planning of Mongolian Sheep in next 3–5 years

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partnership by selling sheep to a buyer at a higher price, the farmers will expose their own character and have a bad reputation. In this case, Mongolian Sheep will not continue its cooperation with the farmers in sheep breeding, provide any service or buy the flattened sheep, cutting off the important sales channel of these farmers and herdsmen and causing great economic losses on them. Moreover, as a valuable intangible asset, reputation can impede the economic subjects’ shortsighted actions. For economic subjects who have accumulated high reputation, the present value of reputation rent is so high that it impels economic subjects to maintain the partnership, thus to maintain their own reputation. Hence, according to the analysis from the reputation perspective, we believe that the stable and sound partnership established between Mongolian Sheep and upstream farmers will help Mongolian Sheep acquire the stable source of sheep. Currently Mongolian Sheep is cooperating with 54 partners for flattening sheep breeding, among which 32 are cooperatives, accounting for 60% of the total number. These 54 partners have cooperated with Mongolian Sheep for 2–3 years on average, out of which 5 have an annual amount of sheep slaughter between 10,000 and 15,000, 15 between 15,000 and 20,000 and 34 above 20,000. After having realized “stability”, now comes to the problem of how to “expand”. For Mongolian Sheep which seeks rapid development, its focus is how to expand the source of sheep and thus improve the scale of production and processing. At present Mongolian Sheep mainly acquires sheep from two channels, the cooperative farmers and retail farmers in market. As the former channel takes up 85% now and makes the source controllable, Mongolian Sheep will strive to acquire sheep totally depending on cooperative farmers in the future. In this respect, the only solution for Mongolian Sheep to expand its source of sheep is to drive cooperative farmers to expand their breeding scale. Following problems must be addressed to expand the breeding scale: First, farmers’ backward breeding technology and weak sense of disease prevention and control leads to the low output. Second, as the farmers cannot access to marketing information timely and are vulnerable to the fluctuation of mutton sheep price, so their incomes cannot be guaranteed. In other words, the problem is whether their mutton sheep can be successfully sold after size expansion. Third, farmers are faced with

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financial difficulty, which results in a serious shortage of start-up and operation capital for breeding. Against the first problem, Mongolian Sheep provides breeding technology services and offer farmers with the whole set of veterinary services for free, to tackle their technical issues. In the respect of market, after the mutton sheep reach certain standards after breeding, Mongolian Sheep will buy all the mutton sheep at a “protection price” that protects farmers’ interests as determined based on the market price of the year. In this way, the losses incurred on farmers by market fluctuation can be lowered. The slaughter price for live sheep offered by Mongolian Sheep to partners is higher than the market price by 0.8–1 yuan/500 g. The last is the problem of no large-scale financial support. Farmers cannot acquire enough financial support since sheep breeding is relatively special and the farmers’ credit conditions fail to meet the banks’ loan requirements. To be specific, “hairy things are not really wealth”. Sheep, as live animals, cannot be applied as a collateral for guarantee. Moreover, they may be exposed to a high possibility of accidents during the breeding. For example, the epidemic, natural disaster and unforeseen circumstance may lead to their mass death, therefore influencing the fund repayment. Furthermore, most farmers can hardly meet the banks’ requirements on the value of mortgage assets, so they suffer the difficulty in and high cost of financing. The investors that provide money shall control risks while supporting the development of real companies, but the current status and operation mechanism of sheep industry do not fulfill the requirements of any traditional risk control framework of investors. Mongolian Sheep has been thinking about how to solve farmers’ capital demand. Only by solving the farmers’ demand can Mongolian Sheep develop in a healthy manner. Currently, Mongolian Sheep has gradually formed a “Sheep Union” model, i.e. the “farmers and herdsmen + cooperative + company + bank + guarantee + insurance” cooperation model. Mongolian Sheep designed the “Sheep Union” model to make credit match between farmers and capital and solve the farmers’ problem of credit enhancement.

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1.2

265

“Sheep Union” Model of Mongolian Sheep

1.2.1 Development Course Based on the original model of “company + base + cooperative”, Mongolian Sheep also introduces the factors of bank, guarantee and insurance in the model (see Fig. 4), aiming to solve the farmers’ financial issues. Each factor corresponds to the investor’s every concern about fund safety. To help Mongolian Sheep farmers successfully acquire credit resources, two tasks shall be completed, increasing farmers’ credit and lowering farmers’ business risks.

Agriculturesupporting bank

Loan Repay principal and interest for farmers

Lamb order

Guarantee

Specialized farmers Guarantee Insurance

Mongolian Sheep

Breeding sheep and seed supply

Xida Guarantee

100% holding

Fig. 4 “Sheep Union” model of Mongolian Sheep

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(1) Increase Farmers’ Credit Farmers can coordinate their resources to meet the bank’s financing threshold, and meanwhile the entities associated with farmers’ business such as the government and leading enterprise can also coordinate their own resources to increase farmers’ credit. In 2013, Mongolian Sheep raised RMB 50 million to found a wholly-owned subsidiary, Hohhot Xida Agriculture and Animal Husbandry Financing and Guarantee Company (hereinafter referred to as “Xida Guarantee”), to increase farmers and herdsmen’s income and expand and strengthen their business. Xida Guarantee is the smallest guarantee company of Inner Mongolia Autonomous Region and only provides guarantee for farmers cooperating with the upstream industrial chain of Mongolian Sheep to buy sheep and materials. For further expansion and development, Xida Guarantee increased its registered capital to RMB 100 million in 2014. From January 1, 2014 to July 30, 2016, Xida Guarantee had offered 133 guarantees in total, valuing RMB 566 million, and its balance under guarantee reaches RMB 277 million. Since its founding until now, Xida Guarantee has achieved a guarantee compensation rate of 0%, a compensation recovery rate of 0% and guarantee loss rate of 0%. With regard to the guarantee fee, general commercial guarantee organizations charge 2–3%, and government platforms collect 1–1.5% to the minimum. By comparison, farmers do not need to pay any guarantee fee if they sell the slaughtered sheep to Mongolian Sheep, otherwise they shall pay 1%. The fact is that farmers all choose to fulfill their obligations, or they cannot acquire credit resources as Mongolian Sheep will not provide guarantee in next financing. The original intention of Mongolian Sheep to establish the guarantee company is to cultivate its upstream breeding chain and safeguard its own development. In addition to creating conditions to increase farmers’ credit, Mongolian Sheep also seeks help from other partners. At present, Inner Mongolian Jincheng Guarantee Company under Inner Mongolia Finance Group also offers guarantee to farmers.

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(2) Lower Business Risks Investors including banks also face two other risks: the safety of capital settlement, i.e. how to ascertain the capital flow; and the risk of the special live animal breeding industry, i.e. how to assure the safety of biological assets and resist the risks arising from live animal breeding. First, Mongolian Sheep solves the settlement safety issue through business bundling with farmers. To be specific, when Mongolian Sheep recommends a customer to the bank, the bank will carry out risk evaluation as per loan review requirements. If the conditions for loan release are met, the ban will transfer the fund to the farmer’s bank card. However, the fund can only be used to purchase sheep and relevant feeds and forage, which are all provided by Mongolian Sheep. Finally, when taking back sheep and making settlement, Mongolian Sheep will pay the part of profit over the loan amount to the farmer and settle with the bank on behalf of the farmer. In this closed capital flow, the fund released by the bank changes into the means of production, and after sheep breeding, it finally changes into money again through settlement. All cash flow is completed within the enterprise, and the bank can achieve risk control simply by monitoring and controlling the cash flow of the enterprise. In this whole process, the fund has not been passed to the farmer at all, so the issue of capital flow is solved. Second, Mongolian Sheep introduces insurance to respond to breeding risks. Mongolian Sheep cooperates with CIC Property and insures the sheep, making it the first enterprise that purchases insurance for mutton sheep in Inner Mongolia. At present, commercial insurance for pigs and cows have matured, and the government offers special budget to subsidize insurance companies when they make compensation for such insurance. However, the sheep breeding insurance has not been incorporated in the agricultural basic insurance subsidized by the government, because compared to pork processing and sales, mutton processing and sales are exposed to lower industrialization and lower level of scaled operation, reaching only 68.2%. Since no enterprise has allocated resources for this purpose before, Mongolian Sheep has met a series of setbacks when advancing mutton sheep breeding insurance. However, the risk during the breeding has been a key risk to the bank, as any risk

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during the breeding will result in systematic risks of the whole industrial chain and strike the enterprise and bank with a deadly blow. After insurance is introduced, farmers can mitigate the risks by 80–90% in case of an outburst of epidemic or a natural disaster. With the smaller risk exposure, farmers are more willing to expand the scale of breeding. At present, farmers only need to pay a guarantee fee of RMB 10 for each mutton sheep, but the insurance compensation for each mutton sheep can reach RMB 500. In this sense, insurance is really a reassurance that farmers depend on. Newly introduced insurance not only stabilizes the market, but also determines the price of sheep indirectly, makes sheep an asset that can be mortgaged and realizes the transition from resource capital to industrial capital. Moreover, insurance also protects consumers’ best interests. In the past, the dead sheep and sick sheep would flow to the market after being skinned and disrupt the market trading order. Now insurance can purify the market effectively and automatically. In a word, insurance not only lowers farmers’ risks, guarantees farmers’ incomes and protects consumers’ food safety, but also reduces the risks of the whole industrial chain. Currently, Mongolian Sheep is actively pushing forward the Party committee and government of the Autonomous Region to incorporate mutton sheep insurance into the agricultural basic insurance subsidized by the government. N.B.C. (General Office of the Ministry of Agriculture and the Ministry of Finance) [2016] No. 12 document, Circular of the General Office of the Ministry of Agriculture on Pilot Service Innovation of Financial Support for Agriculture in 2016 , approved the application of CIC Property for agricultural subsidy to “Internet + credit + insurance + cattle and sheep breeding program” in Hulun Buir. We believe this subsidy will soon cover the whole Inner Mongolia area.

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1.2.2 Analysis of “Sheep Union” Model Centering on farmers’ demands, Mongolian Sheep has created the unique “Sheep Union” model,4 which integrates all resources that seem irrelevant in one platform. This new model not only fundamentally solves farmers’ difficulty in and high cost of financing, but also brings new opportunities to Mongolian Sheep. First, the “Sheep Union” provides farmers and herdsmen with guarantee and low-cost financial support. In the name of “Sheep Union”, Mongolian Sheep negotiates with the bank on behalf of a large number of farmers upstream instead of a single farmer, so the interest rate of the loan can be reduced to a great extent. The previous nominal interest rate of the farmers’ loans was around 12%, in addition to the implicit cost. By comparison, the direct financing cost of “Sheep Union” farmers is now lower than 7%, having declined by approximately 50%. In this way, farmers can enjoy the real benefits. Second, it offers farmers and herdsmen with breeding technology services. Third, it solves the issue of no stable market for farmers and herdsmen. Mongolian Sheep provides all means of production needed in the beginning, offers the whole set of technical services during breeding to overcome technical difficulties of mutton sheep breeding, and buys back the flattened sheep at the protective price to dispel farmers’ worries about sales. Therefore, farmers are more like “workers” who assume the job of raisin sheep and need not

4 In

the “Sheep Union” model, farmers can cooperate with Mongolian Sheep in diversified modes like cooperative breeding and giving ewes to recover lambs. The company will evaluate the interested partners’ breeding capability, site, experience and assets available for mortgage etc. before deciding whether to cooperate. In the cooperative breeding model, the company will help the partner acquire the bank loan for sheep purchase and then give the lambs up to the contract standards to the partner for breeding. When the sheep have met certain standards after breeding, the company will recover the sheep at the protective price. Upon the completion of the breeding task, the company will repay the principal and interest and return the remaining profits to the partner. In the model of giving ewes to recover lambs, the company supplies the partner with ewes and breeding rams that comply with contract standards and have a value consistent with the loan amount. The partner only needs to return lambs as per the standards and quantity agreed to Mongolian Sheep within three years. After this task is completed, the company will repay the loan principal and interest to the bank on behalf of the partner, and the ewes given by the company and the part of lambs outside the specified task will belong to the partner.

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worry about things outside the work. The status of worry-free production and worry-free sales conforms to the concept of new farmer business entity. The “Sheep Union” model increases farmers’ incomes, and meanwhile stabilize and expand the source of sheep indirectly for Mongolian Sheep. This model helps Mongolian Sheep stabilize its partnership with farmers, changes their loose ties of transactions to a community of interests of interest and risk sharing and therefore prevents the possibility of huge swings of the sheep supply. Moreover, with the “Sheep Union” model, Mongolian Sheep utilizes the financial leverage to develop more farmers, expand their breeding scale and further extend the sheep supply scope, which facilitates the large-scale operation and brand development of the mutton sheep industry. Jimusi, a herdsman from Haorao Chaidamu Gacha Village, Xini Town, Hangjin Banner, who had been awarded as the National Model Worker of 2015, is a member of the innovative “Sheep Union” model of Mongolian Sheep.5 Jimusi signed the mutton sheep breeding cooperation contract with Mongolian Sheep in 2013. With the help of Mongolian Sheep, her base can raise 10,000 mutton sheep in total and slaughter 30,000 sheep per year. By December 2014, the 80,000–100,000 sheep slaughtered each month of Mongolian Sheep have been all provided by the farmers of the “Sheep Union”. Meanwhile, the “Sheep Union” model avoids selling goods on credit and improves the liquidity of Mongolian Sheep. Selling on credit is a commonly seen problem in the breeding industry. As farmers can hardly acquire loans and are short of money, core enterprises have to sell the means of production to farmers on credit to assure the stability of upstream farmers. Selling on credit is exposed to high risks and restricts the liquidity of the enterprise. Since its founding in 2013, the “Sheep Union” model has served 94 farmers (including repeated loans). It has released loans totaling RMB 370.42 million, 3.94 million for each farmer on average, which have all been due and timely repaid without any default. “Sheep Union” cooperates with Baoshang Bank, Bank of China, Bank of Inner Mongolia, 5 Mongolian

Sheep “Sheep Union” Model Increases Farmers and Herdsmen’s Incomes (27 May 2015) [31 January 2018], http://www.sohu.com/a/16616400_115402.

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Jingu Bank, China CITIC Bank, HeTao Commercial Bank and Haier Microfinance, respectively guaranteed by Xida Guarantee Company or Jincheng Guarantee Company.

2

“Digital Finance + Insurance + Leading Enterprise + E-Commerce” Model

In addition to the excellent resource endowments of Mongolian Sheep, “Internet+” also brings brand new development opportunities for Mongolian Sheep to rapidly penetrate the national market, and meanwhile enables more consumers to understand Mongolian Sheep and conveniently purchase commodities. Mongolian Sheep has successfully entered several e-commerce platforms including Tmall. Consumers can enjoy delicious food at home. In Juhuasuan activity of Alibaba between October 19 and 22, 2015, “the transaction volume of Mongolian Sheep’s fresh mutton products reached RMB 3.57 million, setting a record of the sales volume in a single activity among national fresh mutton e-commerce”.6 The cooperation between Mongolian Sheep and Tmall marked the beginning of a series of deep cooperation between Mongolian Sheep and “Alibaba” line. The most important cooperation was Ant Financial joining the supply chain of Mongolian Sheep in February 2016 and becoming the provider of financial services. In the e-commerce + supply chain finance, Ant Financial works together with the core enterprise Mongolian Sheep and CIC Property to offer loans to mutton sheep farmers. By now, this program is in a trial period as its design and multi-party collaboration have been completed. MYbank under Ant Financial offered a total credit line of RMB 212 million (43 borrowers in total) to CIC Property. Now 2 farmers have completed the pilot program and the loans released to 23 farmers are in progress. Borrowers are all farmers with years of breeding experience who have cooperated with Mongolian Sheep for more than

6 Mongolian

Sheep Partnered with Juhuasuan to Create an “Internet +” Mutton Feast (28 October 2015) [7 December 2017], http://finance.chinanews.com/life/2015/10-28/7593416.shtml.

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2 years. The loan amount for each farmer is around RMB 5 million, as the cost to purchase 10,000 lambs and the seeds and forage needed. The new entrant Ant Financial7 not only offers funds as an investor, but also brings new vitality to the “Sheep Union” model. While the core enterprise plays a leading role in the whole supply chain, Ant Financial evaluates the capability of the core enterprise in terms of market status, business, credit, assets and personnel etc. When the agreement on cooperation is reached, Ant Financial will provide the core enterprise with comprehensive financial services and the e-commerce service of the associated Alibaba Group, as well as the multi-level solutions of fund, data, insurance and product sales. By doing this, Ant Financial will further expand and intensify the core enterprise. Therefore, depending on its own ecosphere and advantages in e-commerce platform and technology, Ant Financial has made certain adjustments to the initial “Sheep Union” model in the respects of loan release, risk control and payment settlement. CIC Property plays a more important role in this cooperation. It offers sheep breeding insurance to farmers, including credit guarantee insurance,8 and plays the essential part in risk control. According to the Opinions on Implementing the New Concept of Development, Facilitating Agricultural Modernization and Achieving the Goal of Well -off Society in an All -round Way published by the State Council, we should actively explore agricultural insurance policy pledge loan and farmers’ credit guarantee insurance. Also mentioned in the Circular of the State Council on the Print and Distribution of Inclusive Finance Development Plan (2016–2020), we should improve the coverage rate of small and micro enterprises’ credit insurance and credit guarantee insurance and work hard to assure over 95% farmers are covered by agricultural insurance. Credit guarantee insurance has greatly supported and assisted the development of 7 Being

the resource integrator, the rural finance department of Ant Financial is responsible for coordination and communication of the cooperation details; and the specific tasks of due diligence and loan release are borne by Zhejiang MYbank. 8 Credit guarantee insurance means that the guarantee company provides the individual customer with credit guarantee, thus to assist the customer acquiring a mortgage-free small-amount shortterm loan from the company’s cooperative bank. In this case, the borrower is the policy holder, and the bank is the insured. In the event of any insurance accident (overdue for a certain number of days), the guarantee company will compensate the insured and acquire the right to recover the loan.

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inclusive finance and played an important role in the constant deepening of market-oriented reform of finance in China and the continuous improvement of multi-level capital market.

2.1

Specific Process

Before the cooperation officially commences, CIC Property has conducted the due diligence of Mongolian Sheep, and Ant Financial performed the due diligence of Mongolian Seep and CIC Property. As an investor, Ant Financial needs to know the risk control capability of CIC Property despite the guarantee. To this end, MYbank carried out rigorous research on CIC Property and evaluated its shareholders, China Orient Asset Management (its controlling shareholder), registered capital, operating income, guarantee fee income and investment income etc. in details. Before the release of loans, Mongolian Sheep will recommend key farmers who have cooperated with Mongolian Sheep for a long term (over 2 years) and have signed the agreement on the buy-back of flattened sheep to CIC Property; and CIC Property will conduct due diligence of Mongolian Sheep partners to understand their capital demand and use of funds as well as farmers’ basic information, breeding information and debt position etc. After underwriting, CIC Property will issue the policy of credit guarantee insurance and submit all information to MYbank. MYbank will release the Wangnong (literally means farmer-supporting) loan to the farmers based on whole-process review. The loan will be immediately released upon the approval. MYbank will directly transfer the loan to the Alipay accounts of Mongolian Sheep’s feed and lamb enterprises, and the intermediate accounts are now managed by CIC Property Inner Mongolia Branch. Farmers can only purchase the feeds designated by Mongolian Sheep through Rural Taobao platform of Alibaba and have the enterprise’s Alipay account to make payment for them. The trade will be successfully made after CIC Property reviews the order content. Rural Taobao will synchronize the information on farmers’ feeds with Mongolian Sheep as basis for tracking. When sheep are flattened to certain standards of

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slaughter, Mongolian Sheep will buy them back and settle with farmers. For the loan repayment, farmers shall make repayment independently through their Alipay account. In the preparation stage, Rural Taobao will assist all borrowing farmers opening Alipay and Rural Taobao accounts and train them on how to use the accounts. Moreover, the mutton processed food can be sold at Mongolian Sheep’s Tmall flagship store. Since this model is still in the trial period by now (June 2016), the actual operation model is a little inconsistent with the initial design. First, since the directional payment system of MYbank is still being debugged, the intermediate accounts are managed and orders are reviewed by CIC Property, to prevent farmers changing the use of the funds. The intermediate accounts are expected to be cancelled by September 2016. Since then, when farmers having acquired the loans pay for the commodities in Taobao shop, they can directly choose the account of “Wangnongfu” (literally means farmer-supporting payment) account. This direct payment account can only purchase the means of production from the shops of Rural Taobao. Second, as to the repayment, in the future, the money of Mongolian Sheep to buy sheep back will be first applied to repay the Wangnong loan of Ant Financial before the remaining profit is returned to farmers. At present, farmers are making repayments on their own, which brings about two problems. First, the repayment may be delayed since farmers are unfamiliar with Alipay system, and the farmers’ credit may be influenced. The revised model can avoid the default that is not caused by the repayment willingness. Second, more importantly, the fund is circulated completely within the system, lowering the credit risk. In accordance with credit guarantee insurance clauses, when the arrears exceed the term agreed in the insurance policy, CIC will compensate the full amount of loan principal unpaid by the farmer and the corresponding interest to Ant Financial and meanwhile acquire the right to recover the loan. In addition, Xida Guarantee offers counter guarantee to CIC and submits a deposit of RMB 30 million. If the loan repayment obligations have not been fulfilled upon maturity, the deposit will be paid by CIC to Ant Financial as compensation.

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Now the first batch of loans are being released, and the specific operation and loan repayment of this model need to be followed. The three-party cooperation process is shown in Fig. 5.

2.2

Risk Control System

Risk control is the core of finance as well as the life of the Internet. Then, how do the parties involved cooperate to perform risk control in the above case (see Table 1)? Ant Financial possesses a relatively perfect big data information system and depends on its own cloud computing platform to process the massive and miscellaneous data in a professional manner, thus to know everyone’s credit status. However, though the number of active Alipay users in rural market has exceeded 60 million (by March 2016), many users have no or seldom online transaction and behavior. Therefore, Ant Financial cannot carry out risk control over farmers based on big data that it is adept at. Ant Financial has found another way to develop rural credit business, the supply chain finance, which closely links Ant Financial with production. In this way, Ant Financial can rely on the leading enterprise to go deep into every link of production and bring the advantages of every party into play, and all parties will share the risks and risk premiums. Guarantee company offers guarantee to Ant

2 Provide Wangnong loan to cooperatives for production and operation

Farmers

Mongolian Sheep provides counter guarantee to insurance company

1 Mongolian Sheep provides breeding sheep to farmers and sign purchase orders 2 Mongolian Sheep buys back flattened sheep from farmers

3 Wangnong loan directional payment used to purchase the means and tools of agricultural production 3.1 Wangnong loan agricultural insurance used to insure the safety of the means of agricultural production

3.2 Tracking based on the information on the use of the means of agricultural production

Fig. 5 Mongolian Sheep—Ant Financial—CIC Property Cooperation Flow Chart

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Table 1 Risk control system—time dimension Time

Type of credit risk

Risk control measures

Risk control subject

Pre-loan

Information asymmetry

Mongolian Sheep CIC Property MYbank

In-loan

Capital use

a. Mongolian Sheep recommends the farmers who have cooperated with Mongolian Sheep for over 3 years and have established a stable business relationship as the borrowers b. CIC Property conducts detailed due diligence of farmers and understands the specific amount of capital demand c. MYbank performs due diligence of CIC Property and review the results of investigation on farmers a. Loan fund can only be used to buy the means of production from the designated Rural Taobao shops (directional payment technology) b. CIC Property monitors the capital flow in real time. As all means of production and capital flow belong to the enterprise, it only needs to monitor the cash flow of Mongolian Sheep a. To avoid the reselling of the means of production, Rural Taobao partner and CIC Property will check the arrival of the means of production on site Form to “six uniform standards”, and Mongolian Sheep provides technical guidance and veterinary services b. CIC Property provides breeding insurance for Mongolian Sheep farmers to control the risk of farmers unable to repay the loan due to natural disasters or some other causes

Used for other purpose

False trading

Farmers’ Breeding operating risk risk

Ant Financial CIC Property

Rural Taobao Partners CIC Property

Mongolian Sheep CIC Property

(continued)

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Table 1 (continued) Time

After-loan

Type of credit risk

Risk control measures

Price a. Mongolian Sheep fluctuation purchases sheep at a “protective price” that is determined based on the breeding purchase price and safeguards farmers’ interests Mongolian Sheep’s a. Mongolian Sheep operating risk purchases sheep at a “protective price” that is determined based on the breeding purchase price and safeguards farmers’ interests Repayment willingness a. The loan is released to farmers in a form of materials instead of money, and Mongolian Sheep settles with Ant Financial on behalf of farmers Repayment capability a. If the loan cannot be fully repaid due to the occurrence of risks, CIC Property will compensate the full amount to Ant Financial b. Xida Guarantee Company offers counter guarantee for part of the insurance of CIC Property

Risk control subject Mongolian Sheep

Mongolian Sheep

Mongolian Sheep

CIC Property Mongolian Sheep

Table 1 displays the specific risk control measures of different participants in different links. As the hub of the whole supply chain, Mongolian Sheep has a relatively large operation scale and certain brand influence, and has already built the whole-industrial-chain operation model. It is responsible for controlling the risks arising during the operation, including the breeding risk, price fluctuation risk and the operation risk of Mongolian Sheep itself. Farmers credit quality has been verified by the core enterprise during their long-term cooperation and games and is therefore more credible. Hence Mongolian Sheep recommending farmers can reduce information asymmetry to a certain degree.

