Artificial Intelligence, Fintech, and Financial Inclusion 9780367645687, 9780367645700, 9781003125204

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Artificial Intelligence, Fintech, and Financial Inclusion This book covers big data, machine learning, and artificial intelligence-related technologies and how these technologies can enable the design, development, and delivery of customer-focused financial services to both corporate and retail customers, as well as how to extend the benefits to the financially excluded sections of society. Artificial Intelligence, Fintech, and Financial Inclusion describes the applications of big data and its tools such as artificial intelligence and machine learning in products and services, marketing, risk management, and business operations. It also discusses the nature, sources, forms, and tools of big data and its potential applications in many industries for competitive advantage. The primary audience for the book includes practitioners, researchers, experts, graduate students, engineers, business leaders, and analysts researching contemporary issues in the area.

Innovations in Big Data and Machine Learning Series Editors: Rashmi Agrawal and Neha Gupta This series will include reference books and handbooks that will provide the con­ ceptual and advanced reference materials that cover building and promoting the field of Big Data and Machine Learning which will include theoretical foundations, algorithms and models, evaluation and experiments, applications and systems, case studies, and applied analytics in specific domains or on specific issues. Artificial Intelligence and Internet of Things: Applications in Smart Healthcare Edited by Lalit Mohan Goyal, Tanzila Saba, Amjad Rehman, and Souad Larabi Reinventing Manufacturing and Business Processes through Artificial Intelligence Edited by Geeta Rana, Alex Khang, Ravindra Sharma, Alok Kumar Goel, and Ashok Kumar Dubey Convergence of Blockchain, AI, and IoT: Concepts and Challenges Edited by R. Indrakumari, R.Lakshmana Kumar, Balamurugan Balusamy, and Vijanth Sagayan Asirvadam Exploratory Data Analytics for Healthcare Edited by R. Lakshmana Kumar, R. Indrakumari, B. Balamurugan, Achyut Shankar Information Security Handbook Edited by Abhishek Kumar, Anavatti G. Sreenatha, Ashutosh Kumar Dubey, and Pramod Singh Rathore Natural Language Processing In Healthcare: A Special Focus on Low Resource Languages Edited by Ondrej Bojar, Satya Ranjan Dash, Shantipriya Parida, Esaú Villatoro Tello, Biswaranjan Acharya Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0 Edited by Sheetal S. Zalte, Indranath Chatterjee, and Rajanish K. Kamat Artificial Intelligence, Fintech, and Financial Inclusion Edited by Rajat Gera, Djamchid Assadi, and Marzena Starnawska For more information on this series, please visit: https://www.routledge.com/ Innovations-in-Big-Data-and-Machine-Learning/book-series/CRCIBDML

Artificial Intelligence, Fintech, and Financial Inclusion

Edited by

Rajat Gera, Djamchid Assadi, and Marzena Starnawska

Cover image: Getty Images © First edition published 2024 by CRC Press 2385 NW Executive Center Drive, Suite 320, Boca Raton FL 33431 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2024 selection and editorial matter, Rajat Gera, Djamchid Assadi and Marzena Starnawska; individual chapters, the contributors Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright.com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC please contact [email protected] Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloguing-in-Publication Data Names: Gera, Rajat, editor. | Assadi, Djamchid, editor. | Starnawska, Marzena, editor. Title: Artificial intelligence, fintech, and financial inclusion / edited by Rajat Gera, Djamchid Assadi and Marzena Starnawska. Description: First edition. | Boca Raton : CRC Press, 2024. | Series: Innovations in big data and machine learning | Includes bibliographical references and index. Identifiers: LCCN 2023027008 | ISBN 9780367645687 (hardback) | ISBN 9780367645700 (paperback) | ISBN 9781003125204 (ebook) Subjects: LCSH: Finance--Technological innovations. | Financial services industry--Technological innovations. | Artificial intelligence--Economic aspects. Classification: LCC HG173 .A81427 2024 | DDC 332--dc23/eng/20230616 LC record available at https://lccn.loc.gov/2023027008 ISBN: 978-0-367-64568-7 (hbk) ISBN: 978-0-367-64570-0 (pbk) ISBN: 978-1-003-12520-4 (ebk) DOI: 10.1201/9781003125204 Typeset in Times by MPS Limited, Dehradun

Dedication I dedicate this book to my family: Goli, my spouse, Anahita, my daughter, and Cyrus, my son, who support me in my writing endeavors, even though they rarely read my publications. I also dedicate this book to my students, some of whom do read my publications. Finally, I dedicate this book to those who dare to venture into the world of reading. By: Djamchid Assadi I dedicate this book to my family, Bindu, spouse, Pulkit, son and Aarush son, who inspired me to write, and contribute to my academic and research endeavours. I dedicate this book to my students, colleagues, friends, and mentors who are a constant source of support and inspiration for me. I also dedicate the book to my readers and thank the contributors and my co-authors. By: Dr. Rajat Gera