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As the major risk bearer, CIC Property offers breeding insurance and credit guarantee insurance to farmers and mainly controls the risks of Mongolian Sheep and farmers. It shall conduct detailed due diligence before the loan to understand the credit status of farmers, clarify their loan demand and align the specific purchase quantity with the due diligence results, to avoid releasing a loan greater than their practical demand. In the middle of the loan, CIC Property shall monitor the use of the loan fund currently by reviewing farmers’ orders, and in the future, it can get to know the flow and use of fund in real time through the Taobao online query system. In the meantime, CIC Property shall examine the farmers’ breeding status regularly, pay attention to the market conditions and monitor the operation and sales status of Mongolian Sheep to avoid heavy losses. Zhejiang MYbank plays the role as an investor; the rural finance department of Ant Financial is responsible for resource integration and background technical support; and Rural Taobao plays a supporting part. These three subjects from Alibaba line also show important roles in risk control. In case of any default, CIC Property will compensate the full amount to MYbank, so MYbank focuses on control the risks of CIC Property. In addition to pre-loan due diligence, MYbank also conducts spot check (no lower than 10%) on the loan management measures of CIC Property, to urge CIC Property to fulfill its duties and control risks. In order to further assure the fund safety, Ant Financial implements the concept of replacing money with materials and adopts the directional payment system depending on its advantage in e-commerce. In this way, it strictly controls the purpose of loan use, which is one major risk to traditional loan institutions. In the whole supply chain, the loan fund is subject to a closed loop, and the credit risk is therefore controlled. Rural Taobao can assist the loan examination by examining whether lambs and seeds have arrived, thus to avoid the reselling of materials. By doing this, it can acquire a certain commission income. In respect to the risk control at the macro level, Ant Financial assigns over 2,200 servers to monitor, analyze and dispose of risks and depends on the background intelligent risk control brain code named “CTU” as the core weapon. CTU mainly judges whether the transactions are made by the account

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owner and verifies and intercepts suspicious transactions to avoid unauthorized transactions. At present, the risk control rate of Alipay is one in a million, lower than the probability of a person being hit by a meteorite.

3

What Does E-Commerce + Rural Supply Chain Finance Bring?

Under the “Sheep Union” model, Mongolian Sheep had cooperated with Bank of China, China CITIC Bank and Bank of Inner Mongolia. Why did it give up the cooperation with these state-owned banks and commercial banks and turn to cooperate with Ant Financial? Ant Financial’s great superiority is embodied in the ecosystem of Alibaba and the support from numerous partners in the system. Then, what changes have been brought to Ant Financial and Alibaba line, the practitioners of inclusive finance, during their cooperation with large new rural business entities such as Mongolian Sheep?

3.1

What Does “E-Commerce” Bring to Rural Supply Chain Finance?

3.1.1 Advantages in Lending The key issue of the agricultural supply chain is to solve the farmers’ difficulty in and high cost of borrowing. Different financial institutions vary significantly in their customer services due to their different structures and operation mechanisms. Ant Financial focuses on solving customers’ pain points and gives customers a better loan experience. In addition, risk control is the core of finance as well as the key to the development of inclusive finance. Compared to traditional financial institutions, Ant Financial can participate in customers’ use of loan as supported by Alibaba line, control the risks in every link and assure fund safety.

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First, in respect of loan use, the loans provided by Ant Financial can be used more flexibly and conveniently. Distinguished from new lending institutions, state-owned banks and commercial banks require complex review and approval formalities and the loans are provided in a less flexible manner. In general, the credit fund from the bank shall be used in batches, and review and approval are needed for each time of use, which takes about one month in most cases. Moreover, when the agricultural loans of the bank are exposed to a high non-performing loan ratio, the bank’s independent loan review will be stricter and more prudent. In this case, the bank will reject to approve or release loans selectively, which will severely upset the borrowers’ production and operation plans and influence their production and operation practice. For example, it takes about three months to breed a batch of flattened sheep before slaughter. If one month is needed for fund approval, the breeding of a new batch of sheep cannot be carried out or the breeding is suspended, presenting a critical test for farmers. First, Ant Financial will propose creative and flexible solutions against the customer’s specific demand and evidently improve farmers and herdsmen’s borrowing experience. For instance, in this case, Ant Financial designs a special financial product respectively for ewes and flattening sheep of Mongolian Sheep, which vary in the term and amount of the loan. The term of loan is closely associated with the production cycle of ewes and flattening sheep. Also it adopts the method of monthly repayment of interest and repayment of principal at maturity for two purposes: lowering the financial burdens of farmers and herdsmen and avoiding extra interest accrued by the idle borrowing. The second advantage is quick review and approval. MYbank will immediately release the money to the Alipay account immediately upon the approval. Farmers and herdsmen can acquire the loan about one week after the application, much faster than that of commercial banks. For example, Zhang Youquan, at the age of 50, signed the long-term purchase contract with Mongolian Sheep. He applied to Ant Financial for a loan on June 17 and received the fund on the same day upon the rapid review and approval by CIC Property and Ant Financial. At the same time, the partner at Rural Taobao visited Zhang’s home and helped him order feeds for flattening sheep on the agricultural materials platform of Rural Taobao.

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The next day Zhang got the notice on the shipment of feeds, and he no longer worried about money. Third, Ant Financial offers lump-sum credit. Within the one-year term, customers can withdraw money any time they need and no application is needed for each time of use. The simple formalities can save customers a lot of time and energy. Fourth, repayment on Alipay is convenient and free. As borrowers can withdraw and repay the fund any time, they can independently control the loan cost and plan for the fund use. Then, from the perspective of risk control of loans, Ant Financial makes use of Rural Taobao platform and depends on its excellent digital information technology and designs a smooth coordination and communication mechanism for all relevant parties, thus to participate in every link of the model. At first, CIC Property shall understand farmers’ preference about feeds and forage through due diligence and report the information to Rural Taobao. Then Rural Taobao will determine the supplier of products after on-line merchant invitation, release the products online, offer highquality feeds and forage as the means of production and avoids fake and inferior products. By doing this, Rural Taobao assures food safety at source. During the due diligence, CIC Property and Rural Taobao shall collaborate, on one hand, to open the Alipay account and Rural Taobao account to facilitate the following work, and on the other hand, to supervise whether CIC Property has fulfilled its duties and tasks. By involving Rural Taobao in this stage, it has prevented the borrowers of higher credit risks from acquiring loans and thus assured fund safety by controlling borrowers. Moreover, MYbank has electronized its own information and the due diligence information provided by CIC Property, since MYbank is fully aware of the significance of data and how to make full use of its advantage to solve issues. In regard to the use of the fund, the loan fund can only be used to purchase the means of production at the shop of the core enterprise on Rural Taobao platform. With all the online transaction data, Ant Financial can monitor and know the use and flow of the fund in real time. Moreover, if any abnormality discovered, CTU system will initiate multiple validations and fund freezing to assure fund safety. Hence the involvement of Rural Taobao can solve the risk about the use of funds

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to a certain extent. Furthermore, as authorized by farmers, Rural Taobao will synchronize the data on farmers’ purchase of lambs and feeds with Mongolian Sheep. Mongolian Sheep will inquire of farmers when and how many feeds and forage they buy, to speculate whether they breed sheep pursuant to uniform requirements and their breeding status, for the sake of product standards and food safety. During the repayment stage, farmers’ repayment data can be mastered due to the repayment through Alipay account. By gathering farmers’ identity information, purchase information, repayment information and opening of Taobao account, it helps the electronization of transaction information left by consumption behaviors to a certain extent. Based on the real transaction data accumulated, Ant Financial can give full play to its successful experience in data risk control, accurately identify risks and, in the future, realize automatic credit approval for farmers and cooperatives in the breeding industry. As Rural Taobao is deeply involved in the front end and middle end of the chain, MYbank realizes the realtime and all-round risk monitoring of the whole supply chain depending on digital information technology. The participation of e-commerce has improved the risk control methods of supply chain finance.

3.1.2 Market Expansion Being a world-famous e-commerce platform, Tmall has the largest shopping traffic online and highly active user group in China. Tmall Fresh has become the online fresh products platform of choice winning consumers’ trust for its high-quality products and perfect services. With the involvement of e-commerce, the core enterprise can further explore the product sales market, thus to expand the scale of the whole supply chain and achieve common benefits of all participants upstream or downstream of the chain. In the activity themed “Green Inner Mongolian Products for the Youth” held on Juhuasuan platform between October 19 and 22, 2015, “Mongolian Sheep flagship store attained more than 2 million page views, achieved a transaction volume of RMB 3.57 million of fresh mutton products, sold 11,000 lamb gigots, 15,000 lamb rolls, more than 5,000 lamb chops and over 6,000 other kinds of mutton products,

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and placed more than 30,000 cold-chain delivery orders to more than 20 provinces of China. Consumers nationwide enjoyed the authentic Inner Mongolian green and fresh mutton.”9 On July 8, 2016, Mongolian Sheep and Tmall Fresh officially signed the strategic cooperation agreement and initiated win-win cooperation. Both parties will develop their cooperation in terms of user demand mining, product research, development and innovation and production standards upgrading etc. In the future, Ant Financial will exclusively design featured theme activities for Mongolian Sheep, like promoting high-quality products of Mongolian Sheep in the Spring Festival on Rural Taobao platform. Based on the Internet communication, an increasing number of consumers will get to know, understand and rapidly purchase Mongolian Sheep products, which is of great significance of the sales expansion of Mongolian Sheep.

3.1.3 Reduce Middlemen and Weaken “Bullwhip Effect” Agriculture and animal husbandry are naturally characterized by many agents and intermediate links as well as the high cost. Directly matching the company and end consumers, the e-commerce platform can reduce intermediate traders and agencies. In this way, first, the process from farm to dining table is shortened, and the cost of each link is lowered. These profits can be given to farmers and herdsmen, while consumers can enjoy delicious food at a lower cost. Second, the direct matching can weaken the bullwhip effect caused by the too long supply chain. Mongolian Sheep can avoid middlemen and directly acquire the purchase and demand information of downstream consumers, so it can accurately identify the characteristics of different user groups in different areas and create customize products oriented on consumers’ demand. Effective information sharing reduces the distortion of information throughout the multi-party communication. When the distance between the tip and the root of the bullwhip is shortened, the trembling of the tip will not cause big movement of the root.

9 Mongolian

Sheep Partnered with Juhuasuan to Create an “Internet +” Mutton Feast (28 October 2015) [7 December 2017], http://finance.chinanews.com/life/2015/10-28/7593416.shtml.

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What Does Rural Supply Chain Bring to “E-Commerce”?

By the end of April 2016, Ant Financial had acquired Series B financing of RMB 4.5 billion and announced its three strategic focuses in the future, rural finance, international business and green finance. The rural strategy will be combined with Alibaba Group’s strategy of going to countryside and the business of Rural Taobao, to enable rural consumers to enjoy convenient payment services and other financial services. Rural finance is important battlefield for Ant Financial to practice inclusive finance. In Ant Financial’s solution of driving rural finance with credit, the rural finance department classifies customers and offers varied credit product solutions to different users (see Fig. 6), including the data platform for online loans, online + offline acquaintance platform, supply chain + directional payment platform and financial lease platform etc. Rural finance shall seize the opportunity to drive China’s agricultural transformation and upgrading with inclusive finance products and instruments that are closely associated with farmers’ daily life and rural production and operation. Agricultural labor productivity in China is D. Fixed asset input: financial lease platform Large new rural business entities Rural farmers Small business households Small and micro enterprises/ selfemployed households

C. Production means input: supply chain + directional payment platform

B. Online + offline acquaintance platform

Rural consumers Rural farmers Rural e-commerce & Rural Taobao partners Quantity of customers

A. Data platform

Amount of credit funds

Fig. 6 The solution of driving rural finance with credit assists the upgrading of agriculture

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still relatively low now. This situation will be improved in the future, when scaled operation will be developed and consumers’ personalized demands will inspire more high-quality products. In order to facilitate the scaled operation and high-quality production of agriculture, capital and investment are needed to help farmers purchase agricultural machinery and implements and slaughtering equipment etc. Seeing this major trend, Ant Financial strives to accelerate the transformation from traditional agriculture to modern agriculture by cooperating with the core enterprise, building the agricultural supply chain financial service system, introducing urban surplus funds to the sectors of agricultural upgrading and promoting the scaled operation of the planting and breeding industry. By applying supply chain finance, it can depend on the core enterprise to control risks and borrow materials or the means of production instead of money, further lowering the fund risk. Ant Financial has made contributions to facilitating the transformation from traditional agriculture to modern agriculture. In the case of Mongolian Sheep’s supply chain finance, the fund of RMB 5 million and 10,000 flattening sheep are provided in accordance with Mongolian Sheep President Yan Shuchun’s planning strategy that “we will expand farmers’ scale, and help those who own 5000 sheep increase the number of sheep to 10,000 and from 10,000 to 15,000”. Developing rural finance not only supports supply chain finance to satisfy the credit demand of particular groups, but also creates new values for the whole ecosystem of Alibaba. As to MYbank, its Wangnong loan product can only be provided through the Rural Taobao partner. Whenever farmers have any loan demand, the Rural Taobao partner will refer them to the bank. Moreover, the Wangnong loan is smallamount personal credit loan, the amount of which now is around RMB 50,000, so it cannot reach large-scale farmers through the partner mode. However, with the credit + insurance + leading enterprise model, it can reach large-scale rural farmers and small business households, thus it can support the development of real enterprises, expand the credit size and improve the fund utilization efficiency. From the perspective of Rural Taobao partners, their commission incomes are mainly from their purchasing services. In Inner Mongolia with much land and few people, the number of Taobao orders is small and villagers’ consumption

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enthusiasm of fast moving consumer goods (FMCG) is not high. Partners only lived on the commission income from FMCG sales in the past, but now they can purchase lambs, feeds and forage for farmers and earn commission incomes from the sales of the means of agricultural production. Apparently, their incomes have increased. Moreover, by purchasing the means of production on Rural Taobao platform, it, on one hand, guarantees the quality of the means of production used by farmers; and, on the other hand, increases the sales volume of Rural Taobao. Selling high-quality mutton products in Tmall shops also contributes to the development of the e-commerce platform, and meanwhile helps consumers trace products and increase end consumers’ choices.

Case Comments After the system of contracted responsibility was implemented in rural areas in the Reform and Opening Up, the mode of small-scale peasant economy was applied again in rural production. The reform of land management right has inspired farmers’ production enthusiasm, but the problems of small-scale peasant economy have also arisen, such as the vulnerability to natural disasters and market fluctuations and the inadaptability to modern and commercialized agricultural operation modes, which have restricted the development of rural economy and the improvement of farmers’ living standards. Rural supply chain is an important means for modern enterprises to connect upstream or downstream farmers and incorporated the large number of farmers into modern agricultural and commercial economic circulation. Apparently, it is of great significance for changing the backwardness in rural China. By adding financial services to the rural supply chain, it enables small and micro finance for rural areas to play its great part in improving operation and promoting development. In other words, finance is really used to support “agriculture, rural areas and farmers”. Then, what changes will the fast-growing Internet economy and digital finance bring to traditional rural supply chain finance? This case displays vividly the whole process of how Ant Financial, a digital finance institution, offer financial services to farmers by joining the rural supply

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chain. Though this program of Ant Financial has only been implemented for less than a year as of case writing, this case study has collected massive information on and clearly described the overall design of the program, the requests of and connections among the subjects and the operation status right after the commencement of the program. It has presented a whole and detailed picture to readers. With joining in Mongolian Sheep’s supply chain, Ant Financial not only brings financial services (payment and credit), but also provides the e-commerce resources of Alibaba line to provide Mongolian Sheep with a better sales platform and lower its market risks. Such platform-based operation integrating e-commerce, production and financial services has actually extended the traditional supply chain and supply chain finance and created a promising ecosystem that integrate supply, production, sales, finance and consumption. This case cannot show the effect of Ant Financial providing financial services to Mongolian Sheep’s supply chain in a whole credit cycle and the effect of the e-commerce platform in supporting Mongolian Sheep’s sales due to the time limit. We hope that this case study can follow up the case and present it in a more complete manner in the future. Commentator: Song Hua10

References Hu Guohui, Zheng Meng, On the Operation Mode and Income Distribution of Rural Supply Chain Finance, “Rural Economy”, 2013, 5:45–49. Hu Yuefei, Huang Shaoqing, Supply Chain Finance: Background, Innovation and Definition, “Research on Financial and Economic Issues”, 2008, 8:76–82. Song Ya’nan, Zhao Wen, Yu Maomin, Growth of Agriculture Industrial Chain and Innovation of Supply Chain Finance Services: Mechanism and Cases, “Rural Finance Research”, 2012, 3:11–18.

10 Associate

Dean, professor and doctoral supervisor of Renmin Business School.

8 Wenshang Pioneer Loan: Case of JD Rural Supply Chain Finance Congyi Ju, Xinyu Chen, Yangrui Li, Zuo Wang, and Wenzhao Tong

Faced with the huge market in rural China, Alibaba, JD and Suning have launched their rural e-commerce development plans successively. For example, the 1,000-county 10,000-village plan released by Alibaba Group in 2014 and the 3F strategy released by JD both placed their important strategic goals on expanding rural e-commerce market. An important means to expand rural market is to develop rural finance. C. Ju (B) · X. Chen · Y. Li · Z. Wang · W. Tong Renmin University of China, Beijing, China e-mail: [email protected] X. Chen e-mail: [email protected] Z. Wang e-mail: [email protected] W. Tong e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_8

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Meanwhile, rural supply chain finance is an optimum rural finance model that gives full play to the resource advantages of e-commerce. This case attempts to conduct in-depth tracking analysis of JD Agricultural Loan—Pioneer Loan, the e-commerce + rural supply chain finance pilot program initiated by JD Finance in 2015, examine the operation model for e-commerce to join rural supply chain finance and summarize problems and experiences.

1

Rural Strategy of JD and JD Finance

JD officially marched into the e-commerce industry in 2004. In 2015, JD Group reached a trading volume of RMB 462.7 billion and a net income of RMB 181.3 billion. Its annual trading volume has grown by 78% compared to the previous year, with a growth rate 2 times of the average rate of the whole industry. In 2016, JD Group was listed in Fortune Global 500, the first Chinese Internet enterprise in the ranking. JD Group was officially listed on Nasdaq Stock Exchange in May 2014 and selected in NASDAQ 100 index and Nasdaq 100 Equal Weighted Index in July 2015. By December 31, 2015, JD Group had approximately 110,000 regular employees and its business covered three major fields, e-commerce, finance and technology. JD Finance Group started independent operation since October 2013 as a financial technology company. Depending on JD ecosystem as well as the big data accumulated by JD and the credit system constructed on the data, JD Finance provides all kinds of customers and users with various financial services including financing, wealth management, payment and crowdfunding etc. and offers one-stop wholeindustrial-chain services to entrepreneurs and innovators. At present, JD Finance has established seven major business sectors, respectively supply chain finance, consumption finance, crowdfunding, wealth management, payment, insurance and securities. By successively launching innovative products such as Jingbaobei, JD Baitiao (IOU), JD Wallet, JD Finance App, JD Slush Fund, Jingxiaodai (small loan) and Xiaobai Wealth Management etc., JD Finance has successfully achieved an overall layout consisting of company finance and consumer finance.

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Seven Major Business Sectors of JD Finance Sector of consumption finance. The major product in the sector of JD consumption finance is JD Baitiao (literally means the receipt for a loan, i.e. IOU), which is a credit payment product for individual users. Baitiao provides the borrower with interest-free consumption credit of repayment by instalments. The credit is offered to the customer based on the credit model according to the user’s previous consumption information and authentication information at JD. Since February 13, 2014 when the product was released, Baitiao has accumulated 10 million users. During “Double 11” 2015, Baitiao users had increased at a rate of 800%1 compared to the previous year. In addition to shopping consumption lending in JD, Baitiao is now applied to more offline scenarios such as house renting, decoration and education and so on. Sector of supply chain finance. JD Finance provides supply chain finance service to commodity suppliers. In JD Mall, the sales of commodities are conducted in two ways, direct sale or third-party sale. The so-called direct sale means that JD directly orders commodities from the manufacturer and sells them on JD platform, which assures the commodity quality and delivery speed compared to third-party sale. The direct sale occupies a higher proportion than the third-party sale. In self-operated business, JD has established long-term partnership with the suppliers of direct-sale commodities, and developed supply chain finance on this basis. Supply chain finance means that JD and other financial institutions provide loans to manufacturers based on the mortgage of receivables and inventories. Jingbaobei and Jingxiaodai are the most famous products in JD supply chain finance. By the beginning of 2016, Jingbaobei had offered services to nearly 2,000 JD Mall suppliers, whose trading volume at JD had increased by over 200%; and Jingxiaodai had served over 30,000 shops. Moreover, movable property financing and corporate wealth management services have been successively released. 1 JD

Baitiao Users Increased by 800% Compared to the Previous Year in Double 11 (2015, November 12) [2017, November 7], http://www.askci.com/news/chanye/2015/11/12/151628 g70v.shtml.

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Sector of crowdfunding. JD officially launched the product Crowdfunding on July 1, 2014. JD Crowdfunding defines crowdfunding as a “new scenario solution” aiming to activate new consumption forms, advocate new cultural ideas and convey new life attitude. In the age of new consumption upgrading, JD Crowdfunding is not only a funding platform, but also an incubation platform, which, on one hand, supports entrepreneurial and innovative enterprises, and on the other hand, enriches JD users’ product experience and satisfies users’ upgraded consumption demands. JD Dongjia (literally means boss) officially released on Mar. 31, 2015 is a famous brand in JD crowdfunding. Sector of insurance. JD Finance has designed and launched a series of insurance products such as insurance extension, broken-screen mobile phone replacement insurance, “30·180 security insurance”, goods injection insurance and seven-day unintentional overdue Baitiao repayment insurance etc., to safeguard users’ rights and benefits. JD Finance has applied for being an insurance agent and is now preparing to establish its own insurance company. Sector of wealth management. JD wealth management mainly serves customers accumulated in the e-commerce ecosystem and offers following products: bank’s fixed-income financial products including fixed-income securities and notes, insurance wealth management business, funds, stock funds, slush fund and part of monetary funds etc. Wealth management business has a large daily trading volume. Sector of payment. In October 2012, JD purchased Chinabank Payments Technology Co., Ltd. and acquired the license for third-party payment. The payment business of JD Finance provides the main channels for JD Finance’s other businesses and mall business. At present, JD is vigorously promoting its payment service in its mall system, partners with tourism, real estate, catering, communication, games, e-commerce, finance and other major industries and provides Baitiao payment and loan payment. Sector of securities. Since 2015, JD Finance has launched two products, JD Finance big data consumption index and JD Finance quantitative strategy development platform (or quantitative platform), depending on its data capability and technical capability, to serve the securities industry.

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In September 2015, JD Group published rural 3F strategy. It gives full play to JD’s huge advantages in channel sinking (i.e. targeting low-tier consumers and markets), e-commerce and Internet finance and extends JD e-commerce platform’s sales channels and JD self-support logistics to cover the whole process of agricultural material procurement and agricultural products planting and sales. Meanwhile, financial products that satisfy farmers’ demands on agricultural production funds are developed based on the financial demands in the whole industrial chain including processing and marketing. The 3F strategy consists of factory to country, farm to table and finance to country. As to factory to country, JD Group has set up JD service centers responsible for distribution and commodity display in 1,500 counties since 2015 and developed over 300,000 village promoters.2 Farm to table is the fresh e-commerce strategy, which means JD signs agreements with local governments to create local specialty shops in JD Mall, to introduce local characteristic agricultural and sideline commodities to cities and match the suppliers and demanders of agricultural products.3 Finance to country is the rural finance strategy, by which JD depends on its great advantages in channel sinking, e-commerce and Internet finance, focuses on the closed loop of rural economy that centers on “agricultural products to city” and “industrial products to country” and designs and creates the rural finance model with JD characteristics. Such financial model aims to cover the financial demands of the whole industrial chain including farmers’ demands on agricultural material purchase and agricultural product planting of farmers as well as processing and sales demands; and concentrates on rural consumption and life and provides financial services with comprehensive products including credit, payment, wealth management, crowdfunding and insurance etc. JD Finance strives to provide rural product chain finance as its characteristic, builds and optimizes rural economic ecology with its financial services, arouses rural financial vitality, supports rural economic development and improves farmers’ living standards. 2 Promoters

work part time for JD by promoting JD and purchasing commodities for villagers, to increase villagers’ awareness of e-commerce. 3 Consideration the high transportation requirements of agricultural products as they are perishable, JD needs to further build the fresh cold-chain network.

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Released in September 2015, JD Agricultural Loan aims to satisfy farmers’ operation borrowing demands. The first batch of JD Agricultural Loans are provided to DuPont Pioneer seeds growers in Shandong area and loquat growers in Renshou Sichuan in a form of supply chain finance. To this end, JD Finance cooperated with the regional distributor of DuPont Pioneer Company and Sichuan Furenyuan Agriculture Company to establish the operation mode of e-commerce + rural supply chain finance. By May 2016, the Shandong pilot program had released loans of approximately RMB 10 million in Jining area.