Contents Editor Biographies....................................................................................................ix Contributors ..............................................................................................................xi Foreword ..................................................................................................................xii Preface.....................................................................................................................xiii Chapter 1

Big Data and Artificial Intelligence for Financial Inclusion: Benefits and Issues ..............................................................................1 Peterson K. Ozili

Chapter 2

The Contribution of AI-Based Analysis and Rating Models to Financial Inclusion: The Lenddo Case for Women-Led SMEs in Developing Countries........................................................................ 11 Hicham Sadok and Djamchid Assadi

Chapter 3

Is the Capital Market Based on Blockchain Technology Efficient for Financial Inclusion? ..................................................... 26 Mohamed Lachaari and Mustapha Benmahane

Chapter 4

Exploring the Regulatory Contexts of Fintech Innovation for Financial Inclusion: The Case of Distributed Ledger Technologies in India........................................................................ 39 Saon Ray, Sandeep Paul, and Smita Miglani

Chapter 5

Financial Inclusion through the Sphere of Solidarity in Corporate Governance: The Cases of Digital Crowdfunding and Conventional Microfinance ........................................................52 Djamchid Assadi and Jack Wroldsen

Chapter 6

Why Do Bank Customers Adopt FinTech Solution: The Case of India .............................................................................................. 69 Neha, Mamta Sharma, and Thomas Monteiro

Chapter 7

A Scientometric and Bibliometric Review of Impacts and Application of Artificial Intelligence and Fintech for Financial Inclusion ............................................................................82 Rajat Gera, Priyanka Chadha, Ashima Saxena, and Saurav Dixit

vii

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Chapter 8

Contents

The Role of Islamic Fintech in Indonesia to Improve Financial Inclusion for Resolving SDGs ........................................................112 Atina Shofawati

Chapter 9

Challenges of Artificial Intelligence Adoption for Financial Inclusion .......................................................................................... 135 Priyanka Chadha, Rajat Gera, G.S. Khera, and Mona Sharma

Index......................................................................................................................161

Editor Biographies Rajat Gera School of Management and Commerce, K R Mangalam University, Gurugram India [email protected] Rajat Gera is a Professor and a Dean (School of Management and Commerce) at K R Mangalam University, Gurgaon, Haryana, India, and has academic, research, and institutional development experience of over 23 years with leading B Schools (IMT Ghaziabad, Fore School of Management, Delhi, BIMTECH, IILM Delhi) and national universities in India, IIT Roorkee, KIMEP Kazakhastan, and Fonty’s University, Netherlands, as a guest faculty. He obtained a Ph.D. in Management from the University School of Management Studies, Guru Gobind Singh Indraprastha University, New Delhi; PGDRM (Marketing) from Institute of Rural Management Anand (IRMA) (1990–1992, Full Time); and B. Tech from Allahabad University. He has over 45 International and National indexed journal publications and is the author of two books. He is visiting faculty for “Centre for Marketing,” Asian Minor Studies, Fontys University, Eindhoven, Netherlands, under the Erasmus Mundus scholarship program of European Commission and at ISS KIMEP, Almaty, Kazakhstan. He has industry experience of over six years with leading Public and Private sector companies in Sales and Marketing and Operations and has been associated with Management development programs for BP, BHEL, BEL, NTPC, ONGC, Airtel, Vodaphone, and various other organizations of repute. His areas of research are e-service quality, service quality structure and measurement, e-learning, corporate reputation, entrepreneurship, green consumer behavior, consumer technology adoption, and innovation. Some of his achievements are nominated as Associate Fellow-Euro Med Academy of Business, 2013–2014, Euro Med Research Institute, Greece, ranked third in Case competition by Aditya Birla Centre, London Business School, 2010 (Gera, R., “Quatrro BPO Solutions: Developing Outsourcing Solutions Innovatively”), and recipient of Erasmus Mundus scholarship by European Commission in 2010. His research paper titled “Evaluating the Effects of Service Quality, Customer Satisfaction, and Service Value on Behavioural Intentions with Life Insurance Customers in India” was awarded as the outstanding Business and Management article for 2017, Tenth Annual Excellence in Research Journal Awards, by IGI Global. He has been awarded best research paper at various International (GLOCER 2021, GCR 2021) and National conferences. Djamchid Assadi CEREN, Burgundy School of Business, France, [email protected] Djamchid ASSADI teaches at BSB and has authored and contributed to several books and published scholarly papers and professional articles. He is also a member ix