2

JD Rural Supply Chain Finance Program: JD Agricultural Loan—Pioneer Loan4

Initiated in September 2015, JD Agricultural Loan—Pioneer Loan is a loan program of JD rural finance department designed to satisfy growers’ demands on production funds. Pioneer Loan relies on the leading enterprise in the grain planting industry, covers three links respectively seed sales, grain planting and grain recovery, and forms a relatively complete agricultural product supply chain financial model that involves e-commerce. The first pilot program of Pioneer Loan was carried out in part of field crops planting areas in Shandong. This case follows and analyzes the 8-month operation of JD Agricultural Loan—Pioneer Loan in Wenshang, Shandong (September 2015— early May 2016). Though the harvest seasons of wheat and corn have not arrived (June, July, and October) and the final loan recovery cannot be accurately observed as Wenshang Pioneer Loan has not completed a supply chain cycle, we can still understand the program design and the implementation of most processes through field investigation.

4 Explanations

of some terms in this part: JD: Beijing Jingdong Group Co., Ltd. Wenshang Pioneer Loan: JD Agricultural Loan—Pioneer Loan program in Wenshang County. Shandong Daliang: Shandong Daliang Agricultural Development Co., Ltd. Jining Daliang: subsidiary of Shandong Daliang in Jining. Daliang Company: Shandong Daliang and Jining Daliang. BOC Fullerton: BOC Fullerton Community Bank.

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Industrial Background

Wenshang County of Shandong Province is one of the pilot areas of JD Agricultural Loan—Pioneer Loan. Located in the northernmost part of Jining city, Wenshang County covers a total area of 889.1 square kilometers, facing Dongping and Feicheng across the water in the north, joining Ningyang and Yanzhou in the east, bordering on Jiaxiang and Liangshan in the west and connects with Jining downtown area in the south. Moreover, it is situated near Weishan Lake and Beijing-Hangzhou Canal, enjoys adequate illumination and four seasons, and has a geographic position and good climate suitable for grain planting. As an important grain-producing county in Shandong, Wenshang County mainly produces wheat and corn. The planting season of wheat lasts from October to the next May, and corn from June to October. In 2015, the whole county had approximately 765 thousand mu (mu, a traditional unit of area in China, with 1 mu equal to about 666.67 square meters) wheat field, taking up 57.4% of the area of the whole county. According to the agricultural bureau, 562 farmers5 in the county has a planting area over 200 mu. Considering the operation and management capability and risk tolerance of farmers, the optimum production and operation scale recommended by the government is 200 mu per household. According to our investigation, the planting cost of growers in Wenshang County consists of the land contracting fee, agricultural material cost (seeds and chemical fertilizers) and labor cost. Among them, the land contracting fee is RMB 700 per mu per year, while agricultural material cost is about RMB 440 and labor cost is approximately RMB 700 per mu per year. Therefore, the rigid cost for local farmers to work on a single mu of land is about RMB 1,840 on average. The planting area of small local farmers is generally 50 mu, so the total cost is RMB 92 thousand. According to statistics, the per capita net income of farmers in Wenshang County in 2014 was RMB 12.8 thousand. Apparently, the expenditure of RMB 92 thousand imposes heavy pressure on farmers. If a 5 As

estimated based on the operation status of Jining Daliang Company, the figure should be over 700.

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farmer plants crops of 200 mu, the total cost is approximately RMB 368 thousand, which can hardly be afforded with self-accumulated funds. Therefore, to plant crops of 200 mu as per Wenshang government’s appeal, external financial support is needed. However, grains and agricultural materials of growers cannot be mortgaged and the mortgage values of their houses are limited, making it difficult for growers to acquire bank loans. Even they can successfully acquire loans, the loan review and approval take at least one month. The great financing demand and large financing difficulty constitute an important barrier that impedes the development of scaled agricultural operation in Wenshang County. If agricultural growers’ financial demands can be satisfied through agricultural supply chain finance, the scaled and modern development of agriculture in this county will be effectively promoted.

2.2

Grain Planting Supply Chain in Wenshang County

As a subsidiary of Shandong Daliang in Jining area, Jining Daliang mainly serves grain growers in Jining area including Wenshang County and sells agricultural materials mainly including seeds. Among the seeds, corn seeds are supplied by DuPont Pioneer Company and some wheat seeds are of high quality. In 2015, the planting area using the seeds supplied by Jining Daliang occupied 30% of the total planting area of Wenshang County, including 15% contributed by corn seeds. Highquality seeds were applied to an area of 11,000 mu in 2015, accounting for 1.5%, which was increased to 40,000–50,000 mu in 2016. The wheat seeds sold by Jining Daliang include the high-quality wheat seeds supplied by DuPont Pioneer and also ordinary wheat seeds preserved by Jining Daliang itself. The high-quality seeds cost 0.2 yuan/kg higher than ordinary wheat seeds, but they assure the high quality of products and high recovery price.6

6 Jining

Daliang Company purchases the wheat and corn planted with high-quality seeds at a price higher than market price.

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From the perspective of the overall business, Jining Daliang distributes and sells agricultural materials (including seeds, pesticides and chemical fertilizers) to agricultural growers before production, provides plant protection and technical guidance and services during production, and dries and purchases grains after production. In this way, it offers wholeindustrial-chain services. See the products and services of the company in Fig. 1. Jining Daliang applies the three-level sales model, respectively account manager, salesperson and farmer. The account manager is responsible for the farmers’ conditions in the whole area; and the salesperson communicates with some farmers face to face to understand farmers’ conditions and promote company products to increase the sales of the company. See the sales business structure of Jining Daliang in Fig. 2. During the cooperation between agricultural growers and Jining Daliang in Wenshang County, an operation model integrating purchase, production and sales has been formed. In this model, seeds and chemical fertilizers are provided by Jining Daliang; planting work is completed by farmers and Jining Daliang offers plant protection service; and after production, grains are recovered by Jining Daliang in accordance with the agreement; and Jining Daliang sells some corn to downstream Chia Tai Company (a seed company) and the dried wheat to flour mills. In this supply chain, the core enterprise is Jining Daliang Company. It is the Services before production Seeds

Pesticides

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Fig. 1 Products and services of Jining Daliang Company

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Company

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Fig. 2 Sales model of Jining Daliang Company

upstream raw material supplier for grain production as well as the downstream finished product buyer for grain production. By selling seeds and recovering grains, Jining Daliang has cultivated stable farmer customer bases both upstream and downstream and accumulated massive information of customers’ operation and credit status through the supply chain operation, which constitute very important information resources for supply chain management. See the grain production supply chain with Jining Daliang as the core enterprise in Fig. 3.

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Chemical fertilizer etc. Other agricultural material sellers

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Fig. 3 Planting production supply chain with Jining Daliang as the core enterprise

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Origin of Wenshang Pioneer Loan

According to the estimations above, to realize scaled operation of grain planting in Wenshang County (based on the country government’s estimation that the optimum planting area should be around 200 mu) money is a big problem. President Wang of Jining Daliang also told us farmers had large demands for loans based on their years of operation experience. Therefore, its parent company Shandong Daliang hoped to launch a credit product for farmers’ grain production jointly with a financial institution. Shandong Daliang once negotiated with local BOC Fullerton on this. However, being a village bank, BOC Fullerton insisted on traditional rural credit method, and such method required farmers providing mortgage guarantee and needed complicated formalities and a long time for farmers to acquire loans. Farmers have no collateral required in the bank’s traditional business, which is exactly the pain point of rural loans. Apparently, BOC Fullerton cannot help Daliang Company solve farmers’ capital demand in grain planting. After the 3F strategy of rural e-commerce and rural finance was launched by JD in 2015, the question of the first importance was how to march into the rural finance market. JD’s advantage is its e-commerce as well as the big data and information technology accumulated in e-commerce. However, this advantage disappears in rural finance since most farmers are not JD e-commerce customers and have no information on online behaviors and account transactions. The lack of farmers’ information is the biggest barrier that prevents JD from entering rural finance market. As an e-commerce company, JD cannot copy the mode of traditional banks, i.e. establishing a team of rural loan officers. Therefore, by cooperating with Daliang Company, JD can make use of the farmers’ information of Daliang to offer credit to farmers. From the perspective of Shandong Daliang, JD enjoys flexibility and aspiration as a new financial form and thus provides innovative portfolio of credit business; and JD also has a broad e-commerce trading platform, makes information acquisition, portfolio creation and coordination more convenient in e-commerce ecosystem and acquires a higher value of portfolio collaboration. In view of this, both parties will achieve a win-win situation

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through cooperation. Hence, both parties immediately agreed on cooperation after negotiation. Shandong Daliang and JD Finance have worked together to initiate the grain planting supply chain financial program of “Wenshang Pioneer Loan” in Wenshang County.

2.4

Overview of Wenshang Pioneer Loan

2.4.1 Supply Chain Finance Model of Wenshang Pioneer Loan In the supply chain finance of Pioneer Loan, agricultural farmers are the downstream “enterprises”, agricultural material purchaser, in the grain planting industrial chain, as well as the party demanding funds. Daliang Company is the upstream enterprise (supplier) that provides agricultural materials including seeds and chemical fertilizers to farmers. To solve farmers’ fund demands for agricultural material purchase, JD accepts the entrustment of Daliang and pays for the agricultural materials to Daliang on behalf of farmers, by which the loan relationship has been established between farmers and JD. After harvesting and selling grains, farmers will repay JD’s loans with sales incomes. Therefore, the supply chain finance model of Wenshang Pioneer Loan is essentially implemented in a form of entrusted payment. See the process of Wenshang Pioneer Loan in Fig. 4. In the first step, JD cooperates with agricultural material company, and county-level distributors and product managers develop customers. Seed Core enterprise

Farmer

on ati er op Co g in nc t na en Fi y m pa

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Fig. 4 Operation model of Wenshang Pioneer loan

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Second, farmers approved by distributors fill in basic information on JD’s website. Third, JD pays for agricultural materials to the core enterprise (Shandong Daliang). Fourth, farmers use the agricultural materials purchased with loans in agricultural production. Fifth, farmers repay principal and interest to JD as scheduled.

2.4.2 Characteristics of Wenshang Pioneer Loan In addition to the particularity of grain planting supply chain and rural finance, Wenshang Pioneer Loan also features the following three characteristics: First, the power to grant credit is delegated to the lower level. In the process of Wenshang Pioneer Loan, JD first grants initial credit to Shandong Daliang; then Shandong Daliang distributes the credit line to Jining Daliang; and finally Jining Daliang grants credit to farmers. Since Jining Daliang has cooperated with farmers for years and has kept complete trading records of farmers, it can accurately judge the operation status and debt repayment ability of farmers. Hence, the credit-granting process of Pioneer Loan stresses on the credit interaction between Daliang and farmers, which will be explained in details later. Second, the credit guarantee of the core enterprise constitutes the ultimate guarantee for risk control. In Wenshang Pioneer Loan, Jining Daliang offers credit guarantee to farmers’ loans as a second-level distributor; and farmers repay interest monthly and return the loan principal with the income from grain sales (generally realized by the grain purchase by Jining Daliang) upon the maturity of the loan. In this way, the farmers’ issue of few collateral and difficulty to acquire mortgage loan have been solved, and loan mortgage and credit guarantee are effectively combined. Daliang offers full-amount guarantee to farmers’ loans mainly because it masters farmers’ credit information, directly monitors the whole production process and provides necessary technical support to farmers’ production activities. Third, fast review and rapid loan release. According to the design of Pioneer Loan, when demanding a loan, the farmer shall sign a contract with Daliang and fill in the application form on the official website of

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Beijing Agricultural Loan. After the contract is signed, the farmer shall submit the ID card, household register, land use certificate and threehousehold joint-guarantee warranty etc. to Jining Daliang for internal review, which takes 1–2 days. Upon the approval, the materials will be submitted to Shandong Daliang for determination of the loan amount, which needs 1–2 days. Shandong Daliang gives the electronic form of materials to JD for authenticity review, and the loan will be released upon JD’s approval. This link lasts for about 2 days. The whole process is completed within one week. Compared with the loans in traditional finance, the materials required here are more consistent with farmers’ production and operation practice, so loan review in the whole process is faster (see Fig. 5). The dotted line in Fig. 5 represents the capital flow without any borrowing: the farmer borrowers from a credit institution, pays the agricultural material company it selects independently and repays the credit institution at maturity. The grey solid line shows the capital flow in the supply chain finance model: JD Agricultural Loan reviews the farmer’s qualification, provides credit guarantee and makes advance payment for Debtor-creditor relationship Agricultural material release Seed company

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Fig. 5 Process of Wenshang Pioneer loan

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the farmer; the seed company offers agricultural materials; and the farmer repays the loan at maturity. Apparently, the path of capital flow has changed in this supply chain. The black solid line suggests the material flow in the supply chain: the seed company provides materials; and the farmer receives them. Materials only circulate between the seed company and the farmer.

2.5

Wenshang Pioneer Loan in the Pilot Period

2.5.1 Basic Situation According to investigation, JD Agricultural Loan started the pilot program of rural supply chain finance in Wenshang County of Shandong in September 2015. By the end of May 2016, out of the farmland covering over 60,000 mu managed by Jining Daliang, the local agent of DuPont Company, approximately 1/6 purchased seeds making use of the credit model of Pioneer Loan. In the pilot period, the model has covered 1/4 large farmers and part of medium and small farmers. Big farmers refer to those having a planting area over 500 mu, and 100–400 mu for medium and small farmers. Big and small farmers have acquired for loans totaling over RMB 10 million, and the average loan amount per farmer has been around RMB 100 thousand. The formalities for loan release take 3–7 days, no more than 10 days, depending on the documents submitted by different farmers. Compared to local cooperatives and various banks which spend 1 month to release a loan, Wenshang Pioneer Loan shows a great time advantage and therefore has been widely and successfully accepted. Some farmer borrowers were interviewed and reported by 29 media in March this year.

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2.5.2 Loan Principles At present, Wenshang Pioneer Loan has set up two principles for the review on farmers’ loan applications: First, long-term cooperation principle. Based on this principle, Wenshang Pioneer Loan is firstly provided to farmers who have cooperated with Jining Daliang for years. This method is adopted mainly for two reasons. First, it can reduce information asymmetry. The lender has accumulated more information on the credit status of long-term customers, and therefore has a better understanding about their possibility of default. Second, farmers of long-term cooperation are more dependent on the supply chain, so in the context of repeated gams, these farmers have a higher cost of default and lower intention of default. Therefore, the lender’s risks of default loss are correspondingly reduced. Second, the principle of middle-sized farmer customers. Wenshang Pioneer Loan regards 200 mu area of planting as the optimum scale of farmer customers. At the beginning of the pilot program, Wenshang Pioneer Loan mainly released loans to farmers with a planting area over 500 mu, which will be reduced to the optimum scale around 200 mu in the future. According to President Wang of Jining Daliang, the reduction is carried out mainly for three considerations. First, the farmers with a too large planting area will be exposed to greater losses in case of poor management and greater operation risks; and the farmers with a planting area of about 200 mu mainly run family farms, and therefore show lower risks based on steady operation. Second, large farmers have a large demand for funds, while the current loans offered are microloans subject to the limit of RMB 100,000, so their fund demands cannot be effectively solved. Third, from the perspective of farmer adhesion, too small farmers, such as those with a planting scale around 50 mu, may be less likely to purchase seeds from Daliang or establish a long-term stable partnership with the company. Therefore, small farmers are not the ideal loan customers. Farmers with a planting area above 100 mu show a stronger desire to purchase seeds from Jining Daliang, and are regarded as the stable customers of Jining Daliang.

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Therefore, JD Agricultural Loan—Pioneer Loan that provides financial services to the supply chain regards farmers with a planting area of 200 mu as the best choice, but accepts those ranging between 50 and 500 mu.

2.5.3 Credit Model As a feature of Wenshang Pioneer Loan, Daliang is leading the program, while JD involves in loan review. As described in the process of Wenshang Pioneer Loan mentioned above, the loan release is led by Daliang (seed distributor). Daliang will perform initial review based on a farmer’s applications, recommend the farmer to JD if the farmer has passed the review and input all relevant information of the farmer in the business background of JD Agricultural Loan. Then JD will review the data input, introduce the data into the existing risk control system based on JD’s big data and the risk control model of this program, and thus make the final judgment on loan release to the farmer (see Fig. 6). In this process, Daliang plays an important role based on its information on the customer, while JD is in a relatively passive position. With the accumulated data information and the increasingly mature program, JD hopes to take an active part in loan release as soon as possible. Initial credit granting JD

a Re

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Fig. 6 Detailed explanation of the release of Wenshang Pioneer loan

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2.5.4 Interest Rate and Repayment Wenshang Pioneer Loan renders a low interest rate and sees favorable status of repayment. Its current monthly interest rate is 0.7%, lower than the bank’s interest rate of 1%.7 Repayment is made in a form of monthly repayment of interest and principal repayment at maturity. The repayment term is determined by the agricultural production cycle, generally nine months for wheat and four months for corn. As Wenshang Pioneer Loan has not completed one single operation cycle (which is nine months according to the wheat growth cycle) by the end of this case investigation (May 29, 2016), the overall status about loan recovery cannot be fully determined. However, according to statistics, the monthly repayment of interest by farmers is in good conditions. By May 2016, among the first batch of 20 farmers acquiring loans, one has made the full repayment. When making repayments, farmers will directly give money to Jining Daliang, and Daliang will repay the loan on behalf of farmers. Generally, JD sends short messages of repayment reminder one weak ahead. Meanwhile, the farmers who have acquired loans are gathered in a WeChat Group, making it convenient for the company to issue notices and press for loan repayment.

2.5.5 Risk Control System Risk control is the subject of supply chain finance. In Wenshang Pioneer Loan program, JD Finance intends to establish a risk control system based on the Internet risk control capability of JD big data with the guarantee of Shandong Daliang as the core part of credit enhancement. However, in view of the limited data accumulated during the pilot program period, it is premature to discuss the risk control of supply chain finance based on big data and risk control model. The core of risk control now rests in the full-amount guarantee provided by Daliang and other corresponding insurance measures. The full-amount guarantee

7 Data

from Jining Daliang.

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means that Daliang undertakes to bear all default risks of farmers, so Daliang shall adopt a series of safeguards to spread risks. Currently, Daliang’s measures of risk spreading embody the characteristics of multiple guarantees. To acquire loans, farmers must provide “three-household joint guarantee”, account manager’s guarantee and insurance (personal accident insurance with Daliang as the ultimate beneficiary). The specific workflow is as follows: Jining Daliang receives the customer’s materials, and the account manager plays as a guarantor for the farmer’s loan8 ; Daliang requires the farmer submitting the materials related to three-household joint guarantee; the materials are submitted to Shandong Daliang for review, and if any issue discovered, Shandong Daliang will inform Jining Daliang; when the farmer applies for JD Agricultural Loan, the account manager shall tell the farmer to purchase agricultural insurance and personal accident insurance (with Daliang as the beneficiary of the personal accident insurance) to resist any risk arising in agricultural production; and Daliang shall sign the grain sale agreement with the farmer when selling seeds, which specifies that the grains produced by the farmer will be purchased by the company in the future. Hence, Daliang’s purchase order for grains can be considered as the guarantee for this loan. This multi-link guarantee has spread the risks of Daliang and JD. However, the problem is that whether such guarantee can be effectively implemented considering many links in this process. For example, the account manager bears no obligation of mortgage repayment for the loan, but only assumes certain responsibility according to the agreement. Therefore, the so-called account manager’s guarantee for debts may be just a talk. For another example, the “three-household joint guarantee” required from farmers is not as strict as the commercial banks’ requirements on joint-guarantee users. In the multiple-link guarantee, the account manager guarantee and three-household joint guarantee are not so secure or reliable. From the perspective of Daliang, the agricultural insurance and personal accident insurance purchased by farmers

8The research team discovers that though it is provided that sales representatives of the enterprise bear the guarantee responsibility, they assume certain responsibility for the loans and the default punishments on them are not clarified.

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are of greater practical significance; and for JD, what’s more practically important is the full-amount guarantee of Shandong Daliang.

2.5.6 Pilot Program Evaluation Eight months have passed from the release of Wenshang Pioneer Loan product to the end of the case study. Though this is a short period, we can clearly see how Wenshang Pioneer Loan has brought convenience to farmers’ loans and helped the grain planting supply chain. By evaluating the results of the pilot program of Pioneer Loan, we have summarized six achievements as follows. First, it significantly improves the convenience of farmers’ borrowing, mainly manifested by three points. First, fewer materials are required for loan application. The materials that farmers shall submit include the application form, farmers’ basic information, asset information and bank credit information (not required if no such information). The formalities are much simpler than that of RMB 100,000 loans released by village banks and rural cooperatives. Moreover, the account managers of Jining Daliang provide door-to-door services for credit business introduction, preparation of application materials and material delivery etc., significantly reducing farmers’ burdens. Second, the time of approval is shortened. As mentioned above, the whole process from application submission to receipt of the loan takes only about one week, compared to about one month needed for local traditional banks or rural credit cooperatives. Third, the loan review is simple and transparent. Loan review over the materials submitted is simpler in Wenshang Pioneer Loan, and farmers do not need to exploit the connection, give feasts or send gifts to accelerate the loan approval, which is commonly seen in traditional rural credit. This is what farmers feel satisfied with. Research Note “Farmers Generally Feel that It Is More Convenient to Acquire a Loan Now”. The situations with farmers in Wenshang County are diverse. Different farmers vary in their planting areas, operation modes and risk preferences. However, they use JD Agricultural Loan mainly for the same

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reason: convenience. To further understand the effect of Pioneer Loan, we have interviewed four representative farmers, respectively Boss Zhang, Teacher Hu, Li Changgui and Tian Xiancai. Boss Zhang is a famous family farmer in Wenshang County. His farm has developed rapidly in recent three years, from 583 mu in 2013, 1,300 mu in 2014 to 2,000 mu by now. His success is closely associated with the high-quality seeds provided by Daliang. He has cooperated with Daliang for four years, when Daliang has purchased most of his wheat and determined the price based on the demands of flour mills and relevant prices, generally 2 jiao higher than the market price. Boss Zhang stated that he chose Wenshang Pioneer Loan largely because of its simple application procedures. The time spent on loan application was of great significance, especially for agriculture that stressed on timeliness. Boss Zhang had also received the follow-up call from JD and offered his suggestions. Teacher Hu is a teacher of grape planting. He is diligent and honest, but he cannot acquire a loan from the bank due to other farmer’s default in his five-household joint-guarantee loan from the cooperative. After having acquired a loan from JD Agricultural Loan, he told us about his story of loan delightedly. He appreciated the simple procedures and low guarantee requirements of Wenshang Pioneer Loan, which solved his financing difficulty that had lasted for years. He also used the Pioneer high-quality wheat seeds managed by Daliang for many years and trusted the company very much. Li Changgui is a veteran. After he was demobilized and returned from the troop last year, he began to help his father manage agricultural production in his own family farm. He has been interviewed by several media, but he still presented a happy and excited face when talking about Wenshang Pioneer Loan. As a youth just demobilized, he was familiar with the Internet and JD, so he knew that JD was a credible large enterprise and applied for Wenshang Pioneer Loan without any hesitation. He said that the high-quality wheat seeds of Pioneer earned him 200 yuan more for each mu of land. He had used Pioneer seeds in most of his fields, but after acquiring Wenshang Pioneer Loan, he changed all his seeds into Daliang seeds. He also expressed that he hoped that the

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seed company could increase his loan limit so that he could do better in agricultural production. Tian Xiancai is a very conservative farmer who does not want to take too much risk. He did not apply for a loan even in shortage of funds. His 80-mu field has not expanded for three whole years, which is not commonly seen in Wenshang County of rapid land circulation. He started to use JD Agricultural Loan because of its rapid loan release and simple procedure for application. The loan had been leased in five days, having solved his pressing need. He also stated that he would not increase his loan even if his credit limit was improved, since it was better for farmers to be more conservative. Wenshang Pioneer Loan Case Study Team, May 20, 2016. Second, the consistency between the loan cycle and the production cycle improves the use efficiency of the loan. Currently, Wenshang Pioneer Loan provides two kinds of agricultural material loans, respectively of wheat and corn seeds. The terms of the two kinds of loans are nine months and four months respectively, completely consistent with the production cycles of wheat and corn from sowing to harvesting. Agricultural production is seasonal and cyclical. Farmers need to purchase seeds, chemical fertilizers and some other agricultural materials in the early stage, constantly invest on the cultivation and maintenance of crops during the growth period and even apply agricultural machinery to sowing, pesticide spraying and harvesting for a large area of planting. Crops will be harvested only after these investments, and the cost can be covered only when crops are sold. JD Agricultural Loan adopts the method of monthly interest repayment and principal repayment at maturity, and the principal is over recovered when the returns from agricultural production have been obtained. This repayment method not only guarantees that farmers can repay the loans with their incomes from production and lighten their financial burdens, but also avoids any extra interest caused by the idle borrowing if the borrowing cycle is longer than the production cycle and therefore lowers the interest cost. Wenshang Pioneer Loan is a form of supply chain finance that is really rooted in farmers’ production scenarios. It provides financial services that are really suitable for agricultural production and assures that the loans are used for

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the specified purpose. To sum up, Wenshang Pioneer Loan is professional and intensive rural finance of controllable process and measurable risk. Third, it develops credit business through local account managers, which can lower the business cost and meanwhile effectively reduce information asymmetry. Business promotion of Wenshang Pioneer Loan mainly depends on account managers of Jining Daliang, who are responsible for selling agricultural material products including seeds to farmers and providing assistance in farmers’ loan application as per farmers’ borrowing demands. According to the common practice of Jining Daliang, account managers must be locals of Wenshang, Jining, have working experience related to agricultural production and agricultural product purchase etc., have accumulated rich agricultural production experience and know local farmers well. They shall live long in rural areas, maintain frequent contacts with farmers and have a direct understanding of loan applicants’ personal credit and family status and production and management capability. As the first reviewer over the farmers’ credit qualification, account managers can effectively reduce the information asymmetry between Daliang and farmers and thus lower the credit risk of supply chain finance. Since account managers work concurrently as “loan officers” while promoting sales of seeds, JD and Daliang have not employed full-time loan officers for farmers’ loans. By doing this, the credit quality can be better guaranteed and the operating cost can be saved. Fourth, it lowers farmers’ default risks by “special funds for special use” and “purchase commitment”. First of all, the operation process of Wenshang Pioneer Loan assures that the farmers’ loans (in forms of agricultural materials) are specially used for agricultural production, preventing farmers’ default risks of using the borrowing to other purposes. Second, Daliang lists its undertaking to purchase farmers’ grains in the contract attached to the loan, suggesting that farmers grain incomes are guaranteed. Unless a poor harvest is caused by a natural disaster, farmers are protected from the risks arising from grain market fluctuations as guaranteed by grain purchase by Daliang. The reduction of operation risks evidently lowers farmers’ default risks. Fifth, the whole-industrial-chain services of Daliang guarantees the good production quality and reduces farmers’ defaults risks arising from

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production failures. According to President Wang of Jining Daliang, Jining Daliang positions itself as “a service company covering the whole industrial chain of agricultural planting”. Its services include: selling seeds (high-quality wheat seeds and Pioneer corn seeds) before production, providing technical guidance on production and maintenance services during production, and assigning special personnel to help farmers harvest crops and offering sales channels. Based on the whole-industrial-chain services, it guarantees the quality of seeds in the early stage, assures smooth agricultural production in the middle stage and enables farmers’ incomes in the later stage. Being a wholeindustrial-chain service company of grain planting, Jining Daliang has helped farmers’ production throughout the different stages of agricultural production, carried out whole-process quality control and lowered farmers’ operation risks. Therefore, it fundamentally guarantees farmers’ grain sales, assures their repayment ability and reduces their default risks.

2.5.7 Future Plans By the end of May, Wenshang Pioneer Loan has initiated the program of agricultural material loan for wheat production between October 2015 and June 2016. The initial product of Wenshang Pioneer Loan was consistent with local farmers’ production cycle and basically realized the expectation of “satisfying farmers’ fund demand for the agricultural production expansion and increasing farmers’ output and incomes”. Since June 2016, Wenshang Pioneer Loan will launch the agricultural material loan for corn production that lasts for four months, thus to further promote the field loan programs and provide funds to farmers expanding their contracted fields. Though Daliang is cooperating with an e-commerce giant to launch the supply chain finance program, it has not made full use of JD’s e-commerce platform. Most wheat and corn of Daliang are still sold through its own marketing channels. Among the corn sold by Daliang, 30% are used as raw materials for industrial production, 60% are used to produce feeds, and only 10% are eaten. Wheat is mainly applied to the production of flour. However, the sales volume of either edible

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flour or wheat flour on JD’s e-commerce platform is still small. In the future, Daliang and JD both intend to give better play to the e-commerce platform as a sales channel.

Case Comments Wenshang Pioneer Loan of JD Finance has taken the first step to explore the e-commerce + supply chain finance model. While following JD Finance to visit Jining Daliang and the grain growers in Wenshang County who cooperated with Daliang, we have discovered the exciting achievements and highlights of this model as well as some problems or doubts. Our problems and thoughts are summarized below:

How to Give Play to the Role of E-Commerce in Field Grain Planting Supply Chain? Supply chain finance is not innovation. Rural supply chain finance in China has achieved rapid development since 2007. Some traditional financial institutions engaged in rural finance business such as the Agricultural Bank of China, China Development Bank and Longjiang Bank have made great achievements in this sector. It was only since the latter half of 2015 that e-commerce joined rural supply chain finance, when Ant Financial and JD Finance had released specific programs. Though e-commerce just started out, we expect that involvement of e-commerce will lead to a new round of innovation of rural supply chain finance, mainly manifested by the value increment of rural supply chain brought by e-commerce platforms. In this respect, Wenshang Pioneer Loan is still in the process of exploration, as the e-commerce platform has barely played its part in the sales of grain planting supply chain in Wenshang County. This may be because of the difficulty to sell grains as food on the e-commerce platform or the sales channel of JD. Nevertheless, cooperative partners of Wenshang Pioneer Loan share the same idea about its future development plan, and will jointly strive to explore the contributions of e-commerce to Wenshang grain supply chain.

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JD had released the loquat supply chain and corresponding financial service JD Agricultural Loan—Renshou Loan in Renshou County of Sichuan at the end of 2015. This program had given full play to the role of JD e-commerce. In this program, JD Finance cooperated with Sichuan Renshou Furenyuan Agricultural Development Co., Ltd. Since Furenyuan Company’s products such as loquat juice had stable sales in JD Mall, JD incorporated the products in its self-support channels considering their controllable demand, and, on this basis, sought for upstream partners in the supply chain and made use of its financial advantages to improve the supply chain network. In the loquat festival held by JD in May 2016, various activities were carried out including coupons, 1-yuan wealth management and special snack shop etc., bringing into full play the marketing channels of e-commerce. However, an unsolved problem faced by JD and other e-commerce companies lies in how to give full play to e-commerce’s advantages in resource integration, organizational coordination and resource sharing to contribute the special values of e-commerce to grain supply chain finance and promote the scaled operation of grain production. We expect that JD Pioneer Loan pilot program can make contributions in this respect.

Is There Any Problem with the Account Manager Recommendation System? This case study gives positive comments on the account manager recommendation system in Wenshang Pioneer Loan, but still has some doubts about it. One point is that the account manager has too much power. In the pilot period of Wenshang Pioneer Loan, JD offered a credit line of RMB 20 million to Daliang, while Daliang provided the farmer recommendation right to account managers for this credit of RMB 20 million. Daliang completely delegated the power to account managers, as they knew the situation of farmers the best in the whole supply chain finance. However, the too concentrated power would leave the space for rentseeking. For example, account managers might practice favoritism and commit irregularities with the loan recommendation right in exchange for personal interests, which severely affect the loan quality.

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To inspire account managers, Daliang associates their remunerations with the sales of seeds. Apparently, the number of loans will directly affect the sales of seeds. Considering this, a large number of farmers should have been recommended. However, the actual results show that the loan size has not rapidly expanded, and by the end of May 2016, Daliang has released loans of RMB 10 million. This situation proves another constraint on account managers’ impulse to release loans. By examining the system of Pioneer Loan, we find that the provision on account managers’ guarantee responsibility for the loans may constitute another constraint. In view of this provision, account managers must review every loan repeatedly to prevent any loss incurred by any careless mistake in loan release. If this is true, the account manager guarantee system is proved to be rational. However, every coin has two sides. If account managers are too cautious about loan recommendations because of their guarantee responsibility, some farmers of good credit who need borrowing for development may miss the opportunity. Hence, a question worth pondering is how to design the incentive and constraint mechanism to make best use of the advantages and bypass the disadvantages of the account manager guarantee system. The risk control system of Wenshang Pioneer Loan fundamentally aims to utilize the massive user data accumulated by Daliang and establish the risk control model based on the current Internet risk control capability of JD big data. It suggests that despite account managers’ recommendation right, the final decisions are completely made by JD. In this way, the risk control performed by JD and the strict management of Daliang jointly constitute the double insurance that prevents power rentseeking. In the future, JD’s risk control system based on digital information technology and data accumulation will be increasingly powerful. Account managers and similar roles will become service providers, and the possibility of rent-seeking will be eliminated fundamentally.

Advantages of Digital Information Technology? The risk control model of JD Agricultural Loan—Pioneer Loan adopts the traditional multiple-level review and mainly depends on the review

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and monitoring of the core enterprise, Daliang Company. JD has not participated in review or real-time monitoring due to JD Finance’s limited human resources. However, if giving full play to its advantage in digital information technology, JD can join the whole process synchronously. At present, in JD Agricultural Loan—Pioneer Loan, the digital technology is only applied to the optimization of the application process (material submission online) and convenient repayment, and shows no effect on the mining and application of rural big data. We believe that with the constant operation of the program and the accumulation of farmers’ information, JD can change its “marginal role” in the credit process and get involved more depending on its digital information technology. It will not only practice real-time monitoring, but also participate in pre-loan review based on the accumulated information on farmers’ behaviors. JD Finance is establishing its rural credit information system and rural Internet information system, in order to realize online risk control through data processing. At present, JD Finance cooperates with Tuliu Net and other data information institutions, mine and process farmers’ credit and consumption information based on the records of service providers, product providers and JD Mall and integrate data and information in the whole industrial chain, thus to give full play to e-commerce platforms in supply chain finance.

How to Think About the Phenomenon that Pioneer Loan Fails to Benefit Small Farmers? In the grain planting supply chain of Jining Daliang, Wenshang Pioneer Loan chooses to provide loans to middle or middle-small farmers with a certain planting area and reject growers with a planting area smaller than 50 mu in principle. On one hand, small farmers have small demands for funds; and on the other hand, they are not stable customers of Daliang. Therefore, small farmers cannot benefit from this supply chain finance. How do we think about this phenomenon? If this is a common phenomenon, does it mean that rural supply chain finance

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or e-commerce and rural supply chain finance is not a part of inclusive finance? Is supply chain finance playing a limited role in supporting farmers at the bottom level? It is true that rural supply chain finance mainly helps middle and middle-small farmers if it establishes direct connections with farmers. The support to these farmers of a certain scale, similar to small and medium-sized enterprises in cities, also constitutes an important part of inclusive finance. To serve small farmers, the supply chain finance can cooperate with production cooperatives, which may be the best channel for rural supply chain finance to support small farmers. In the e-commerce + rural supply chain finance model, with the help of e-commerce, the rural supply chain of certain products will expand rapidly and inspire an increasing number of farmers to join the supply chain. Therefore, from the perspective of development, the e-commerce + rural supply chain finance model benefits farmers of all levels to varying degrees. Commentator: Yan Li

References JD Executives Gathered in Gu’an Rural Area, Liu Qiangdong Proposed Rural Ecommerce “3F” Strategy, (2015 April 21) [2018 January 31]. http://finance. ce.cn/rolling/201504/21/t20150421_5170901.shtml. Liu Qiangdong Proposed Rural E-commerce “3F” Strategy, (2015, April 21) [2017, December 15]. http://news.xinhuanet.com/tech/2015-04/21/c_1277 15700.htm. Lu Qihui, Li Fei, Zhou Weihua, Decision-making Analysis and Value Research on Supply Chain Receivable Financing, “Journal of Management Sciences in China”, 2012, 15(5):10–18.

Part IV Credit

Credit is an important force that maintains the order of people’s social and economic activities. In particular, a trader’s credit quality decides whether a financial transaction can be concluded. To judge the credit quality of a financial trader, the reviewer will get to know the trader’s previous transactions and current economic and social behaviors and estimate the trader’s possible performance in the future on this basis. Hence, the most important point is to master the information that reflects the trader’s credit status. Almost 2/3 workloads of financial transaction are about collection, analysis and evaluation of customers’ information. Professional credit institutions can help financial institutions acquire relevant customers’ credit information conveniently. In view of the characteristics of inclusive finance’s target customers, inclusive finance is more dependent on customers’ credit information than traditional finance, but has less available customers’ credit information for the same reason. For example, small and micro enterprises and low-income groups had few lending behaviors previously, providing very limited observable credit information. Apparently, for the sake of the development of inclusive finance, an important goal of infrastructure construction is to improve the credit analysis and evaluation system for inclusive finance customers.

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This part consists of two cases, which conducts in-depth discussions on how to promote the construction of social credit system relying on digital technology.

9 “Credit Use” and Cost of Discredit: The Supreme People’ Court and Zhima Credit Cooperating to Punish “Lao Lai” Yan Li, Lin Wang, Jianxiao Su, Yuqin Tang, and Puning Wang

1

How to Solve the Issue of “Lao Lai”?

For a long time, after the court judgment comes into force, quite a few debtors do not conscientiously fulfill their obligations, and even adopt Y. Li (B) · J. Su · Y. Tang · P. Wang Renmin University of China, Beijing, China e-mail: [email protected] J. Su e-mail: [email protected] Y. Tang e-mail: [email protected] P. Wang e-mail: [email protected] L. Wang Beijing University of Posts and Telecommunications, Beijing, China © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_9

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various means to resist the execution.1 These people not only damage creditors’ legal rights and benefits, but also impair the majesty of the law. The issue of so-called “execution difficulty” has arisen therefrom. To improve the execution efficiency, the Supreme People’s Court issued the Provisions on Publicizing the List of Dishonest Debtors,2 which provided that the list of dishonest debtors (commonly known as “Lao Lai”) should be publicized to the whole society. According to statistics, by the end of 2016, China has had approximately 3.26 million “Lao Lai”. Only in 2016, “Lao Lai” increased by 4,735 every day on average, while only 975 “Lao Lai” fulfilled their obligations every day on average.3 The fundamental reason for the emergence of a large number of “Lao Lai” is the too low cost of discredit to urge them to regulate their own behaviors conscientiously. In order to increase the cost of discredit of “Lao Lai”, law enforcement shall be further strengthened, while the effective closed loop of “credit collection – credit evaluation – credit use” shall also be established within the social credit system. This effective closed loop suggests that not only personal behavior information shall be recorded and evaluated in people’s social life, but also rewards and punishments shall be given based on credit evaluation. By doing this, whenever “Lao Lai” are found dishonest somewhere, they would be restricted everywhere. By increasing the cost of discredit, Lao Lai will be impelled to fulfill their obligations. The more scenarios that impose restriction on Lao Lai and the stricter restriction will also raise the cost of discredit. 1 According

to incomplete statistics, in the court cases where the person subject to execution had property and the execution was completed between 2008 and 2012, more than 70% of the persons subject to execution had escaped, evaded or even violently resisted the execution, and fewer than 30% conscientiously fulfilled their obligations (Gong Yunju and Yu Xianghua, 2015). 2The people’s court shall incorporate in the list of dishonest debtors the person subjected to execution who has the ability but reject to perform the obligations specified in effective legal documents and has one of the following situations: impede or resist execution by evidence falsification, violence or threat; evade execution by false litigation, false arbitration or property concealment or transfer; violate the property reporting system; violate the high consumption restriction order; refuse to fulfill the execution of settlement agreement without justification; and other cases where the person subjected to execution has the ability but reject to perform the obligations specified in effective legal documents. 3This result is obtained based on the analysis of data in the list of dishonest debtors published by the Supreme People’s Court.

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American credit system has set a good example in this respect. First, as to credit collection, American law guarantees judicial publicity and government information publicity, and the government offers basic data to credit institutions with compensation. With the government’s support, the credit institutions possess an extensive source of basic data, which significantly improves the efficiency and accuracy of “information collection” and lowers the credit transaction cost (Xiong Xueping 2011). Second, from the perspective of credit evaluation and credit use, the three major credit institutions in the US,4 based on information collection and data standardization, on one hand grade users’ credit with their own credit evaluation models, and on the other hand actively expand the scope of use of credit reports. They provide multiple credit products to various organizations, maximize the value of credit and form the cycle of “credit collection – credit evaluation and credit use”. In this process, an increasing number of commercial organizations regard the credit report as a necessary condition for customer evaluation. The government also encourages the whole society to participate in credit rating and use the credit rating results (Zou Hao 2006). By now, these three credit institutions have established the industrial chain of “credit collection (and data standardization) – credit evaluation and credit use”, covering 85% population of the whole America. Besides, some new credit enterprises are developing rapidly. For example, Zest Finance, founded in 2009, has made use of its big data technology to evaluate the credit of the remaining 15% population that is not covered by the traditional credit system and effectively covered the shortage of the traditional credit system. In a highly advanced credit system, enterprises and individuals in the US all attach great importance to maintaining their credit, and the credit profile is regarded as the “second ID card” by Americans. Dishonest individuals or enterprises cannot do anything in social life. They can hardly acquire any bank loan or credit card service, or even they may find it difficult for them to find a job, rent a house or go to school etc. Therefore, “execution difficulty” is not a social problem in the US.

4 Experian,

Trans Union and Equifax.

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Compared to the US, China’s credit system construction is still in the initial stage. The industrial chain of “credit collection – credit evaluation – credit use” has not been formed, and even the credit data are scattered in every corner of society but cannot be effectively connected or integrated. Though the People’s Bank of China had established the credit center as early as in 2006, the system has very limited data and scope of use (Sun Ya et al. 2008). Information isolation has severely impeded the credit system from playing its significant social value. However, the good news is that this situation is being gradually improved in the efforts of social credit institutions. In 2015, Zhima Credit, the third-party credit institution under Ant Financial, signed the Memorandum of Cooperation on Credit Punishment of Dishonest Debtors with the Supreme People’s Court. Since then, both parties have started their cooperation to explore the joint punishments on “Lao Lai”. The Supreme People’s Court provides the information of “Lao Lai” to Zhima Credit, while Zhima Credit synchronizes the data, assigns negative information prompts to “Lao Lai”, lowers their Zhima Credit rating, restricts them in the services provided by partners to Zhima Credit, increases their cost of discredit and urges them to fulfill obligations. By doing this, Zhima Credit collects credit information from “Lao Lai” database of the Supreme People’s Court, evaluates the credit of “Lao Lai” and restricts the credit use of “Lao Lai”. It has established the chain of “credit collection – credit evaluation and credit use” and increased the cost of discredit of “Lao Lai”. By the end of 2016, Zhima Credit has incorporated approximately 720 thousand “Lao Lai”, among whom 128 thousand have fulfilled their obligations, accounting for 17.75% of the total number of Lao Lai. This ratio is 5% higher than the ratio of Lao Lai who are not covered by Zhima Credit,5 showing the initial effect of the punishment. To reduce actors’ opportunistic behaviors in social and economic life, the corresponding contract governance mechanism shall be applied to guarantee the effective execution of contracts. Scholars generally divide

5 Calculated

based on the data provided by Zhima Credit.

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the contract governance mechanism into the law-based open governance mechanism and reputation-based private governance mechanism (Macaulay 1963; Grief 2003; Johnson et al. 1999; Zhang Weiying 2002) and further discuss the interactions between the two (Uzzi 1997; Ellickson 1991). In this case, the legal judgment on “Lao Lai” is the constraint of the legal mechanism, and the credit collection, credit evaluation and credit use of “Lao Lai” performed by Zhima Credit gives play to the governance role of the reputation mechanism. Legal judgments have caused certain cost of discredit of “Lao Lai”, while credit institutions convey the information of “Lao Lai” to various credit use scenarios in social life to restrict their activities, magnify their cost of discredit and effectively improve their willingness to fulfill obligations. In this case, we have observed the support of the reputation mechanism to the legal mechanism. Based on the punishment of the legal mechanism on dishonest debtors, the reputation mechanism further increases the cost of discredit through the information communication promoted by credit institutions. The study on this phenomenon is of both theoretical and practical significance. On one hand, most of current studies set the legal mechanism and reputation mechanism against each other and consider that the two show a relation of mutual substitution or mutual independence (Ghoshal et al. 1996; Uzzi 1997; Bernheim et al. 1998). However, we discover that the reputation mechanism can support and complement the legal mechanism in practice. On the other hand, from the practical perspective, China’s credit information is now exposed to severe data isolation.6 All sides of society shall jointly discuss how to further develop the social value of the credit system, enhance the coordination between credit activities and other governance mechanisms, increase the cost of discredit of dishonest people and improve the credit value of promise keepers. Therefore, the case of Zhima Credit and the Supreme People’s cooperating to punish “Lao Lai” provides research materials for relevant theoretical exploration and meanwhile sets a practical example for reference.

6 Bei

Duoguang, Good Finance, Good Society—Report on Inclusive Finance Development in China (2015), Beijing; Economy & Management Publishing House, 2016.

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In view of this, this case study first compares the credit system construction at home and abroad and points out current problems of China’s credit system as the background for research; then introduces in details and analyzes the case of Zhima Credit and the Supreme People’s Court cooperating to punish “Lao Lai”; and finally summarizes this case.

2

Development Status of Credit Industry in China

Credit constitutes the foundation of sharing economy. A complete credit system shall comprise a closed loop of credit collection, credit evaluation and credit use, by which the credit system collects personal credit information, processes and evaluates such information and delivers it to more scenarios. By doing this, it can increase the cost of discredit, punish the dishonest, urge them to fulfill their obligations, bring convenience to promise keepers and therefore cultivate a virtuous cycle. However, compared to the mature credit industrial chain in foreign countries, many problems still exist in the development of China’s credit industry and construction of credit system. First, we have not established a complete legal system, but we are making constant improvements. The development of credit industry first requires the support of legislation. Before 2012, the laws related to the credit business in China were scattered in different laws including the Law on Commercial Banks and General Principles of the Civil Law, so the credit industry was all in a mess (Tang Mingqin 2010). In 2012, to normalize credit activities, protect relevant parties’ legal rights and benefits, guide and promote the healthy development of credit industry and advance social credit system construction, the State Council issued the Regulations on the Administration of Credit Industry. Consisting of eight parts such as the general principles, credit institutions and credit business rules etc., the Regulations provided the basic law that the credit industry should observe. Afterwards, the State Council released the Administrative Measures for Credit Institutions and Outline of Social Credit System Construction Planning etc. successively, reflecting the government’s determination to promote and normalize the development of the credit

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industry. However, these laws are not refined and explicit enough and have some ambiguities due to the restriction of market development. For example, no explicit provisions are given on the collection, processing and disposal of big data credit. Therefore, relevant departments shall further refine and improve credit laws and regulations to guarantee the healthy development of the credit industry. Second, the high information barrier hinders the development of information sharing. Data is the foundation for the development of the credit industry. Western governments have advanced data disclosure and transparency from the top. However, in China, enterprises at the bottom level can hardly acquire data from the top. Data acquisition has become one of the biggest difficulties for the development of the credit industry. The People’s Bank of China had established the credit center in 2006, which mainly focuses on banks’ credit information and also includes public information on social security, housing fund, environmental protection, tax arrears and civil adjudication and execution etc. By the end of April 2015, the credit system had collected information of more than 860 million natural persons and about 20.68 million enterprises and other organizations. However, this system is relatively closed and does not provide data to the credit industry. Moreover, the valuable credit information in China is mostly controlled by the industry and commerce, customs, courts, public security, statistics and quality inspection department etc. (Wang Jingwei 2009). These departments of the same hierarchy do not share information due to administration cause and multiple other causes, not to mention provide data to credit enterprises. Therefore, “isolated data islands” become commonly seen. The units which demand credit records also establish their credit database independently, resulting in the waste of resources and repeated construction. The government’s monopoly of information and the difficulty in information sharing have severely impeded information collection by credit enterprises. Third, people lack the concept of honesty and have a weak credit awareness. The underdeveloped credit transaction market in China leads to the weak credit awareness of society. By the end of 2016, A total number of 3.26 million Lao Lai still refused to perform the judgment

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after the court ruled, which not only reflected their contempt for judicial authority, but also proved the weak credit awareness of society. The root cause still lies in the lack of discredit punishment mechanism or vigorous sanctions and the low cost of discredit. In this case, Lao Lai leave things to chance in the hope that they can escape the execution. Furthermore, the dishonest may even benefit from being dishonest and promise keepers may suffer loss in some cases. In this context, “bad money drives out good”, further worsening the credit environment. This is also because of the broken credit system of China, which fails to deliver information of dishonest persons to every respect of society, restrict their social activities or increase the cost of discredit. In addition, in view of the imperfect credit system and the public’s superficial understanding of credit, the public do not take the initiative to upload credit information, accumulate credit records or even use credit reports, which also significantly restricts the development of the credit industry. Fourth, credit evaluation shall be further normalized and credit can be used in only a few scenarios. At present, the credit industry has no relatively authoritative standards for credit scoring. Different enterprises collect data from their own channels, and the source, accuracy and objectivity of such data can hardly be judged. Moreover, there is no uniform regulation on this industry, as the industry is subject to the common supervision by the People’s Bank of China, China Securities Regulatory Commission (CSRC) and the tax bureau etc. (Sun Jianchao 2006). The regulation methods and standards of these departments are not exactly the same. Moreover, since China’s credit industry is still in the initial stage of development, not many mature credit products have been released as in western countries. How to and where to apply credit data are still an urgent problem of the credit industry. From the perspective of information users, individuals and enterprises do not actively accumulate credit information or take the initiative to seek and use credit results due to their weak credit awareness. Therefore, the current market is still small in size. Credit enterprises shall actively explore and create demands. To sum up, from the aspect of the whole industrial chain of credit system consisting of “credit collection”, “credit evaluation” and “credit

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use”, the closed information of Chinese government gives rise to “isolated data islands” and impedes the “credit collection” by credit enterprises; multi-department regulation and non-uniform standards undermine the comprehensiveness and objectivity of “credit evaluation”; and the lack of credit awareness of society and scenarios where credit can be used make “credit use” difficult. Information of the dishonest cannot be delivered to more people and only a few scenarios can make use of such information to restrict and punish the dishonest. In this way, the cost of discredit cannot be increased, and therefore the credit awareness will become weaker. However, credit enterprises can strive to connect the fragmented links, collect and process information and deliver it to people in need, and finally promote the formation of the complete industrial chain.

3

Cooperation Between Zhima Credit and the Supreme People’s Court

To effectively control “Lao Lai” phenomenon, the Provisions of the Supreme People’s Court on Restricting High Consumption of Judgment Debtors (hereinafter referred to as High Consumption Provisions) was released in 2010. To adapt to the general trend of the development of information society and further solve the “execution difficulty”, the Supreme People’s Court promulgated the Provisions on Publicizing the List of Dishonest Debtors in July 2013, established the database of courts’ lists of dishonest debtors and issued the high consumption restriction order to all dishonest debtors in the list database in January 2014. The High Consumption Provisions enumerate nine kinds of high-consumption behaviors, including the restriction on taking airplane, soft-berth train carriage and ship cabin above second class for transport. On July 21, 2015, the Supreme People’s Court published the Decision of the Supreme People’s Court on Revising the Provisions of the Supreme People’s Court on Restricting High Consumption of Judgment Debtors, which imposed stricter restrictions on consumption and incorporated the consumption restrictions on dishonest debtors from all types of seats of trains initiated with the letter G and seats of Class 1 or above in other bullet trains.

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This judicial interpretation has come into force since July 22, 2015. In November 2016, the General Office of the CPC Central Committee and the General Office of the State Council announced the Opinions on Accelerating the Construction of Credit Supervision, Warning and Punishment Mechanism for Judgment Debtors. As mentioned above, though the Supreme People’s Court takes the initiative to publish Lao Lai’s information and becomes aware of the supporting role of daily-life scenario punishments, the effect is still poor since the current legal system is incomplete. Not many Lao Lai are covered, so the deterrent to Lao Lai is not powerful enough. In order to make use of the reputation mechanism to constrain Lao Lai in more extensive life scenarios, the Supreme People’s Court hopes to establish exploratory cooperation with social credit institutions and relies on the forces of credit and social credit use to increase Lao Lai’s cost of discredit. Along with the unprecedentedly rapid development of Chinese economy and society, domestic personal credit transactions are expanding, including bank loan and consumption finance as well as car renting, house renting, accommodation, book borrowing and other daily life affairs. The market is proposing increasingly diversified demands on credit products and services. Credit not only influences people’s financial activities in traditional finance, but also shows its presence in every aspect of social life. As the basic credit data and services provided by the credit center of the Central Bank can no longer satisfy social and economic demands, more active forces are needed in this sector. At present, as guided by several national policies, the commercialization and marketoriented development of personal credit has commenced in China. Eight commercial credit institutions are attempting to enter the personal credit field and depending on digital technology and Internet economy to explore and innovate credit models. According to Zuo Zhuan (i.e. Zuo’s Commentary on the Spring and Autumn Annals), “A man of noble character shall be honest and speak on the basis of fact, so he keeps away from grudges”. Along with the rapid development of the credit industry, “credit” has received more and more attention from social public. The “credit” industry is full of vitality. In January 2015, Zhima Credit Management Co., Ltd. of Ant Financial Services Group under Alibaba had officially launched its personal

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credit system, Zhima Credit. As a legal and independent organizations specialized in credit evaluation and credit management, Zhima Credit Management Co., Ltd. released Zhima Credit, a credit service system serving whole society. Zhima Credit collects personal credit information, evaluates personal credit based on big data and cloud computing technology and cooperates with several financial and daily-life scenarios including credit card, consumption finance and car renting etc. and various service platforms to offer credit services to users and merchants. The commercial operation of Zhima Credit enables users to experience the value of credit and also establishes extensive credit reach with rich content. Distinguished from traditional practice that credit is simply used in banking and financial businesses, Zhima Credit expects to base on its commercial strength, learn successful experience from foreign countries and create the complete closed loop and industrial chain of multiscenario “credit collection – credit evaluation – credit use”. Zhima Credit holds that credit collection, credit evaluation and credit use are indispensable from and promote each other. The normative process of credit collection and evaluation increases credit use, the more frequent credit evaluation and use improves the demands for credit collection, and credit use and collection propose higher requirements on credit evaluation. The more cycles of credit collection—credit evaluation—credit use prove the better credit quality, the higher value of credit and the stronger credit outlook of society. Hence, Zhima Credit also hopes to cooperate with the Supreme People’s Court, in order to collect Lao Lai’s information, connect the isolated data islands, apply such information to more life scenarios, promote credit use and achieve the complete closed loop of “credit collection – credit evaluation – credit use”. In view of both parties’ common demands for cooperation, the Supreme People’s Court and Zhima Credit reached agreement easily and determined the cooperation program quickly. On July 24, 2015, the Executive Bureau of the Supreme People’s Court and Zhima Credit signed the memorandum and officially decided to cooperate in punishing dishonest debtors. This program is essentially that Zhima Credit makes use of the reputation mechanism to complement the legal mechanism, punishes Lao Lai with “credit collection – credit evaluation – credit

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use” and magnifies the cost of discredit. This is exploratory cooperation for both the Supreme People’s Court and Zhima Credit. Through this cooperation, the Supreme People’s Court is exploring how to combine social credit forces to strengthen the punishment; and Zhima Credit is attempting to connect isolated data islands, realize the closed loop of credit and develop the role of credit activities in social governance. If the cooperation results turn out to be good, the Supreme People’s Court will standardize the cooperation and further expand the scope of cooperation; and Zhima Credit will collect more extensive credit information, cooperate with more information-source institutions and give better play to the role of social credit institutions in social governance.

3.1

Credit Collection—Integration of Big Data

“Credit collection” means to collect and summarize the information of respondents. Zhima Credit collects information based on Internet economic ecology and data information technology and effectively complements China’s traditional credit system. First of all, Zhima Credit possesses massive Internet/mobile Internetbased information of individuals and small and micro enterprises. In the respect of the quantity, the total number of Chinese netizens has reached 721 million7 at present, while the number of real-name users of Alipay8 has reached as high as 450 million.9 In the aspect of information content, Zhima Credit covers more extensive types of data, including both traditional offline static data and online dynamic data, and both data of Alibaba and data from other departments (such as tax, industry and commerce and banks etc.). Hence, it is characterized by a large amount of data, rich data content and “fresh” data. Zhima Credit mainly acquires data from four major channels. First is the e-commerce data of Alibaba, including Tmall, Taobao and Cainiao Network etc., which consist of the transaction information of more than 300 million individual users and more than 37 million small and micro 7 Data

come from the report of International Telecommunication Union. and Zhima Credit are both the products of Ant Financial. 9 Data come from 2016 National Bill of Alipay. 8 Alipay

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enterprises. Second is the Internet finance data of Ant Financial, mostly from Alipay, Yuebao, Ant Financial and Alibaba Cloud. Third is the data from cooperative public institutions and partners (including public security, court and industry and commerce department etc.) to Zhima Credit, such as Lao Lai’s information provided by the Supreme People’s Court to Zhima Credit. Such information includes the case number, the name, ID card number, gender and age of the dishonest debtor, the court of execution, the number of the document as execution basis, the unit issuing the execution basis, the obligations specified in legal documents, the performance of the judgment debtor, the specific condition of the dishonest debtor, the release time, filing time, performed part and outstanding part. Fourth is the information voluntarily submitted by real name users. The credit system of the People’s Bank of China, the representative of China’s traditional credit, covers only a narrow scope of information and mainly collects personal information and credit records, comprising eight categories of public information including credit information such as personal loan, credit card and guarantee, the payment and release of personal housing fund and social security, vehicle transaction and mortgage and so on. The system does not incorporate online realtime transaction information and other information that reflects people’s credit level. In addition, the coverage of the system is limited. Since its establishment in 2006, the personal credit system of the People’s Bank of China has kept records of 899 million natural persons, but only 412 million with credit data. It means that more than a half of natural persons have no credit record in the system.10 Apparently, Zhima Credit has incorporated more information in addition to credit records in the credit system by mining and analyzing the data of personal behaviors online and calculating tens of thousands of variables on the Internet based on big data, effectively making up the deficiency of traditional credit data (see Fig. 1).

10 2017 Report on Social Credit System Development in China (21 July 2017) [31 January 2018], http://www.west960.com/p-155704.html.

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Wide coverage

With 721 million netizens, Internet credit data can be used as a supplement

Broad and multidimensional information

Traditional data + life data + Internet data

Real-time and new data

Real-time interaction Online + offline integration Specific life-related data

Tens of thousands of variables

Fig. 1 Extensive, multi-dimensional and real-time big data

Second, Zhima Credit promotes data integration in a commercial manner and helps connect isolated data islands. Isolated data islands mean that data collectors and owners do not share data, as they do not know which departments have what kind of data and even if they know, they are unwilling to exchange data for complementation. The biggest data owner in China is still the government, but different government departments do not share data like isolated data islands. Though the government has made great efforts to change this situation in recent years, this problem has not been evidently improved. The consequences of isolated data islands are as follows: First, information asymmetry among different departments. For example, courts have the default information of Lao Lai, but such information is rarely known to and used by other departments, even after the information is published. The information on discredit and default mastered by the industry and commerce administration is barely known to and used by other departments or enterprises. Second, information asymmetry among different regions. For example, an entrepreneur has been subjected to administrative penalties for tax evasion in a province, but

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he can register a new company in another province and set out again. He may even change the place again after being punished again. The rise of isolated data islands was associated with the imperfect management function of the government and incomplete civil privacy law. Though the government has adopted measures to change the phenomenon of isolated data islands in recent years, the improvement progress cannot satisfy the demands for data sharing caused by the rapid economic development of China. By introducing credit forces from market such as Zhima Credit, we hope the phenomenon of isolated data islands can be soon eliminated. A batch of private credit institutions such as Zhima Credit are pursuing cooperation with government departments including public security, court, industry and commerce, customs and tax department etc. Where existing laws permit, they will integrate data from different government departments and different regions and form the database of more dimensions and bigger coverage, which will significantly improve the value of data and information.

3.2

Credit Evaluation—Big Data Mining

“Credit evaluation” means to analyze the data collected and evaluate the credit status. Zhima Credit evaluates credit based on its credit scoring model and comprehensively evaluates personal economic credit from five dimensions, credit history, performance ability, behavior preference, identity characteristics and social connections, based on traditional data, Internet data and life data. Credit history refers to previous credit account repayment records and credit account history. Performance ability means the ability to honor an agreement when enjoying various credit services, such as the ability to timely return the car after car renting and timely pay water, electricity and gas fee etc. Other information such as consumption stability and level of consumption etc. can also be used to judge the user’s performance ability in the future. Behavior preference indicates the user’s preference in and stability of shopping, payment, transfer and wealth management and so on. Identity characteristics refer

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to personal basic information, including the personal data acquired from public security, education, industry and commerce, court and other public departments. Social connections indicate the identity characteristics of the user’s friends. Zhima Credit applies big data and cloud computing technology, carries out more objective and comprehensive credit evaluation of users by analyzing users’ educational background, occupation, consumption behaviors and asset status etc. and actively attempts to adopt the cutting-edge random forest and neural network algorithm to develop the unique credit scores—Zhima Credit score. See the evaluation model in Fig. 2. Zhima Credit scores range between 350 and 950, which are divided into five levels that represent different credit conditions. The score of 350–550 falls into the lowest level, suggesting “very bad” credit status; 550–600 means “average”; 600–650 means “good”; 650–700 indicates excellent credit status; and 700–950 is the highest level, symbolizing “extremely good” credit status. The discredit record is an important impact factor in the credit score model.

Cluster 1

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Cluster adjustment

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Credit history score

Identity and material score

Performance ability score

Behavior preference score

Occupation

Account asset

Account active time

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House

Consumption amount

Number of credit card

Stability of mobile phone

Vehicle

Consumption level

Credit card line

Stability of address

Length of credit account history

Credit card level

Richness of consumption scenarios

Fig. 2 Zhima Credit score model based on big data

Social connections score Credit of social connections Extent of social connections Depth of social connections

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Credit Use—Construction of Multiple Scenarios

“Credit use” means how society uses credit results. Though credit use seems to be independent from any link of credit, it actually constitutes the most critical link in credit system construction. No credit use means no reward to promise keepers and no punishment on dishonest persons. If no social punishment on dishonest persons and no cost of discredit, the reputation mechanism will impose no constraint on dishonest persons and the credit will have no meaning of existence. However, the credit system construction in China has stressed on credit collection and evaluation but underestimates the role of credit use for years. The credit system led by the government has no driving force or measure to advance credit use of society, resulting in the backwardness of credit use and the slow development of credit. Zhima Credit attaches great importance to the use of credit results and has constructed multiple scenarios to promote people to use the credit score. The scenario application of the credit score not only produces commercial value of the “Zhima Credit score” product, but, more importantly, encourages and rewards the honest and makes it difficult for the dishonest to do anything. The scenarios constructed or reached by Zhima Credit are not confined to the financial area, but also involve daily life, spreading the influence of credit to people’s everyday life. In financial scenarios, Zhima Credit has provided credit query and service convenience to more than 500 million people, and over 20 million users have acquired credit from financial institutions, including consumption financial credit valuing over RMB 100 billion.11 Ant Financial has developed three kinds of consumption credit products based on Zhima Credit scores, i.e. Ant Check Later, Ant Cash Now and Haoqidai. In these products, the consumption credit line of a user will be determined by the user’s Zhima Credit score. Users can directly transfer the credit to their own Alipay accounts. The loan amounts of these products range between RMB 1,000 to 50,000, with a maximum term of 12 months and a daily interest rate of 0.045%. These loans can be borrowed and repaid any time. Life-related scenarios include house renting, car renting, marriage 11The

data are provided by Zhima Credit.

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service and hotel service etc. as well as other services related to daily life and social connections. Based on Zhima Credit scores, customers can check into the hotel without a deposit and reduce the time spent on the check-in procedure from more than 10 minutes to 45 seconds on average and the check-out time from four or five minutes to 18 seconds; rent a car without deposit and reduce the time spent on car renting application by 60–80%; and rent a house without deposit, significantly lowering the cash pressure of new graduates and blue collars. According to the data provided by Zhima Credit, the total amount of deposits that customers have saved in various deposit-free services based on Zhima Credit scores has exceeded RMB 13 billion.12 The influence of Zhima Credit on daily can be seen in Fig. 3. Zhima Credit divides cooperative scenarios into three types, finance, life and government service. When choosing scenario partners, Zhima Credit will consider from the perspective of the partner, find out the links in the partner’s business flow where credit is applied and evaluate whether the data source and data model analysis results bring value to

Solve the issue of trust between merchants and individuals and between individuals

Life

B2C

C2C

Car renting

Zhima scores

Hotel

Car renting

Tourism

House renting

Visa

Marriage service

Finance Scenarios + risk control technology improve financial penetration Anti-fraud Credit card Consumption credit Education credit

Fig. 3 Presence of Zhima Credit in all aspects of life

12The

data are provided by Zhima Credit.

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the partner, whether the value can be embodied in the partner’s business process and whether the use of credit can improve the partner’s working efficiency and risk control. If the answers are yes, the scenario really demands credit. If the natural-person user also has such pain point and demand in this scenario, Zhima Credit will actively seek cooperation with the partner and promote the realization of the scenario. Application scenarios of Zhima Credit mainly consist scenarios inside Alibaba system and external scenarios established jointly with partners. Inside Alibaba Group and Ant Financial Group, the restrictions and punishments on dishonest debtors are implemented in many business scenarios including purchase, recruitment, merchant access, crowdfunding, credit investigation, insurance and credit etc. As to the external scenarios established with partners outside, Zhima Credit has established business cooperation with hundreds of companies in terms of the loans of traditional banks and other financial institutions, Internet-based financial consumption and loans and Internet-based car renting, house renting, marriage service and social connections etc. Zhima Credit and its partners will jointly punish Lao Lai. Case 1 In the cooperation with several large car-renting enterprises including CAR Inc., Zhima Credit evaluates credit of the individual applying for car renting and delivers the corresponding result to the car-renting enterprise. Individuals with favorable credit can rent cars without a deposit or even the traffic violation deposit; and individuals with bad credit (such as dishonest debtors) will not receive the deposit-free preferential and may even be rejected. Wang, a dishonest debtor in Haidian District of Beijing, placed an order for car renting on the Internet but was rejected. He was informed that CAR Inc. refused to serve him due to his negative record in Zhima Credit, and he cancelled his travel plan. When he became aware of the severe consequence of being a dishonest debtor after he called Zhima Credit, he took the initiative to contact the court and fulfill his repayment obligations. He completed his performance approximately 2 months later and was removed from the list of dishonest debtors. His Zhima Credit scores also gradually recovered. After he fully performed his obligations, he called Zhima Credit again to ask when his credit

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rating could be restored, showing high attention to the credit recovery mechanism. Case 2 In the cooperation between Zhima Credit and online house renting platform Ziroom, users can apply for renting their favorite houses based on their credit and place an order on the Internet. When Liu, a dishonest debtor in Jinxian County of Jiangxi Province, applied for house renting online, he was informed that Ziroom refused to provide services to him since he was a dishonest debtor according to the information synchronized from Zhima Credit. In this case, Liu’s house renting plan could not be realized as he could not get the house he liked, and his life quality was severely affected. Liu called Zhima Credit and was told that the data was synchronized from the Supreme People’s Court. Not only his Zhima Credit score was lowered to 456, but also he would be subjected to credit punishments according to relevant laws and the requirements of the Supreme People’s Court. Liu could not find accommodation immediately, so he had to stay at his friend’s home and lied that he was too busy to find a house. After this embarrassing experience, Liu had become fully aware of the huge influence of his dishonest behaviors on his own life, so he contacted the court of execution and fulfilled his repayment obligations in December 2015. After that, Liu called Zhima Credit several times to ask: “When can my credit scores recover? Since I have performed my obligations in the execution case, when can the influence of this record be eliminated?” Case 3 When cooperating with Laidian Technology, Zhima Credit provides data on users’ credit status as the access platform from the technical aspect. Information including Users’ ID, credit scores, access and credit records etc. are accessed through the interface, which helps Laidian Technology precisely identify dishonest debtors. These dishonest debtors cannot enjoy the deposit-free portable battery renting service and are restricted from using battery service conveniently. Moreover, the information on any default behavior of the user during battery renting will be directly sent to the Zhima Credit system through API from every outlet. Their default behaviors exert influence on their credit scores and restrict their

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credit use in the future. In this way, Zhima Credit lowers the threshold for use, reduces the bad debt ratio and meanwhile cultivates the credit culture. Case 4 During the cooperation with major communication operators including China Mobile, Zhima Credit and operators have jointly launched the credit package and credit-based phone renting activity. Individuals with high Zhima Credit scores and favorable credit can get mobile phones of new models such as iPhone 7 etc. at a very low cost, so long as they promised to purchase a certain monthly package. If any default behavior in this process, it will be recorded in Zhima Credit list and affect users’ Zhima Credit scores. Meanwhile, corresponding recovery measures will be adopted to solve issues. Apparently, dishonest debtors have no right to enjoy this package preferential. Case 5 In the cooperation with banks including SPD Bank and Bank of Beijing, Zhima Credit will deliver the list of dishonest debtors and the corresponding risk control ability to the banks as soon as possible. These banks will query the corresponding database of dishonest debtor lists before releasing a loan and decide whether to release the loan as well as the loan amount based on the information, lowering Lao Lai’s accessibility to borrowings. Case 6 As to the cooperation with some governmental agencies, government workers previously had one or only a few credit sources from other units, and they had no energy, ability or channel to query more credit information of applicants or customers and could not identify dishonest debtors. Zhima Credit will send the Zhima Credit scores of applicants or customers calculated based on extensive credit collection and big data as well as the list of dishonest debtors acquired from multiple departments or units to these governmental departments to solve the information asymmetry issue. In this way, when transacting some administrative approval affairs, enjoying government services or transacting other matters, dishonest debtors will be affected in terms of the transaction process and results.

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Seen from above cases, Zhima Credit mainly promotes credit use with scenario construction based on the Internet. With the Internet-based “credit collection – credit evaluation – credit use”, Zhima Credit can make full use of its big data, cloud computing and other information technology, solve the issue with the communication of dishonest persons’ information at a lower labor, time and capital cost and punish dishonest persons by increasing the cost of discredit. Moreover, during the cooperation with the Supreme People’s Court, Zhima Credit has especially set up a contact channel to communicate information with the Supreme People’s Court, which will deliver the execution results including Lao Lai’s name, ID card number, time of punishment and punishment results to the Supreme People’s Court Command Center in real time so that the Supreme People’s Court can immediately know the effect of credit punishments. This effect is only realized relying on information technology. While Zhima Credit fully advances the construction of “credit use” scenarios, the cost of discredit has been significantly increased. An increasing number of dishonest persons who reject to execute the court’s judgment find that they are restricted from doing many things in life and work. This high cost of discredit urges them to take the initiative to fulfill their obligations and correct their mistakes. As it should be, any innovation will be impeded by conventional habits. Zhima Credit also encounters some problems and challenges while promoting cooperation on external scenarios. For example, the partner doubts or worries about data feedback, which will influence the operation of the closed loop of credit system. As mentioned above, Zhima Credit expects to acquire data feedback and support from partners to improve its credit system, but some partners have concerns about it. Zhima Credit shall still work on this problem. For another example, it is not a simple task to find the win-win points of both parties during the design of scenario construction and balance both parties’ interests. To achieve this, it is required to go deeper into the scenario smartly, solve difficulties with personalized proposals and find out the solutions for the whole industry. These are all challengers Zhima Credit faces.

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However, innovative enterprises like Zhima Credit will necessarily advance the construction of more credit use scenarios in view of their great creativity and the fierce external competition, and finally form a perfect closed loop of “credit collection – credit evaluation – credit use” during the credit system construction.

4

Effect of Zhima Credit’s Punishments on Lao Lai

The Supreme People’s Court provides Zhima Credit with Lao Lai’s information including the case number, the name, ID card number, gender and age of the dishonest debtor, the court and region of execution, the number of the document as execution basis, the unit issuing the execution basis, the obligations specified in legal documents, the performance of the judgment debtor, the specific condition of the dishonest debtor, the release time, filing time, performed part and outstanding part. Based on such information, Zhima Credit punishes Lao Lai who have opened Zhima Credit by lowering their Zhima Credit scores, restricting Lao Lai from using consumption products and services in relevant scenarios and informing them through Alipay. How is the effect of punishments on Lao Lai by increasing the cost of discredit after the Zhima Credit scores incorporating the information from the Supreme People’s Court are applied to multiple scenarios? The part below will analyze this question from the quantity perspective and, based on the analysis of overall effect, further analyze the relation between Lao Lai’s performance and the scenarios established.

4.1

Coverage of Zhima Credit Score

A prerequisite to the cooperation between Zhima Credit and the Supreme People’s Court on Punishing Lao Lai lies in that Lao Lai has a Zhima Credit score. Only the Lao Lai who have join the Zhima Credit score system can be included in Zhima’s punishments. In other words, the coverage of Zhima Credit scores among Lao Lai is a pre-condition to

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the effect of punishments. The coverage of Zhima Credit scores among Lao Lai means the ratio of Lao Lai with Zhima Credit scores to the whole group of Lao Lai. As mentioned above, the closed credit loop of Zhima Credit is mainly based on the Internet ecology. Zhima Credit scores mainly cover Alibaba users and other users dependent on Internet ecology outside Alibaba line. By the end of December 2016, the number of real name users of Alipay, which is a part of Alibaba, had reached 450 million. These users can acquire Zhima Credit scores after authorization. From April to December 2016, despite the increase of the quantity of Lao Lai by about 5,000 per day, the Zhima Credit score’s coverage among Lao Lai had also grown from 14 to 22% at a stable rate. Though this ratio was lower than 32%, the proportion of Alipay users in the national population, it is acceptable since Zhima Credit was launched (in January 2015) later than Alipay and is still at the development stage. Figure 4 shows the stable growth of the coverage of Zhima Credit among Lao Lai. Number of Lao Lai

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Overall Effect of Zhima Credit’s Punishments on Lao Lai

In order to observe the effect of Zhima Credit’s punishments on Lao Lai from the perspective of total quality, we have designed to indexes, Lao Lai performance ratio and Lao Lai performance duration. The effect of Zhima Credit can be seen by examining the differences of the performance ratio and performance duration between Lao Lai who have opened Zhima Credit and who have not.

4.2.1 Comparison of Lao Lai Performance Ratios Between Who Have Opened Zhima Credit and Who Have not We define “Lao Lai performance ratio” as the ratio of Lao Lai who have performed their obligations to the whole number of Lao Lai in a certain area. By the end of December 2016, China has a total of 3.26 million Lao Lai, including 436 thousand who have performed obligations and 128 thousand who have opened Zhima Credit and performed obligations. See details in Table 1. As shown in Table 1, Lao Lai performance ratio in whole China is 13.35%; the performance ratio is 12.11% for Lao Lai who have not opened Zhima Credit, lower than the ratio of Whole China; and the performance ratio of Lao Lai who have opened Zhima Credit reaches 17.74%, 5% higher than the performance ratio of Lao Lai who have not opened Zhima Credit. The data demonstrate the role of Zhima Credit in improving Lao Lai’s performance ratio. In other words, it is effective to strengthen credit use and punish Lao Lai with the reputation mechanism. Table 1 Overall performance status of Lao Lai Opened Zhima or not

Performance (people)

Non-performance (people)

Total (people)

Performance ratio

Yes No Total

127,883 307,870 435,753

592,960 2,234,689 2,827,649

720,843 2,542,559 3,263,402

17.74% 12.11% 13.35%

Data source Zhima Credit

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4.2.2 Dishonesty Duration of Performed Lao Lai Lao Lai who have performed obligations vary in their dishonesty durations. The dishonesty duration refers to the time from the day they were determined as “Lao Lai” by the Supreme People’s Court to the day they fulfilled their repayment obligations. The dishonesty duration reflects Lao Lai’s performance efficiency. The shorter dishonesty duration suggests the quicker performance of Lao Lai. Figure 5 shows the population distribution of different dishonesty durations of performed Lao Lai who have opened Zhima Credit, while Fig. 6 shows that of Lao Lai who have not opened Zhima Credit. In general, the average dishonesty duration of performed Lao Lai who have opened Zhima Credit is 9.35 months, compared to 10.04 months of those who have not opened Zhima Credit. By comparing the two figures, we can discover that within the two intervals, respectively 0–3 months and 4–6 months, the proportion of performed Lao Lai who have opened Zhima Credit is higher than he proportion of those who have not opened Zhima Credit. The former 21~24 months 2.93% 19~21 months 6.58% 16~18 months 7.48%

More than 24 months 3.14%

0~3 months 25.24%

13~15 months 9.58% 4~6 months 15.43%

10~12 months 13.55% 7~9 months 16.07%

Fig. 5 Dishonesty duration of performed Lao Lai who have opened Zhima (Data source Zhima Credit)

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22~24 months 3.08%

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More than 24 months 3.49%

19~21 months 6.99% 0~3 months 20.90%

16~18 months 8.70%

13~15 months 10.92%

10~12 months 14.87%

4~6 months 14.81%

7~9 months 16.24%

Fig. 6 Dishonesty duration of performed Lao Lai who have not opened Zhima (Data source Zhima Credit)

group account for a total of 40.67% in the two intervals, while the latter group take up 35.71%. In the intervals above 6 months, the proportions of performed Lao Lai who have opened Zhima Credit are all lower than the proportions of those who have not opened Zhima Credit. The data above indicate that Zhima Credit’s punishments can impel Lao Lai to perform obligations as soon as possible to a certain extent and shorten the dishonesty duration of Lao Lai on the whole.

4.3

Role of Scenarios

According to the reputation mechanism theory, the intensity of social punishments will influence the effect of the reputation mechanism. In the case with Zhima Credit, the intensity of social punishments is manifested by the cost of discredit and embodied in credit use scenarios. The number of credit use scenarios and the dependence of dishonest persons on these scenarios determine the cost of discredit. By analyzing

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the scenarios, we can further understand the role of Zhima Credit in credit punishments.

4.3.1 Number of Scenarios Only Lao Lai covered by Zhima Credit system are subject to the restrictions in various scenarios. Our discussion on scenarios is conducted based on the data of Lao Lai within the system. At present, Zhima Credit has established 30 scenarios, mainly comprising four major categories, respectively finance, life services, information services and public services. The scenarios of the four categories are listed in Table 2. With more scenarios, the credit mechanism can reach more people, and Lao Lai’s life will be severely restricted in these scenarios. In this way, Lao Lai cannot escape the cost caused by dishonesty. While the total number of Lao Lai with Zhima Credit scores is 720,843, all scenarios of Zhima Credit cover 278,782 Lao Lai, accounting for 39.16% of Lao Lai with Zhima Credit scores. Though the coverage of scenario restrictions is still less than a half, with the increase of scenarios established, a bigger network will be created, which will expand the scope of Lao Lai subject to the punishments.

4.3.2 Effect of Scenarios To observe which scenarios play the most important role in promoting Lai Lai’s performance, we shall focus on the specific scenario where Lai Lai’s behaviors were restricted before they performed their obligations. The scenario where Lao Lai were restricted most frequently before their performance is exactly the scenario that Lao Lai depends on the most. In other words, this scenario shows a higher possibility to urge Lao Lai to perform obligations. According to our statistics, the top 5 scenarios that restrict Lao Lai most frequently include travel service, borrowing consumption platform, tourism service, e-commerce/shopping guidance and bank’s credit card center (see Fig. 7). It means that these scenarios are the most important and also play the greatest effect.

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Table 2 Application scenarios of Zhima Credit Public Services

Finance

Life Services

Information Services

Borrowing consumption platform Other financial scenarios Internet lending platform Consumption finance company Bank’s credit card center

Travel service

Classified information platform News/entertainment

Public security

Tourism service

Recruitment

Education services

Accommodation service

Internet basic services

Other government scenarios

Door-to-door service

Part-time job information platform Operator

P2P company Payment institution Bank’s personal loan Financing guarantee company Insurance Microloan company Financing lease company

E-commerce/shopping guide

Marriage service Community/social connections Other life services

Medical services

4.3.3 Relation Between Performance and Scenarios To further examine the relation between Lao Lai’s performance and Scenarios’ restrictions on Lao Lai’s consumption (transaction) behaviors, we will adopt two indexes, the number of people subject to restrictions and the times of restrictions. The number of people subject to restrictions refers to the total number of people who have been restricted by a certain scenario; and the times of restrictions mean the times that Lao Lai have been restricted (as one person may be restricted for several

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Financing lease company

Door-to-door service Community/ social connections

Financing guarantee company

Internet basic services Other life services Insurance

Operator Other government scenarios

Public security

Marriage service

Payment institution

News/ entertainment

Accommodation service

Recruitment

P2P company

Bank’s personal loan

Internet lending platform

Other financial scenarios

Classified information platform

Bank’s credit card center Consumption finance company

E-commerce/ shopping guide

Travel service

Borrowing consumption platform Tourism service

Number of people subject to restriction

People

Fig. 7 Distribution of scenarios that restrict Lao Lai before they performed obligations (Data source Zhima Credit)

times). Per capita times of restrictions = times of restrictions/number of people subject to restrictions. Now we rank the scenarios Lao Lai used based on the number of people subject to restrictions. Since Top 10 show a large-margin lead compared to other scenarios, only Top 10 scenarios are discussed here for convenience. The scenarios that have restricted the largest number of Lao Lai include: travel service, borrowing consumption platform, tourism service, consumption finance company, bank’s credit card center, e-commerce/shopping guide, other financial scenarios, Internet lending platform, P2P company and bank’s personal loan (see Fig. 8). The number of restricted people reflects the importance of a scenario in terms of the coverage. The larger number of restricted people demonstrate the more extensive reach range of the scenario. In this case, credit information can be communicated in a wider range and the scenario plays a bigger role in increasing the cost of discredit. Meanwhile, the per capita times of restrictions reflect Lao Lai’s dependence on the scenario and the influence of the scenario on Lao Lai. The more percapita times of restrictions suggest a higher cost of discredit incurred on Lao Lai.

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Number of people subject to restrictions

Per capita times of restrictions

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Fig. 8 Top 10 scenarios in the number of performed Lao Lai subject to restrictions (Data source Zhima Credit)

In order to compare the differences of scenario restrictions on performed Lao Lai and unperformed Lao Lai, we also calculate the Top 10 scenarios ranked by the number of unperformed Lao Lai subject to restrictions as well as the per-capita times of restrictions (see Fig. 9). The top 10 scenarios that restrict unperformed Lao Lai are same with those for performed Lao Lai, but the rank is different. The Top 10 for unperformed Lao Lai are respectively travel service, borrowing consumption platform, tourism service, other financial scenarios, ecommerce/shopping guide, consumption finance company, Internet lending platform, P2P company, bank’s credit card center and bank’s personal loan. The rank of bank’s credit card center shows the most evident change, as it ranks the fifth in the scenarios for performed Lao Lai with per capita times of restrictions of 2.19, and ranks the ninth with per capita times of restrictions of 1.97. This phenomenon proves that the bank’s credit card center plays different roles among performed and unperformed Lao Lai, and exerts greater influence on performed Lao Lai. We also discover that except the bank’s personal loan, the per capita times of restrictions for performed Lao Lai are higher than that for unperformed Lao Lai in other nine scenarios. It demonstrates that the more times of restrictions improve Lao Lai’s performance.

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Fig. 9 Top 10 Scenarios in the Number of Unperformed Lao Lai Subject to Restrictions (Data source Zhima Credit)

5

“Improve Credit” by “Using Credit”

Reputation is a social contract governance mechanism parallel to laws, and both complement and support each other. By analyzing the case of Zhima Credit and the Supreme People’s Court cooperating to jointly punish “Lao Lai”, this article further proves the fact that the reputation mechanism supports the legal mechanism. Also two other important points are discovered: First, “credit use” is a key link that gives full play to the reputation mechanism based on the credit system. Only by using credit, it can increase the cost of discredit through social punishments and make the reputation mechanism effective. Meanwhile, “credit collection” and “credit evaluation” only make sense on the basis of credit use. Therefore, the emphasis shall be placed on the closed loop of “credit collection – credit evaluation – credit use” during credit system construction in China. In this way, we can reward the honest, punish the dishonest and create honest society where all the people attach importance to credit. Second, we can effectively promote the construction of credit use scenarios depending on commercial credit institutions. More credit use scenarios will increase the cost of discredit. Zhima Credit under Ant Financial has played a pioneering role in this respect.

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Case Comments Same as laws, reputation is also a constraint mechanism that maintains the order of people’s social and economic activities. Laws force people to abide by statutory rules through the coercive power of national judicial organ, while reputation urges people to abide by social morals and rules through social public opinion (praise or condemnation) in a noncompulsory manner. It is generally believed that laws should be the most powerful constraint force compared to the reputation mechanism. However, through careful observation we find that law is not everything. For example, the person concerned may refuse to execute the judgment after the court rules, or the “Lao Lai” issue arises everywhere. In this case, only by applying the reputation mechanism to support the legal mechanism can the problems be effectively solved. The case of the cooperation between Zhima Credit and the Supreme People’s Court on solving the “Lao Lai” issue in debt disputes fully shows the role of the reputation mechanism and demonstrates that the reputation mechanism and legal mechanism complement each other in maintaining social order. Moreover, this case also proves that a sound credit system determines the effect of the reputation mechanism. When referring to credit system construction, people generally mean the collection (recording) and evaluation of credit information on people’s behaviors, which means credit institutions can record people’s credit information timely and accurately and give correct comments, i.e. the “credit collection” and “credit evaluation” mentioned in this case. However, from this case we sincerely feel that “credit collection” and “credit evaluation” are not enough to exert a strong deterrent, but “credit use” shall be given attention to. Credit use mans that people reward the honest and punish the dishonest based on the results of credit collection and evaluation. Without credit use, reputation would show no value and people would take no notice of reputation. Therefore, the reputation mechanism would lose its constraint force. This case emphasizes that “credit collection”, “credit evaluation” and “credit use” are the three indispensable links in social credit system construction. Only by attaching importance to all the three links can the virtuous cycle of the social

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credit system be formed and the rapid development of honest society be promoted in China. How to promote society to accept the results of credit evaluation and to use credit? Apparently, merely depending on the government force is not enough. This case proves that by utilizing the force of market and driving enterprises’ behaviors with commercial interests, it not only delivers evaluation results to all scenes that demand credit use, but also explores the potential scenes for credit use and maximizes the effect of credit collection and evaluation. Hence, the construction of social credit system must rely on the force of market. It shall no noted that Zhima Credit can only reach limited scenarios of credit use. Upon the success of the pilot cooperation, the Supreme People’s Court shall further promote the cooperation model and cooperate with more credit institutions, thus to acquire better results. However, it shall adopt open and equal standards when selecting the cooperative credit evaluation institutions to assure the equal competition in the credit industry. Meanwhile, the credit center of the People’s Bank of China shall play its important role as the “national main force” in the credit industry, particularly in credit use. Commentator: Wang Jun13

References Bei Duoguang, Good Finance, Good Society—Report on Inclusive Finance Development in China (2015), Beijing; Economy & Management Publishing House, 2016. Bernheim B D, Whinston M D, Incomplete Contracts and Strategic Ambiguity. American Economic Review, 1998, 88(4):902–932. Ellickson R C, Order Without Law: How Neighbors Settle Disputes. Boston: Harvard University Press, 1991:86. Ghoshal S, Moran P, Bad for Practice: A Critique of the Transaction Cost Theory, 1996, 21(1): 13–47. 13 Part-time professor of finance of China Europe International Business School (CEIBS) and director of CEIBS—World Bank Inclusive Finance Center.

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Gong Yunju, Yu Xianghua, Judicial Self-rescue from “Execution Difficulty” — From the Perspective of Cases with No Executable Assets, “Shandong Justice”, 2015, 2. Greif A, Institutions and Impersonal Exchange: The European Experience. Stanford University, Working Paper, 2003. Johnson S, Mcmillan J, Woodruff C. Contract Enforcement in Transition. CEPR Discussion Paper Series Number 2081, 1999. Macaulay S, Non-Contractual Relations in Business: A Preliminary Study, “American Sociological Review”, 1963, 28(1): 55–67. Sun Jianchao, Discussion on Improving the Credit Industry Regulation System in China, “Journal of the Chengdu Municipal Party College of C.P.C”, 2006, 4. Sun Ya, Tang Youwei, Problems in China’s Credit System Construction and Suggestions for Improvement, “Review of Investment Review”, 2008, 12. Tang Mingqin, Issues with the Development and Improvement of China’s Credit System from the Comparison Perspective, “Credit Reference”, 2010, 2. Uzzi B, Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness, “Administrative Science Quarterly”, 1997, 42(1): 35–67. Wang Jingwei, Theory and Practice Analysis of China’s Credit System Construction, “Credit Reference”, 2009, 2. Xiong Xueping, Institutional Framework of Credit in America and Europe and China’s Choice of Personal Credit System, “Journal of Huazhong Agricultural University: Social Science Edition”, 2011, 3. Zhang Weiying, Reputation Foundation for Legal System, “Economic Research Journal”, 2002, 1. Zou Hao, Exploration of American Consumption Credit System, Beijing: China University of Political Science and Law Press, 2006.

10 Bairong Zhixin: Big Data and Credit System Construction Bao Sun

Steam engine in the first industrial revolution had driven the traffic and transportation reform; telephone and telegraph in the second industrial revolution had created the new communication mode; and computer technology in the 1960s opened the door to the age of information. Each technological revolution had greatly improved the speed and expanded the coverage of information communication. Along with the maturity and extensive application of Internet/mobile Internet, e-commerce and social network in the early twenty-first century, the number of Internet users has seen a sharp rise. Massive data have been generated by users’ online behaviors such as shopping, social interaction, web browsing and Members of this case study group include Bao Sun, Li Zhenni, Gao Yuexia and Cao Rui. This report is prepared based on the data collected on site and interview record and reviewed by Bairong (Beijing) Financial Information Service Company Limited. We would like to express our gratitude to support of Bairong.

B. Sun (B) Renmin University of China, Beijing, China e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_10

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search etc. Big data technology has emerged as times require, and credit evaluation technology is undergoing new reform. However, how is big data technology applied to credit analysis and evaluation? What are the advantages compared to traditional credit evaluation? What are the problems that shall be overcome? We hope we can answer these questions through the in-depth analysis of Bairong Zhixin case.

1

Origin of Bairong Zhixin

Bairong Zhixin (Beijing) Credit Company Limited (hereinafter referred to as “Bairong Zhixin”), affiliated to Bairong (Beijing) Financial Information Service Company Limited (hereinafter referred to as “Bairong Financial Service”), is a big-data credit analysis and evaluation company incorporated in strict accordance with national administrative requirements and information system security level evaluation report system, in the context of the State Council advancing social credit system construction and the rapid development of Internet/mobile Internet and big data technology. Being a specialized company providing big data financial information services, Bairong Zhixin establishes its credit analysis business based on the massive data accumulated by Percent Technology Limited Liability Company (hereinafter referred to as “Percent”) in its Internet marketing business. Such data include users’ online and offline behaviors, consumption, reading, preferences, social connections, car and house information and blacklist data etc. Since 2013 when the credit analysis business commenced, Bairong Zhixin has already accumulated data of about 500 million real name users and 700 million anonymous users and has reached cooperation agreements with several commercial banks and Internet finance companies. It is also negotiating or launching pilot programs with over 100 financial institutions at present.

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First Stage: Internet Precision Marketing (July 2009–March 2014)

E-commerce in China has experienced rapid development since 2008. To acquire more traffic, improve users’ experience and improve conversion rates, e-commerce enterprises face increasingly urgent demands for customized user recommendation. In this context, Percent was founded in July 2009 in Beijing. Commencing with Internet precision marketing, it provides products and overall optimization solutions of traffic conversion and business intelligent analysis for e-commerce enterprises and Internet media enterprises, and meanwhile offers cloud platforms and overall solutions of basic big data technology, big data management and application to traditional industries. In 2010, Percent had completed the research, development and test of the recommendation engine. This recommendation engine integrates users’ behavior records, constructions the users’ “profile”, i.e. the unified view of users’ preferences and interfaces with webpages, mobile App, WeChat and E-mail etc., to make personalized recommendations for the whole shopping process, provide intelligent shopping guide and improve users’ experience in the website. In 2012, Percent launched the intelligent business analysis engine designed exclusively for e-commerce enterprises, which increases e-commerce platforms’ traffic and sales volume and optimizes their operation decisions. By now Percent has established a partnership with more than 1,500 famous e-commerce enterprises and information websites including Vancl, YHD, Jumei, Intime Online, Mbaobao, CBN Weekly, Caixin Online and Xici Hutong etc. It is now offering 250 million personalized precision recommendations and business intelligent analysis services to more than 82 million users everyday and spreads its presence to online education, online tourism, finance, securities and manufacturing etc. Against different characteristics of different industries, Percent has established corresponding industry departments to develop business. The financial industry department covers extensive financial forms including banking, securities, insurance and P2P online lending etc.

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Second Stage: Financial Marketing and Risk Control Services (March 2014–December 2014)

Risk control over the whole financial industrial chain from the frontoffice business to back-office management is all conducted based on data. Compared to physical commodity sectors, the financial industry, particularly the core function of financial enterprises, i.e. the risk management and control technology centering on credit analysis and evaluation, can be more easily combined with Internet technology. Along with technological progress and business model innovation, such new technologies as the Internet and big data are penetrating into traditional financial industry. After over 5 years of accumulation and development, Percent has become a big data-based underlying platform that includes the data of about 500 million real name users and 700 million anonymous users. In the process of precision marketing that creates “profiles” for real name or anonymous users, Percent found another important use of data, credit analysis and evaluation. Therefore, it started relevant business and incorporated it in financial industry department. Since 2013, the government has successively released a series of laws and policies including Regulations on the Administration of Credit Industry, Administrative Measures for Credit Institutions and Outline of Social Credit System Construction Planning (2014 –2020), which has promoted the rapid development of China’s personal credit market. To adapt to regulation requirements and assure the healthy development of credit analysis and evaluation, Percent split the original financial industry department in March 2014 and registered Bairong Zhixin (Beijing) Credit Company Limited. The original businesses were wholly transferred to Bairong Zhixin. Bairong Zhixin had acquired the credit license granted by the Central Bank. Depending on big data technology and multi-dimensional data from the Internet, offline retail, social activities, media, aviation, education, operators and brands etc. Bairong Zhixin provides precision marketing, pre-loan credit review and after-loan management services to customers of the financial industry, reduce customers’ risks, improve their approval rates and supports customers to improve their overall management.

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In December 2014, with a structure of diversified financial information services, Bairong Zhixin was renamed as Bairong Financial Service. Bairong Financial Service has established branch offices/subsidiaries engaged in financial credit information, financial marketing and management of non-performing financial assets etc. and operated as a group. By now Bairong Financial Service has established partnerships with more than 70 financial enterprises, including traditional financial institutions such as banks, insurance and securities companies as well as P2P online lending and other Internet finance companies. The relations among Bairong Zhixin, Bairong Financial Service and Percent are shown in Fig. 1.

1.3

Third Stage: Bairong Zhixin—Dedicated to Personal Information Analysis and Evaluation (from January 2015 Until Now)

To further adapt to regulation requirements, establish the information system security level evaluation report system and support the development of personal credit business, Bairong Financial Service set up a wholly-owned subsidiary, Bairong Zhixin, in January 2015, which shares platform and information resources with Percent and Bairong Actual controller & legal person: Zhang Shaofeng

Actual controller & legal person: Su Meng

Three founders: Zhang Shaofeng, Su Meng, Bai Linsen

Three founders: Su Meng, Bai Linsen, Zhang Shaofeng

Percent

Chengdu

Beijing

South China

East China

North China

Bairong Zhixin

Nanjing

Five branch offices

Whollyowned subsidiary

Shanghai

Three branch offices

Data cooperation

Shenzhen

Bairong Financial Service

Fig. 1 Relations among Bairong Zhixin, Bairong Financial Service and Percent (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center)

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Financial Service and focuses on personal information analysis and evaluation. Bairong Zhixin has reached cooperation agreements with several commercial banks and Internet finance companies. It is also negotiating or launching pilot programs with over 100 financial institutions at present. In the meantime, it is actively applying for personal credit investigation license.

2

Build the Big Data Platform

Internet user

The big data platform constitutes the foundation of big-data credit evaluation. The so-called big data platform refers to the whole process of “collecting, sorting, preserving, processing and providing” data. According to the definition of credit business specified in Regulations on the Administration of Credit Industry, Bairong Zhixin carries out credit analysis and evaluation business from the five aspects mentioned above, has established a whole process from data collection to data integration and utilization (sorting, preserving and processing) and then to product output to customers (providing) (see Fig. 2) and therefore has built a Data collection

Data integration & utilization

Online

Five Engines and Eleven Systems

E-commerce website

Calculation

Collection

Storage

Internal control Settlement

Retail consumer

App Mobile App Media website

Model Offline Retailer

Financial user

Payment

Downstream users P2P Bank & P2P

Evaluation report

Credit review

Evaluation score

After-loan management

Customer service

Monitoring Risk evaluation

Marketing Product

Insurance & securities Marketing service

Marketing service

Subscription service

Customer service

Enterprise

Financial institution Public record

Credit product output

Value evaluation

Data Individual

Fig. 2 Overall Framework of Data Collection, Processing and Output of Bairong (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center)

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complete big data platform. The big data platform of Bairong Zhixin shows two major strengths: first, data collection that includes comprehensive online and offline multi-dimensional data, and second, data integration and utilization based on “five major engines” and “eleven systems”.

2.1

Collection of Online and Offline Multi-dimensional Data

Data collection is the most important part of big data credit evaluation. A focus of Bairong Zhixin lies in how to acquire authentic, comprehensive and real-time information in accordance with laws and regulations. To this end, Bairong Zhixin formulates the principle of data collection: adhering to an objective, independent and fair third-party position, abiding by laws, regulations and relevant supervisory rules and assuring authentic, comprehensive, real-time and continuous data collection. As guided by the principle of data collection, Bairong Zhixin has presented a characteristic of multi-dimensional data collection (including online and offline data) through great efforts and realized all-round data collection, timely updates and dynamic description, which makes up for the deficiencies of the traditional credit investigation model including few dimensions, time lag and no dynamic description of credit changes etc.

2.1.1 Principle of Collection The specific details of Bairong Zhixin’s principle of data collection are as follows. First, comply with laws and regulations on information collection without touching the red line of supervision. Therefore, Bairong Zhixin must observe following requirements: collect personal information only upon the consent of the information subject and never collect information without such consent; do not collect information on personal religious belief, DNA, fingerprint, blood group, disease

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and medical history or other personal information, the collection of which is prohibited by laws and administrative regulations; and do not collect information on people’s income, deposit, securities, commercial insurance, real estate and taxed amount if without strict authorization or if being informed of any negative influence. Second, adhere to an objective, independent and fair third-party position. Global credit leaders, Equifax, Experian and Trans Union, are all independent third-party credit institutions that do not provide financial businesses. By doing this, they can provide customers with credit services fairly and prevent the conflict of interest with customers. Either shareholders or associated enterprises of Bairong Zhixin show no financial or quasi-financial background, so the interest game of being a “referee” and a “player” at the same time can be avoided. Third, adhere to collecting data in an authentic, comprehensive, realtime and continuous manner. Data of Bairong Zhixin can reflect the real online and offline traces of the object of information collection, preventing the object from presenting a false appearance for the sake of material gains to the maximum extent possible. Moreover, Bairong Zhixin also compares and verifies data for authenticity through technical methods.

2.1.2 Channels of Collection At Bairong Zhixin, data of multiple dimensions are collected from multiple channels. The channels of data collection mainly include the follows: the subject of information; more than 1,500 online e-commerce, media and communities using the recommendation engine service; offline retailers and brands using the big data technical services and O2O services; third-party data sources which have established a strategic partnership with Bairong; information published by public departments including governments and judicial departments during their performance of duties according to law (including information on the legal person and citizen’s qualification, reward, commendation, prohibition, disciplinary sanction, administrative punishment, court and tax arrears etc.); and other data sources. The types of data include demographic data such as gender, age, telephone number, living address, working

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address, educational background and occupation etc.; online behavior data such as consumption, aviation, tourism, social connections, reading, entertainment, news, real-time information, video, music and game etc.; and offline data such as consumption at retailers’, borrowing at financial institutions and data from public security organs etc. 60% of Bairong Zhixin’s data are collected online. Meanwhile, Bairong is also further exploring offline market and data sources and has concluded cooperation with many famous large retailers including Xinglong Store, Chaoyang Joy City, Wanda Group, Wangfujing Department Store and Intime Department Store. By developing cooperation with online and offline customers and further extending the fields and industries of cooperation, Bairong can acquire more complete transaction data of better quality in real time and achieve extensive coverage of people’s basic information while respecting people’s privacy.

2.2

Data Integration and Utilization

Accurate, comprehensive and real-time data are the lifeblood of credit evaluation, and the precise, efficient and stable model are the soul. On the foundation of massive data collected, the original data shall be cleaned and sorted thus to build the big data platform and realize data integration and utilization. Percent, the parent company to Bairong Zhixin, is an influential data operation service enterprise in China. It features inborn advantages in the construction of credit information system centering on data, laying solid technical foundation for Bairong Zhixin to perform data collection, data calculation, data mining and data service. Based on original technology, Bairong Zhixin has designed and released its own information system consisting of “five engines” and “eleven systems” in accordance with the principles of system stability and data security, usability and extendibility (see Fig. 3), and further developed the data characteristics and algorithm engine to support the product and business development of Bairong Zhixin.

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2.2.1 Five Engines “Five engines” is the core of Bairong Zhixin information system and embodies the long-term advantages of Percent in big data processing technology, data mining technology and massive data accumulated networkwide. The five engines include big data storage engine, big data calculation engine, big data model engine, user risk evaluation engine and user value evaluation engine respectively. Among the five engines, the big data storage engine has accumulated the most data on cross-website consumption preferences in China and contains the consumption and interest preferences of around 500 million terminal web users, which is the data foundation to provide third-party enterprise credit services. The big data calculation engine and big data model engine are responsible for the cleaning, standardization, integration, analysis and modelling of billions of pieces of original data coming from thousands of channels, to create user profiles of Bairong Zhixin and the model algorithm foundation for high-added-value credit information service. The user risk evaluation engine uses four algorithm models to evaluate users’ risks based on users’ reading behavior data, consumption behavior data, stability data and brand preference data. The four algorithm models respectively include: the user risk logistic regression model, a Bairong Zhixin user risk evaluation model based on logic regression Enterprise customer management system Internal control system

Settlement system

Payment system

Personal information service system

Data access system

Product configuration management system

User risk evaluation engine Big data calculation engine

User value evaluation engine

Big data storage engine

Data collection system

Big data model engine

Customer service system

Monitoring system

Marketing management system

Fig. 3 Five Engines and Eleven Systems (Data source Bairong Financial Service)

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algorithm; user risk SVM model, a Bairong Zhixin user risk evaluation model based on SVM algorithm; user risk neural network model, a Bairong Zhixin user risk evaluation model based on neural network algorithm; and integrated user risk algorithm model, an integrated Bairong Zhixin user risk evaluation model based on all algorithms above. With the five engines, Bairong Zhixin can apply risk evaluation mechanism and model suitable for the user. The five engines constitute the company’s core advantage of its credit analysis products and services.

2.2.2 Eleven Systems The “eleven systems” include data collection system, internal control system, settlement system, payment system, customer service system, monitoring system, marketing management system, product configuration management system, enterprise customer management system, data access system and personal information service system. These eleven systems make up the foundation of Bairong Zhixin information system and the guarantee that effectively supports Bairong Zhixin’s business, product design, development and application and customer service.

3

Construct the Risk Model

The goal of credit information analysis and evaluation is to identify external fraud risk and credit risk. As defined by Basel Committee on Banking Supervision, the external fraud risk refers to the loss incurred by a third party deliberately obtaining property by deception or stealing or evading the law; and the credit risk means the possibility for a borrower not to perform the obligations of principal and interest repayment according to the credit contract. The big data credit evaluation model of Bairong Zhixin that combines online and offline data can identify the fraud risk by evaluating the behaviors of one person with several IDs, referring to the library of special lists and conducting quantitative modelling. Meanwhile, the credit risk identification and evaluation are

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performed with multiple models based on the 5C principle1 and online and offline multi-dimension data. Modelling of Bairong Zhixin mainly draws on the experience of Zest Finance model and uses the massive non-financial and financial data online and offline to construct the credit risk model, which consists of about 500 thousand weak variables including consumption, reading, social connections, tourism, entertainment, finance and public records etc. Zest Finance model is established with approximately 70,000 variables based on structural and non-structural big data including demographic data, website behavior data and social network data etc., which is the foundation for the construction of the big data risk model. The principle of Zest Finance model is as follows: first, process the massive weak variables collected to generate more effective derived variables; second, regroup these variables based on their relevance and make them stronger variables; third, introduce these relatively stronger variables into dozens of independent models built based on different machine learning algorithms for processing and have each independent data model produce an independent score; and finally, apply the weighted combination method to these scores and integrate them into a final credit score.

3.1

Identification of Fraud Risk

Bairong’s fraud risk control system mainly relies on the anti-fraud database that is made up by online and offline data of 500 million people in terms of consumption, reading, social connections, house, car, borrowing, wealth management and account transactions etc. This system features following characteristics. First, data are real. During consumption, reading and social activities, users have no intention to conduct identity or behavior fraud as they are not applying for a loan. Second, the user’s identity is identified through cross validation. The system can connect different IDs of one person through crossscreen, cross-terminal and cross-browser technical means and examine 1 Character,

capacity, capital, collateral and condition.

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and group the same person’s behaviors in different IDs to accurately identify the user’s identity. Third, this system shows strong real-time performance. If the user conducts potential fraud behavior, it will be captured by the database, and the system will identify the risk within several milliseconds. What is distinguished from the traditional fraud risk identification method is that Bairong Zhixin’s method performs multidimensional cross validation of data and dynamic monitoring of the model. The traditional method can only identify a part of fraud risks based on the ID card number and address examination and telephone confirmation, but some fraud behaviors such as counterfeiting and disguise cannot be discovered. It shows poor performance in authenticity, cross validation and real-time response. The fraud risk identification method of Bairong Zhixin mainly comprises three parts, identity information and behavior abnormality check, special list library verification and scoring of the potential fraud risk based on the quantitative fraud risk model.

3.1.1 Identity Information and Behavior Abnormality Check Identity information and behavior abnormality check means to compare the information submitted by the credit applicant with the applicant’s information in Bairong’s database, thus to determine the authenticity of the applicant’s identity. The data involved here are mainly the individual user’s PII2 image relation data, including ID card, mobile phone number, e-mail, name, landline telephone number, address, Cookie3 and IMEI.4 The identity check strategy can discover following fraud scenarios: using several different identities to apply for credit cards or loans with the same

2 Personal

identifiable information, PII, refers to the user’s personal information including name, ID card, mobile phone number, e-mail and other information that helps identifying a specific person in reality. 3 Cookie is the data stored by certain websites in users’ local terminals (often encrypted) for identifying users’ identification and tracking users’ dialogues. 4 IMEI, International Mobile Equipment Identity, is the unique identification number of a mobile phone. Every mobile phone will be assigned with an IMEI during production.

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device; applying for credit cards or loans from several financial institutions with the same device; applying for credit cards or loans with the identities of others (inconsistency between the identity information and contact method); inconsistency between the address of application and the living address; users who have never acted online showing up online suddenly and applying for credit cards etc.

3.1.2 Special List Library Verification Special list library verification refers to examining sensitive information to identify the individual’s fraud risk. For this purpose, the sensitive information of individual risk is compared to the special list, to judge the authenticity of the user’s loan. Bairong Zhixin has a special list library, which is constructed based on Bairong big data analysis platform. The special list library is used in two cases. First, query the special list library against the new applicant. If the applicant is found in the library, the type of the special list will be provided. Second, query the PII image relation database against the new applicant and query the special list library again with the user’s extended identity information. If the applicant is found in the library, the PII image relation hierarchy and the type of the special list shall be provided. Bairong’s special list library aims to compare the PII image relation data and special list data of individual users with the special lists, thus to judge users’ credit qualification.

3.1.3 Quantitative Fraud Risk Model Bairong anti-fraud model is a mixed scoring model based on the decision tree algorithm and experts’ experience. It reflects customers’ fraud risk in a quantitative manner, makes up the deficiency of artificial review and shortens the time of fraud review. This model combines the experience of anti-fraud experts and predicts customers’ fraud possibility with a score according to the work procedure listed below: data integration, sampling, variable analysis, cross validation, prediction ability test, fraud

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expert screening variables, creation of fraud scoring model, model evaluation and model inspection. Once the model is applied, the application results of the model shall be subject to continuous monitoring and the changes of market frauds shall be tracked, to adjust the variables and scores of the model.

3.2

Credit Risk Modelling

The credit risk model generally evaluates users’ credit based on their behavior information in accordance with 5C principle. Several types of models can be used to accurately evaluate users’ risk based on current data. Bairong possesses multi-dimensional data including demographic data, behavior data and asset data etc., covering thousands of data dimensions. In order to use massive information to acquire the risk evaluation of users, Bairong must adopt a risk evaluation model more complicated than traditional credit investigation. However, the models can be divided into classification models and algorithm models in general. At present, the main classification models used by Bairong Zhixin include the score card model, credit risk strategy model, credit rating model and big data integrated model; and the main algorithm models include the logistic regression, decision tree, K-Nearest Neighbor (KNN), SVM, Bayes and neural network model etc. Here we would like to use the traditional score card model as an instance to understand the basic principle of these models in the context of big data. The score card model based on logistic regression algorithm is used to evaluate users’ credit risk in a quantitative way. When this model is applied, we shall first determine the data source and select samples. Bairong Zhixin will first conduct exploratory analysis of massive data on consumption, media browsing, asset, business trip, matching method and the bank’s application form data and overdue/ non-performing data etc., in order to find which of the weak variables can be used in credit evaluation.5 After having found the weak variables available for credit 5 Exploratory

data analysis includes univariable analysis, bivariable analysis, correlation analysis, cross validation and outlier analysis. The univariable analysis involves such continuous variables as observed data, mean value, standard deviation, skewness, missing data and mode etc. as

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evaluation, we shall group these weak variables based on their predictive capability and the corresponding bad debt rate and business concept, and turn them into stronger variables. These strong variables are exactly predictive variables. The values of predictive variables are considered as the indexes to measure the predictive capability of default variables (binary-valued variables) (see Fig. 4). The latest data sets generated by grouping can be applied to logistic regression calculation, and the initial regression model can be built. Based on the regression model, we shall convert the probability into score using the conversion algorithm between probability and score, to further obtain the score card. The score card can be applied to the credit evaluation of all groups applying for

Gender, age, telephone number, living address, working address, educational background and occupation; consumption, aviation, tourism, social connections, reading, entertainment, news, real-time information, video, music and game; and consumption at retailers’, borrowing and public records etc.

Exploratory data analysis screens weak variables: univariable analysis, bivariable analysis, correlation analysis, cross validation and outlier analysis

Raw data

Credit product

Credit evaluation, marketing service, user value appreciation and collection after loss of contact

Weak variables

Examination, monitoring and adjustment

Credit scoring model

Key information match data, stability data, commodity consumption preference data, media browsing preference data, asset data and application information verification data etc.

Stronger variables Strong variables: borrowing and public records etc.

Fig. 4 Flowchart of Construction of the Big Data Credit Risk Model (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center)

well as categorical variables including frequency, relative frequency, cumulative frequency and cumulative relative frequency of each category. Bivariable analysis is used to predict the trend of variables and bad debt ratio. Correlation analysis calculates the Pearson correlation coefficient among continuous variables as the index to measure the correlation of continuous variables. These weak variables will be subject to cross validation to determine that whether they can be applied to valuate credit risk. Finally, the outlier analysis is performed and outliers and removed.

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loans, including the credit of customers with a better credit quality and having been approved as well as customers who failed in credit approval, in order to avoid any systematic distortion of sample spaces. The final task is to examine, monitor and adjust the model. The predictive capability and stability of the model can only be applied in practice after examination. For example, after the model is established, the stability monitoring report shall be prepared, to compare the score distribution of new applicants and sample customers and monitor the effectiveness of the model. The feature analysis report shall also be prepared, to compare the feature distribution of present score cards and all score cards established during modelling and thus monitor the effectiveness of the model. Meanwhile, the non-performing loan data analysis report shall be made to evaluate the non-performing loans in different score ranges, compare to the predictions obtained during modelling and therefore monitor customers’ loan quality. In most cases, along with the changes of economic and market environment as well as constant changes of the user structure after a certain period of time, the predictive capability of the credit score card will gradually decline. Bairong Zhixin shall adjust the scoring model to adapt to the changes. Therefore, the credit score card shall be subject to constant monitoring after establishment and shall be adjusted appropriately or rebuilt after a certain period of time.

4

Diversified Credit Analysis and Evaluation Products

According to credit business development at home and abroad, credit analysis and evaluation products show three characteristics. The first is the standard credit results (reports). Most countries adopt the credit report standards of Consumer Data Industry Association (CDIA) in America. The second feature is the integration of credit systems. There is a trend of integration of personal credit and enterprise credit. When the enterprise risk is evaluated, the personal credit status of its executives and board members will be considered. Third, credit service products

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become more detailed and risk evaluation is exposed to more precise classification. The single credit risk evaluation has been classified into asset prediction, bankruptcy prediction, repayment prediction and income prediction etc.; and simple scoring products are further refined to be service for data application and tool docking. The scoring product is merely a result of initial screening, which can be used as a model to accept the input of customized customer applications. The current major credit analysis and evaluation products of Bairong Zhixin are mainly the credit granting evaluation products that identify fraud and credit risks, which consist of enterprise credit and personal credit risk analysis. They are applicable in China’s credit market. The company is attempting to extend the credit services to financial marketing and some value-added services, in order to cover the whole business process of financial customers. For example, it provides marketing and traffic-attraction service, anti-fraud service and credit granting evaluation service in the pre-loan module; and offers user value appreciation, risk warning and entrusted debt collection service in the post-loan module (see Fig. 5). Pre-loan approval module

Product sales

Submission of application

Scanning for input

After-loan management module

Approval

After-loan monitoring

Collection

Credit granting evaluation

Identify fraud risk and credit risk

Marketing service

Analyze user behaviors and provide targeted advertising service

User value appreciation

Explore potential customers of “revolving credit + payment by instalments”

Collection after loss of contact

Rebuild the contact with users who have been out of contact and collect debts

Fig. 5 Whole-process Credit Analysis and Evaluation Product System of Bairong Zhixin (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center)

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Anti-Fraud Service

The initial and necessary step of pre-loan credit review is to fight fraud. In traditional banking busines, the fraud risk is mostly seen in the credit card sector. However, in Internet financing business, mainly P2P online lending, it becomes easier and cheaper to make fakes in the virtual Internet, and fraud becomes more severe. Bairong Financial Service provides the anti-fraud service based on massive data through identity information and behavior abnormality check, special list library verification and quantitative fraud risk model etc. The core processes of this service include: rejection upon special list library verification, rejection upon rule quantification, equipment anti-fraud rejection and high-risk customer warning. Anti-fraud business is a focus of the current development of Bairong Financial Service.

4.2

Personal Credit Analysis and Evaluation Report

The personal credit analysis products of Bairong Zhixin include personal authenticity evaluation repot, risk evaluation report, value evaluation report and interest evaluation report etc. (see Table 1). The company plans to further expand personal credit analysis products and services for recruitment, marriage, friending, house rent, car rent and trade etc. At present, the personal credit information analysis business of Bairong Zhixin mainly targets financial customers and ordinary customers. For financial institutions, the company mainly provides Internet credit service to satisfy their demand for lowering credit risk and explore customers’ demand; and for ordinary users, it mainly helps them establish and enhance credit records conveniently and acquire more financial services.

4.3

Bairong Credit Score

The most important result of Bairong Zhixin credit risk modelling is Bairong personal credit score. The credit score ranges between 300 and 1,000. The higher score reflects the lower credit risk and the lower default

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Table 1 Bairong Zhixin personal credit information analysis reports Report name

Module

Meaning

Authenticity evaluation report

ID information verification

Check the consistency of user identity information including the user’s ID card, mobile phone number, e-mail, name and landline telephone number etc Information on the city where the user uses a device to access to the Internet, which is only provided after Bairong code is assigned Check the consistency of the user’s detailed address information Evaluation of the number of changes of the user’s mobile phone number, e-mail, name and address etc Statistical evaluation of the user’s commodity consumption (item name encrypted, detailed to level-1 category such as maternal and child supplies) Verification of credit application and use status, including the user’s ID card number, mobile phone number and device (Bairong code shall be assigned) Information on the operating system of the user’s connected device and type of Internet access, which is provided only after Bairong code is assigned Statistical evaluation of the user’s commodity consumption behavior (item name publicized) Evaluation of the user’s educational background

Online information verification

Risk evaluation report

Position information verification Stability evaluation

Commodity consumption evaluation (item name encrypted) Application information verification

Online behavior evaluation

Commodity consumption evaluation Educational background evaluation Risk evaluation model Initial credit line model

The score of user’s credit risk or policy rules The score of user’s initial credit line or rules (continued)

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Table 1 (continued) Report name

Module

Meaning

Value evaluation report

Sign of small and micro enterprise owner/ enterprise executive Business trip consumption evaluation Payment consumption evaluation Reading interest evaluation (item name encrypted)

The sign suggests whether the user is a small/micro enterprise owner/ enterprise executive

Interest evaluation report

Brand interest evaluation Reading interest evaluation Commodity interest evaluation

Statistical evaluation of the user’s consumption in the air trip Statistical evaluation of the user’s general consumption conditions Statistical evaluation of the user’s media reading preference (item name encrypted, detailed to level-1 category such as finance and economics) Statistical evaluation of the user’s brand consumption Statistical evaluation of the user’s media reading preference (item name publicized) Statistical evaluation of the user’s commodity consumption preference (detailed to level-3 category such as car supplies/ interior/ neck pillow/ headrest)

Data source Bairong Financial Service

possibility. Bairong Zhixin plans to further develop Bairong credit score to Bairong score system, which includes the credit application score, credit behavior score, fraud score and collection score etc. As to Bairong credit score, different score ranges respond to different credit risk levels. [300,400) suggests extreme high risk; [400,500) shows high risk; [500,550) means medium risk; [550,650) is low risk; and [650,1 000] means the extreme low risk. For example, Bairong conducts credit scoring against a borrower applying for a loan from certain commercial bank and acquires Bairong credit score of 653, suggesting the borrower’s extreme low risk. According to the contents of scoring, the borrower has spent little time and money on game and entertainment activities, so the credit score in this category is low (36 points); and by contract, he spends much time and money on education and scientific

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Table 2 Example of a Borrower’s Score Card Selected variables

Variable value

Items where the most transactions are completed

Entertainment, game, animation 3C digital Food & beverage, business trip and travel Clothing and lifestyle Current political news, literature/art, finance & economics, life/ geographical communities, history/society/ humanities/ education, finance & economics/ management, car, tourism/ traffic, health/ medicine, parenting knowledge Q&A, game/animation Only ID or e-mail matched ID and e-mail matched or only mobile phone matched ID and mobile phone matched Mobile phone and e-mail matched or ID, mobile phone and e-mail all matched Only ID or e-mail matched ID and e-mail matched or only mobile phone matched ID and mobile phone matched

Items where the borrower accesses to for the most times

Matching types

Users’ 3C consumption amount Bairong credit score

Credit score 36 46 64 51 48 56 40

35 39 45 49

39 51 54 653

Data source Bairong Financial Service

activities, so the credit score in this respect is higher (56 points). These online behavior variables can help the company identify the borrowing applicant’s credit risk to a certain extent. Table 2 lists a borrower’s score card as an example.

4.4

Bairong Zhixin Credit Analysis Assistant

The credit analysis assistant is a product of Bairong Zhixin that directly targets loan users and covers 700 million users’ data of consumption, reading, social connections, asset and operator. Main users of the credit analysis assistant include individual or enterprise borrowers and financial institutions. In case of any capital demand, individual or enterprise borrowers can apply for loans through mobile App from P2P online lending platform or the institutions in partnership with the credit analysis assistant. They will come to the interface of the credit analysis

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assistance through links, fill in basic information and grant authorization for relevant accounts. After calculation, the system will display the borrower’s credit evaluation results, available borrowing amount, term and interest rate etc. After having acquired customers’ application information, financial institutions can log in the credit analysis assistant to view or download the evaluation report of the user issued by the credit analysis assistant. Then, the financial institutions will review the borrower and complete the subsequent lending processes. The credit analysis assistant provides front-office function, back-office function and other functions. The front-office function includes registration, login, basic information query (including name, ID card number, mobile phone number and e-mail etc.), network account authorization (including mobile phone number, Taobao account and JD account), information updates (including mobile phone number, Taobao account and JD account) and sharing (including WeChat Moments, QQ Space and Sina Weibo). The back-office function is mainly embodied in the institutional back office and management back office, while the former enables financial institutions to view and download users’ evaluation reports and the latter offers services of account (role) management, borrower query and statistics query (by dimensions of time, organization and region). Other functions mainly include the data interface function (see Fig. 6). Registration and login

Account management: add, delete, edit Borrower query

Management back office

Credit Assistant

Statistics query

View and download users’ evaluation reports

Frontoffice functions

Backoffice functions

Institution back office

Data interface

Basic information query: name, ID card, mobile phone number and e-mail etc. Network account authorization: Taobao account, Weibo, WeChat etc. Information updates: mobile phone number and Taobao account etc.

Other functions

Sharing: Moments and Sina Weibo etc.

Fig. 6 Functions of Credit Analysis Assistant (Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center)

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How Is the Effect of Big Data Credit Analysis and Evaluation?

China’s credit system has made remarkable progress through over two decades of development. However, it is still subject to some problems that result in not ideal credit effect on the whole, such as the insufficient credit information and narrow coverage etc. The emerging big data credit evaluation technology provides real-time and dynamic credit evaluation and monitoring based on its unique “dynamic information” mechanism, and has improved the effect of credit evaluation, expanded the coverage, indirectly solved the issue of “isolated information islands” and reduced information asymmetry. As we can see from the online and offline big data credit modelling and the application case of Bairong Zhixin, big data credit evaluation has achieved remarkable effect. First, it has expanded the credit coverage. By the end of November 2013, the credit system of the People’s Bank of China credit center had recorded the credit information of over 830 million natural persons. However, the data features relatively simple dimension. Moreover, only the data of about 300 million natural persons included credit records, and the information on most natural persons in the system was simply identity information. In other words, it actually covered information of over 300 million natural persons. Many individuals have no credit record outside the credit system of the Central Bank. Hence, the financial institution cannot provide a loan to the borrower or release a loan of an amount lower (or higher) than the borrower’s objective credit line. At present, Bairong Zhixin has accumulated the data of about 500 thousand real name users and about 700 million anonymous users. With big data credit evaluation of Bairong Zhixin, proper credit evaluation can be conducted for borrowers outside the central bank’s credit system. In this way, the credit coverage has been expanded, particularly to Internet finance industry where most applicants have no credit record at the central bank. Second, it improves the credit evaluation effect of the credit center of the People’s Bank of China. For borrowers whose credit records can be found at the central bank, Bairong Zhixin can identify the borrowers whose credit status is worse than the judgment of the central bank, i.e.

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the borrowers sugaring up the credit status; and can also identify he borrowers whose credit status is better than the judgment of the central bank. Third, it lowers the non-performing loan ratio of financial institutions. The fundamental goal of credit evaluation lies in identifying and evaluating the fraud risk and credit risk, thus to improve the non-performing loan ratio. Great achievements have been made in this respect according to the business results of big data credit evaluation during the cooperation between Bairong Zhixin and over a hundred financial institutions including banks, microloan companies and P2P companies. This point will be further analyzed below.

5.1

Reduce the Non-performing Ratio of Banks’ Credit Card Business

Banks constitute an important part of Bairong Financial Service’s customer base. Seeing the rapid growth of retail businesses in recent years, banks are faced with increasing pressure on data analysis, data storage and system expansion etc. as well as the huge challenge of how to make use of customers’ data effectively to innovate services. With the expansion of banks’ customer size, banks need to identify the different types of customers, release different cards to different consumers and evaluate the risk and value of credit card users. By reviewing bank users’ evaluation reports and combining online and offline data in credit modelling, Bairong Financial Service can identify the characteristics and value of bank users and therefore solve the issues faced by some bank credit card centers and other financial institutions. According to the results of the experimental cooperation between Bairong Financial Service and three national joint-equity commercial banks ranking Top 10 in China, the non-performing ratio of banks’ credit card business can be reduced by nearly a half if the evaluation reports of Bairong Zhixin are applied to risk control of loans. Examples are given below: For Bank A, Bairong Zhixin made two rounds of tests against 500 thousand real name users, and, based on users’ evaluation reports,

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reduced the non-performing ratio of offline individual users by 1/2 and the non-performing ratio of online individual users by 1/3. For Bank B, Bairong Zhixin made one round of tests against 300 thousand real name users, and, based on users’ evaluation reports, reduced the non-performing ratio of offline individual users by 5/8. For Bank C, Bairong Zhixin made two rounds of tests against 200 thousand real name users, and, based on users’ evaluation reports, reduced the non-performing ratio of offline individual users by 5/7. Meanwhile, the KS value6 of the risk evaluation model improved from 0.28 to 0.497 (see Fig. 7).

5.2

Reduce the Non-Performing Ratio of Microloan Companies

As the most important business of microfinance, microloan mainly targets small and micro enterprises and individuals due to its low credit line. The weakness of microloan is mainly shown in risk control, particularly the pre-loan credit risk evaluation. Bairong Zhixin can establish a model of credit evaluation for online and offline users of microloan companies to lower their non-performing loan ratios. Especially for microloan companies which mainly grant small-amount loans through mobile App, Bairong Zhixin has developed a unique method of risk identification. First, the mobile App will acquire the hardware number of the mobile phone. If the model discovers that the mobile phone of this 6The KS test is a statistical method adopted to conduct statistical analysis over a group of data. It compares the data for statistical analysis to another group of standard data to obtain the deviation between the data for analysis and the standard data. Generally in a KS test, we shall first calculate the cumulative distribution functions of the two groups of observed data that need to be compared, and then calculate the maximum value D of the absolute difference between the two cumulative distribution functions. Then by checking the table, we will determine whether the value D falls into the corresponding confidence interval as required. If yes, it proves that the tested data comply with the requirements; and vice versa. In the model of Bairong Zhixin, the KS value is defined as the maximum difference between the cumulative “bad” customers proportion and the cumulative “good” customers proportion when all applicants in the sample are sorted by the score from low to high. 7The banking industry universally uses KS value to judge the effect of the model in distinguishing good from bad customers. The higher KS value suggests the better performance of the model.

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Data of bank’s application form KS value comparison

KS curve

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Bank’s application form + Bairong Zhixin users’ evaluation reports

KS curve

KS curve comparison Cumulative bad customer proportion Cumulative good customer proportion

Cumulative bad customer proportion Cumulative good customer proportion

Fig. 7 Comparison of KS Values and KS Curves (Data source Bairong Financial Service)

number is used to apply for loans for several times to the same company or several other companies by changing the applicant’s identity information, the applicant using this mobile phone is highly suspected of committing fraud. Second, the mobile App can acquire the geographical position of the applicant. If the applicant’s position is found being far away from the address filled in the application form, the applicant also shows high risk. Microloan company X grants credit through mobile App and accepts both online and offline loan applications. With a credit line between RMB 500 and 5,000, it mainly targets customers in cities below Tier 3 or 4. In the cooperation between Bairong Zhixin and X, Bairong Zhixin performed credit evaluation on its microloan business. After the model of Bairong Zhixin was adopted, the non-performing ratios of online and offline customers of this company declined to 7.57% by 5.36% and to 5.32% by 4.26% respectively (see Table 3).

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Table 3 Comparison of Non-performing Ratios of Customers of Microfinance Company X Current non-performing ratio of users Previous recommended by non-performing Bairong Zhixin Type of customer ratio model By the channel of customer acquisition

Online user Offline user

12.93%

7.57%

9.58%

5.32%

Data source Bairong Financial Service

5.3

Help Financial Institutions Identify the Authenticity of POS Merchants’ Loans

Quite a few financial institutions determine the loan release to merchants by analyzing the transaction records of merchants’ POS machines. They regard the POS transaction records as the merchants’ incomes. However, dishonest small and micro enterprises may deliberately increase the transaction amounts to acquire loans by deception. According to the quantitative modelling experience of Taiwan credit center, 40% of a small and micro enterprise’s credit shall be determined by the credit of the enterprise owner; and 20% shall be determined by the personal credit of upstream and downstream enterprise owners who cooperate with this small and micro enterprise. Bairong Zhixin found through analysis that the credit of small and micro enterprises is highly associated with the personal behaviors of small and micro enterprise owners, as shown in Table 4. Hence, Bairong Zhixin combined the POS transaction records and the personal information of the merchant to predict the cash-out risk and POS loan default risk of the merchant. The results proved that Bairong Zhixin’s model had very good performance in identifying information authenticity and merchants’ behavior characteristics. In this way, Bairong Zhixin can help financial institutions identify the authenticity of POS merchants’ loans.

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Table 4 Risk Level of Personal Behaviors of Small and Micro Enterprise Owners Personal behaviors of small and micro enterprises The merchant’s ID card number and telephone number are consistent with the accumulated data The merchant’s personal credit and enterprise credit scores are high The merchant’s profile has several ID card numbers The merchant shows a high level of consumption of game, animation and entertainment The merchant is highly active in economic management and technology category The key guest of the merchant who pays by card frequently at the merchant’s has several ID card numbers or mobile phone numbers The key guest of the merchant who pays by card frequently at the merchant’s has the same address information with the merchant

Level of risk Low risk Low risk High risk Very high risk Low risk High risk

High risk

Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center

5.4

Improve P2P Companies’ Risk Control and Lower Their Non-performing Ratios

P2P online lending is the major form of Internet finance. However, P2P companies are exposed to a high non-performing ratio due to their weak risk control as well as a high financing cost due to their high credit risk caused by insufficient means of credit investigation. Bairong Zhixin cooperates with a large P2P company ranking Top 3 in China and has introduced Bairong big data credit evaluation model to risk control of the company. Great results have been achieved. This P2P company acquires customers through both online and offline channels. Customers of borrowing can be divided into salary and business group. Generally, the customer non-performing ratio with Bairong evaluation is much lower than the ratio without Bairong evaluation. As to different customer acquisition channels, the overall data matching rate8 for online customers is 66.77%, and the customer 8The overall matching rate means the proportion of customers who have at least one of the certificate number, mobile phone number and e-mail address registered at the P2P platform

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non-performing ratio with Bairong evaluation is 7.57%, 5.36% lower than the ratio without Bairong evaluation (12.93%). The overall data matching rate for offline customers is 43.50%, and the customer nonperforming ratio with Bairong evaluation is 5.32%, 4.26% lower than the ratio without Bairong evaluation (9.58%). As to different customer types, the overall data matching rate for the salary group is 60.48%, and the customer non-performing ratio with Bairong evaluation is 5.56%, 7.89% lower than the ratio without Bairong evaluation (13.45%). The overall data matching rate for the business group is 71.69%, and the customer non-performing ratio with Bairong evaluation is 6.53%, 5.15% lower than the ratio without Bairong evaluation (11.68%) (see Table 5). As demonstrated by the above case, the big data credit evaluation technology of Bairong Zhixin can help customers accurately identify borrowers’ credit risk and reduce the non-performing loan ratio, especially for microfinance businesses such as microloan companies and P2P online lending platforms and banks’ credit card business. Table 5 Customer Non-performing Ratios of Different Channels in the P2P Company Customer Customer non-performing non-performing Overall data ratio with ratio without matching Bairong Bairong Customer classification rate evaluation evaluation By customer acquisition channel By customer type

Online Offline

66.77% 43.50%

7.57% 5.32%

12.93% 9.58%

Salary Business

60.48% 71.69%

5.56% 6.53%

13.45% 11.68%

Data source Bairong Financial Service and Renmin University of China Small and Micro-finance Research Center

consistent with the data in Bairong Financial Service database in the total number of platform customers.

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387

Ensure Data Security and Protect Personal Privacy

While playing a greatly positive role by reducing information asymmetry and controlling credit risk, big data credit evaluation is also exposed to some potential dangers. The primary trouble is information security, i.e. the protection of personal privacy. The data collected for big data credit evaluation come from extensive sources, including offline information such as financial data and public records as well as online information such as shopping consumption, reading habits, social trajectories, online tourism, entertainment and hobbies etc. Tens of thousands or hundreds of thousands of variables can be obtained. These data involve personal sensitive information. Any information leakage will harm the information subject and the whole credit evaluation industry. Therefore, how to safeguard data security during credit evaluation is not only a real concern to the public but also the focus of credit evaluation companies. According to the practical experience of Bairong Zhixin, with the established external regulation environment, Bairong Zhixin mainly safeguards data security through data safety management and internal control strengthening, and strictly conforms to the Level-3 requirements of national classified information system protection. It ensures data security by designing a strict management system and faithfully implementing such system.

6.1

Establish a Strict Data Security Management System

As a new type of independent third-party credit institution, Bairong Zhixin attaches much importance to data security for the sake of its long-term interests. To this end, it has designed strict data protection measures, which control the whole process. The measures are applied once when the processing of raw material is commenced. Raw data will be subject to data desensitization, data segmentation, safety grading of data and apps and encryption and isolation of data in different links of

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data processing, until the whole process ends. Based on data desensitization, the key information of sensitive data [such as PII information] will be concealed and substituted by random strings; the desensitized data will be segmented so that the information of one person will be divided into different parts managed by different employees; the segmented data and apps will be subject to safety grading, increasing the difficulty to identify the specific individual corresponding to the data; and encryption and isolation measures will be adopted during the transmission, storage, backing up and cleaning of data subject to processing, lowering the possibility of data leakage.

6.1.1 Data Desensitization PII information is the sensitive data in Bairong Zhixin database. When a batch of data incorporating PII information enters Bairong Zhixin database, they will be subject to the first automatic processing procedure “PII desensitization”. Suppose a piece of data about “Zhang San” whose mobile phone number is “13,000,000,000” and ID card number is “517,087,198,703,180,917” has entered the database. Through “PII desensitization”, this person’s name, mobile phone number, ID card number and real name contact method will be converted into an anonymous user number similar to “f4&*Gx(8@3Y”. “PII desensitization” is completed automatically by computer program without any employee involved, so employees cannot access to users’ PII information. Later, the code name of “Zhang San” in Bairong Zhixin database will become “f4&*Gx(8@3Y”, and the processing of all information related to this user will be performed based on the code name “f4&*Gx(8@3Y”. Since such information cannot correspond to a specific person in reality, the leakage will not cause great harm. The match between the user’s real PII information and anonymous user number is separately stored inside Bairong Zhixin and subject to the company’s highest level of regulation. Any person or application must complete the following process before accessing to users’ PII information:

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First, the applicant shall report to the company’s data management committee. Upon approval, 3 authorization passwords must be input before the applicant can access to the PII query system. The 3 passwords are respectively managed by 3 members of the data management committee, but the 3 members holding the password are changing regularly. Anyone outside the data management committee has no idea of who are currently the three members holding the three passwords. Second, the three members of the data management committee will personally input the three authorization passwords together. Whenever someone starts inputting the first password, PII query system will deem that the “password input process” starts, and the whole “password input process” must be completed within 3 minutes, otherwise the program timeout will occur and an alarm will be given. After timeout, the whole process shall be started again by inputting the first password. If the three passwords have been correctly input continuously within three minutes, the PII query system will see the “password input process” as a “correct password input process”, otherwise it will be a “wrong password input process”. Only three “password input processes” at most are acceptable to the PII query system every day. No matter whether the “password input process” is correct or wrong, after the “password input process” occurs 3 times in a day, PII query system will automatically close and not accept any password input on that day. Each “password input process”, correct or wrong, will be automatically recorded in the access log of PII query system for analysis and inquiry afterwards. For each time of “wrong password input process”, the alarm module of the PII query system will give an “ordinary-grade” alarm. If three times of “wrong password input process” occur consecutively in a day, the PII query system will release a “severe-grade” alarm. The PII query system is watched by a special employee 7 × 24 hours, so each time of alarm is subject to manual monitoring. The “ordinary-grade” alarm will be automatically sent by the alarm module to the five members of the data management committee in a form of SMS + e-mail. In case of any “severe-grade” alarm, the duty

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officers will contact the five members of the data management committee in person, report the current status to them, investigate the cause and initiate the emergency procedures.

6.1.2 Data Segmentation After PII information desensitization, the real name user’s information can hardly be identified by outsiders, and the security has been improved. However, this is not enough. Bairong Zhixin will perform further security processing against the desensitized information, i.e. data segmentation. In this way, every employee can only access to information of some attributes of a part of anonymous users. The example is given below: Suppose that Bairong Zhixin database has 4 users and each user’s information has 4 attributes. In this case, the complete description of the 4 users can be displayed with the 4 × 6 table (see Table 6). The system of Bairong Zhixin assigns the data of Line 1 and Line 2 with Attribute 1 and Attribute 2 to Employee 1 for management; assigns the data of Line 1 and Line 2 with Attribute 3 and Attribute 4 to Employee 2 for management; assigns the data of Line 3 and Line 4 with Attribute 1 and Attribute 2 to Employee 3 for management; and assigns the data of Line 3 and Line 4 with Attribute 3 and Attribute 4 to Employee 4 for management. With such data segmentation, every employee only sees information of some attributes of a part of anonymous users, thus to further reduce the harm caused by data leakage.

Table 6 Principle of Bairong Data Segmentation Anonymous Line user No Attribute 1 Attribute 2 1 2 3 4

f4&*Gx(8@3Y g4&*Gx(8@3Y h4&*Gx(8@3Y i4&*Gx(8@3Y

Employee Employee Employee Employee

1 1 3 3

Data source Bairong Financial Service

Employee Employee Employee Employee

1 1 3 3

Attribute 3

Attribute 4

Employee Employee Employee Employee

Employee Employee Employee Employee

2 2 4 4

2 2 4 4

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6.1.3 Data and App Grading In addition to data desensitization and segmentation, Bairong Zhixin also grades data and apps by security level and carries out security management regulations corresponding to data and apps of different security levels. Bairong Zhixin grades the data in database by security degree into following levels: First, unique anonymous number of the user. The number should be a meaningless string such as “f4&*Gx(8@3Y”, which cannot respond to a specific individual in reality. Second, the user’s demographic information, including age, gender, occupation and educational background. Third, the user’s behavior information, including consumption, reading and other behaviors. Fourth, the user’s PII information, i.e. the personal identity information or contact method of a user, including name, ID card, mobile phone number and e-mail etc., which corresponds to a specific individual in reality. Among the four levels, the “unique anonymous number of the user” is no longer sensitive, because “f4&*Gx(8@3Y” does not correspond to a specific person. The latter three levels are more sensitive, particularly the last type of data. According to the security grade of data used, Bairong Zhixin classified all apps (internal or external) as follows: First, Type A app, which involves the user’s PII information, including multiple sign matching of the user, mapping between the user’s unique number and PII information and personal information mining etc. This type of app can only be used internally only for the purpose of connecting multiple types of data of the same user and associating these data to the user. Type A app is only used inside the company and is not provided to customers as service. Second, Type B app, which involves the external application of the user’s behavior and demographic information. This type of app is applied to process or desensitize data based on specific demands of financial institutions before the data are provided to outsiders. Third, Type C app, which involves the unique anonymous number of the user, including user matching, personalized commodities and content recommendation etc. This type of app can be use internally and externally, as it outputs the unique anonymous number of the user (such as “f4&*Gx(8@3Y”) and the model results excluding any sensitive information (such as whether

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the user likes the business-trip credit card). By outputting the unique anonymous number of the user, it can match the user of the financial institution; and by outputting the model results excluding any sensitive information, it helps the financial institution make more scientific decisions.

6.1.4 Security Control Process for Data Processing Requirements on Safe Use of Data Use of data refers to the process of utilizing production data to carry out various activities in non-production environment. The data use department of Bairong Zhixin has adopted a series of measures to strictly control the use of data in non-production environment. Such measures include setting up the permissions of data use corresponding to the different safety degrees of the data, controlling the scope of use of the data, clarifying the cycle time for data use, controlling data use within the cycle time and assuring no leakage of data to the outside. Outsourcing personnel and other personnel who are not the employees of Bairong Zhixin shall also observe relevant requirements of the company when using the data, in order to ensure data security.

Requirements on Safe Transmission of Data To transmit data, it is necessary to adopt rational encryption technology based on risk evaluation. In particular, encryption must be provided for the storage and transmission of confidential information and top-secret information. Bairong Zhixin shall conform to following normative practices when selecting and applying the encryption technology: comply with national laws and regulations on encryption technology; determine the protection level based on risk evaluation as well as the type and attribute of such encryption algorithm and the length of the key; and listen to experts to determine the appropriate protection level and select appropriate tools.

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Requirements on Safe Storage of Data Data are mainly stored in media including the hard disk, USB drive, magnetic tape and optical storage medium. Bairong Zhixin shall conform to following practices for storage medium management: assign special personnel to safeguard movable storage medium that contains important, sensitive or key data; when deleting confidential and top-secret data in the storage medium that can be used repeatedly, carry out demagnetization or complete formatting against the medium to avoid any information left in the movable medium, or use special tools to fill in useless information to overwrite the previous information; and authorize the outcoming and incoming of any storage medium and keep relevant records for the convenience of audit tracking. In addition, the company requires that internal employees can only write data processing programs in the computers of sound security protection, when data are stored on the server in a remote machine room. The computers that employees directly operate have no function of data storage or access to public networks, so the operators of these computers cannot export data or send data to public computer networks. In this way, the risk of data export is completely avoided.

Requirements on Backup and Cleaning of Data For data backup, Bairong Zhixin shall use strong media of reliable performance for data backup, such as magnetic tapes and compact discs. Note the data source, backup date and recovery steps on the physical medium of backup data and place the medium in safe environment. In general, the configuration data of servers and network safety devices shall be backed up once a month or before configuration modification, system version upgrading and patch installation. The configuration files of network devices shall be backed up before version upgrading and configuration modification. Operation and maintenance personnel shall keep increment backup of core business data everyday and back up all

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data once a week. In case of any major change to the business system, all core business data shall be backed up. For backup recovery and cleaning of Bairong Zhixin, the operation and maintenance personal shall study out the backup data that shall be tested and the cycle of the test based on different conditions of the business system. When the backup data in some equipment need to be recovered due to general failures caused by equipment failures or operation errors, the operation and maintenance personnel shall follow the abnormality handling process and recover the data. Operation and maintenance personnel shall examine and test the backup media and information regularly whenever possible to ensure their usability and completeness and recover the system within a specified period. Determine the retention period of important business information and the retention period of the archive copy of the documents that shall be permanently preserved. The recovered program shall be subject to regular examination and test, to guarantee that the recovery can be completed within a time period specified. In the recovery strategy, the backup frequency (daily or weekly, incremental or overall) shall be determined based on the importance of data and the frequency of the introduction of new data.

6.2

Internal Control

In addition to the specific business process-related measures on data safety management mentioned above, a sound internal control system is also needed to ensure the strict implementation of the process and further to monitor the execution of data safety management rules. Otherwise, data safety management rules will make no sense no matter how detailed and perfect they are. For this purpose, Bairong Zhixin has constructed a rigorous organizational structure for internal control. Only a small number of staff members can access to core information, who will supervise and restrict each other. Bairong Zhixin sets up three committees and seven functional departments. Among them, the information safety management committee, data management committee and credit compliance

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committee jointly make up the core institution of internal control (see Fig. 8). The Information Safety Management Committee is responsible for guiding the business exploration department, internal control department, customer service department and other relevant business departments to formulate relevant credit internal control systems, reviewing the reports and procedures of finance, compliance, risk evaluation and investment decisions etc., reporting the overall risk management status of the company directly to the board of directors, proposing the improvement and review of the company’s risk control process and monitoring the company management’s implementation of such improvements. The credit internal control systems set up by the Committee include: information submission management system, system user management Bairong Zhixin Information Safety Management Committee

Data Management Committee

Board of Directors CEO

Credit Compliance Committee

Administration Office

Department of Capital and Finance

Platform Departments

Department of Human Resources

Project implementation

Department of Technology Development

Product research and development

Product operation

Data analysis and modelling

Department of Product Operation

Product research and development

Objection handling

Department of Industrial Strategy

Internal control and compliance

Department of Marketing

Policy and industrial research

Data system and maintenance

Data use approval and supervision

Department of Data Platform

Data safety system

Other credit fields

Innovative cooperation with microloan P2P

Innovative cooperation with banks

Department of Innovative Cooperation

Fig. 8 Organizational Structure of Bairong Zhixin (Data source Bairong Financial Service)

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system, information system operation procedures, measures for partners’ data safety control, administrative measures for data quality and objection maintenance, position risk management system, internal risk audit system and administrative measures for information system safety etc. The Data Management Committee is the highest decision making organization of data quality, mainly responsible for making decisions on major data quality matters, supervising the rectification of data quality, reviewing the company’s data quality examination proposal and the examination results and reporting the company’s data quality regularly. The Credit Compliance Committee is responsible for regularly performing self-inspection on credit compliance, preparing the credit compliance report based on the self-inspection results and submitting it to the top management of the company and external regulatory departments, formulating or updating the credit compliance management system in real time according to government policies and laws, reviewing the compliance of the company’s credit businesses and resisting risks and correcting mistakes timely. The Committee also performs review on each project to prevent money laundering, bribery, unhealthy competition or other actions violating national laws and company rules.

Case Comments With economic development and growing marketization in China, the imperfect credit reference system has become a bottleneck that restricts China’s economic development and honest society construction. Increasingly mature technologies including Internet/ mobile Internet and big data etc. have provided vigorous technical support to the development of the credit industry in China. By effectively utilizing big data credit evaluation technology, we can rapidly develop the present backward credit industry and even catch up with or transcend opponents. The case of Bairong Zhixin displays how a private big data company depends on big data credit evaluation technology to provide individual and enterprise credit risk evaluation services to financial institutions. As demonstrated by the practice of Bairong Zhixin, big data credit evaluation can make up the deficiencies of traditional credit technology,

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provide more effective tools for financial institutions to control credit risk, make up the insufficient social credit services in the traditional model guided by the government and fill the gap of the credit market based on advanced technology and efficient market-oriented operation model. A large batch of credit information analysis and evaluation institutions similar to Bairong Zhixin have contributed to promoting social credit system construction while pursuing their own development. Bairong Zhixin is a pioneer in big data credit information analysis and evaluation in China. When developing the big data credit evaluation business, it has encountered the problem that the construction speed of laws and regulations cannot match innovators’ expansion rate. This is a commonly seen problem that shall be paid attention to; otherwise, the “advanced productivity” would be useless owing to the “backward production system”. If big data credit evaluation is compared to “advanced productivity” in credit system construction, then the production system that enables advanced productivity to play its part is the improvement of credit legislation construction, the clarification of regulatory departments and their functions and the creation of effective development model of the credit industry. China’s big data credit evaluation is still in the initial stage. However, the regulatory departments are actively supporting its development and have adopted corresponding regulatory measures though they may not have a deep understanding of it. The current industry regulation is mainly led by the People’s Bank of China. In view of the imperfect institutional design and incomplete laws and regulations, the model of “asking for instructions in the morning and reporting the job in the evening” is adopted. In other words, the credit information service agencies shall report their business development conditions to regulatory departments regularly or irregularly. Nevertheless, the industry is still exposed to the risk of uncertain policies, which may impede the healthy development of the social credit system. This situation shall be improved as soon as possible. Hence, the government shall facilitate the social credit system construction in two ways:

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First, rapidly establish and improve the supporting laws and regulations on credit and relevant business rules. Learning from the experience of credit reference legislation and credit industry development in America and European countries, we find that the development of the credit industry can be facilitated by making independent law on the credit industry, under the premise of full legal protection of personal information safety. The laws and regulations on China’s credit industry were issued late, and the current credit business rules fall far behind big data credit evaluation technology. Moreover, regulations now are mainly provided to regulate traditional credit institutions, which can hardly satisfy the institutional and legal demands of the credit industry in the context of emerging new technologies including the Internet and big data. During credit business development, the collection and use of big data may involve the issues of national information safety, enterprise busines secret and citizen privacy etc. To institutionally safeguard the big data-based development of the credit industry, the legal system of information safety and data management shall be improved, to clarify the rules of data collection, sorting, processing, analysis and use in the big data context and assure the development of the credit industry in compliance with law in the age of big data. Special laws and regulations on personal information protection shall be formulated. The restrictions on credit institutions’ use of personal sensitive information shall be relaxed as much as possible. Priority shall be given to improving the accuracy of the prediction of personal credit status, as long as the essential and basic personal privacy or social concepts were not threatened. Second, improve credit supervision and management. As big data credit evaluation involves new technologies such as the Internet/ mobile Internet and big data, the regulatory departments shall strengthen their own study and keep up with the times. First, only by acquiring proficiency in technologies and business processes related to big data credit evaluation can the regulatory departments formulate credit business rules in line with the characteristics of big data and drive the credit industry to adapt to the requirements of the big data age. Second, regulatory departments shall formulate and implement regulatory measures suitable for the credit industry in the age of

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big data and establish the cross-department cooperation supervision mechanism, such as the collaboration with the Internet Information Office and the Ministry of Industry and Information Technology. Finally, a national credit industry association shall be founded to guide and promote industrial self-discipline and therefore promote the orderly development of the credit industry in the age of big data. On August 19, 2015, the State Council executive meeting approved the Action Plan for Promoting the Development of Big Data. This action plan believes that by developing and applying big data technology, the basic strategic resource, it can advance mass entrepreneurship and innovation, transform and upgrade traditional industries and cultivate the new engine for economic development and new strengths in international competition. The action plan also emphasizes the follows. First, advance the connection and sharing of the government’s information system and public data, eliminate isolated information island, enhance government credibility and promote the construction of the social credit system. Second, conform to the historical trend, guide and support the development of the big data industry, strengthen policy support to enterprises in a market-oriented manner, focus on creating relaxed and fair environment, establish the commercialization mechanism, develop the innovative application of big data in various industries, produce new business forms and models, build a big data production system perfectly matching the demands and make open big data the new driving force to promote entrepreneurship and innovation. Third, strengthen the protection of information safety, improve the industrial standard system and fight data abuse and privacy infringement in accordance with laws and regulations. In this way, various subjects can fairly enjoy the technology, system and innovation benefits brought by big data. These policies are inspiring! We believe that big data credit evaluation represents the cutting-edge technology of the credit industry. As guided by advanced technology, guaranteed by corresponding laws and regulations and vigorously supported by the government, big data credit

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evaluation will definitely promote the rapid development of credit undertakings in China. Under these circumstances, the credit undertakings in China will reach the international leading level and catch up with or even transcend the opponents. Commentator: Mao Jiye9

References An Jian, Liu Shiyu, Pan Gongsheng, Interpretation of Regulations on the Administration of Credit Industry, Beijing: China Democracy and Legislation Publishing House, 2013. Zhang Shaofeng, Big Data Risk Modelling Combining Online and Offline Data, “China Credit Reference”, 2014, 11.

9 Dean

of Renmin Business School, and professor and doctoral supervisor of the Department of Management Science and Engineering, Renmin University of China.

Correction to: Inclusive Finance in China Yan Li and Lin Wang

Correction to: Y. Li and L. Wang (eds.), Inclusive Finance in China https://doi.org/10.1007/978-981-16-1788-1 The original version of this book was published without the funding detail, which has now been updated.

The updated version of the book can be found at https://doi.org/10.1007/978-981-16-1788-1

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 Y. Li and L. Wang (eds.), Inclusive Finance in China, https://doi.org/10.1007/978-981-16-1788-1_11

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