x

Editor Biographies

of several academic councils and editorial boards. His research aims to identify the conditions under which the excluded can be included in the economic transactions and social interactions. In this perspective, he explores two avenues: (digital) entrepreneurship and/or technological and social innovations which reduce costs to make the markets of the excluded profitable for conventional companies and specifically financial institutions. The foundations of Djamchid Assadi’s research favor the spontaneous order between individuals and the market economy from the perspective of the Austrian school of liberal thought. Marzena Starnawska Faculty of Management, University of Warsaw [email protected]., pl+48-225534031 Marzena Starnawska works at the Faculty of Management, University of Warsaw. She has authored and contributed to many articles, book chapters, and industry papers. She serves in editorial roles in management and entrepreneurship international journals. She is an active member of EURAM community where she acts as a board member and SIG Entrepreneurship Chair. Her research interests focus on social entrepreneurship, social innovation, social enterprises and their strategies, and social inclusion, particularly in the framework of the institutional approach.

Contributors Djamchid Assadi CEREN, Burgundy School of Business France and Université Bourgogne Franche-Comté France

Peterson K. Ozili Central Bank of Nigeria Nigeria Sandeep Paul University of Texas at Austin Texas, USA

Mustapha Benmahane LARGESS Chouaib Doukkali University El Jadida, Morocco El Jadida, Morocco

Saon Ray ICRIER India

Priyanka Chadha Amity University India

Hicham Sadok Mohammed V University in Rabat Morocco

Saurav Dixit Peter the Great St. Petersburg Polytechnic University Russia

Ashima Saxena NorthCap University India

Rajat Gera CMR University India G.S. Khera Manav Rachna University India Mohamed Lachaari LARGESS Chouaib Doukkali University El Jadida, Morocco El Jadida, Morocco Smita Miglani Institute of Economic Growth India Thomas Monteiro K R Mangalam University India

Mamta Sharma Chandigarh Group of Colleges India Mona Sharma Manav Rachna University India Neha IILM University Gurugram Haryana, India Atina Shofawati Universitas Airlangga Surabaya, Indonesia Jack Wroldsen California Polytechnic State University United States

xi

Foreword To fight poverty, we have been using all our resources to provide education, free meals to children, combat major diseases, provide vaccinations, and increase financial inclusion, among many others. Yet despite our efforts, financial inclusion is less than 10% in poor countries like Burundi and South Sudan. How can we use technology to transform the lives of the millions of people living in poor countries such as these? What kind of financial innovations can help? When we talk about technology, the critical technology which brings hope is artificial intelligence. This technology can garner all our documented knowledge and bring to light, almost instantly, the answers to various questions. Artificial intelligence and fintech innovations are the most transformative forces shaping the financial industry and access to financial services. Can we use these to help the underprivileged? The co-editors of this book, “Artificial Intelligence, Fintech, and Financial Inclusion,” Rajat Gera, Djamchid Assadi, and Marzena Starnawska, have assembled a team of experts from academia, industry, and policy to explore the implications of AI in fintech, with a particular focus on alternative finance and financial inclusion. This book is an important contribution to the ongoing discussion about the role of AI in fintech, providing insights and analysis that will be valuable to scholars, policymakers, and industry practitioners alike. The book regards the poor as the excluded and aims to include them in the exchange mechanism thanks to emerging digital technologies that enable them to generate income on their own and for their trade partners through transactions. I am honored to have been asked to write a foreword for this book because I believe it addresses the opportunities and challenges of AI in fintech for expanding financial services to underserved populations. I commend the co-editors and contributors for their outstanding work. Arvind Ashta April 5, 2023

xii

Preface Welcome to the book Artificial Intelligence, Fintech, and Financial Inclusion. This book covers the intersection of artificial intelligence (AI) and financial technology (fintech) and its impacts. AI and fintech innovations have not only increased the efficiency of the financial industry from automating manual processes to creating personalized services but have also opened boulevards for financial inclusion. There are two main strategic views when it comes to addressing the issues of poverty and inequality as persistent challenges in today’s world: one that sees the poor as “unfortunate” and aims to redistribute wealth (“fortunate”) for a balanced society, and the other that regards the poor as excluded and seeks to include them in the exchange mechanism to generate income on their own. This book focuses on the second view and explores how AI, emerging fintech innovations, blockchain technology, and regulatory issues can reduce transaction costs for the financial inclusion of the excluded. In this perspective, the contributions examine the potential of various applications of AI and fintech to make financial services accessible, affordable, and secure for individuals and businesses previously excluded from the conventional financial system. However, the adoption of AI in fintech also poses significant challenges around data privacy, bias, transparency, job displacement, and increased inequality. It is important to address these challenges to ensure that the benefits of AI in fintech are widely distributed and sustainable. In this book, experts from academia, industry, and policy explore both opportunities and challenges of AI in fintech for expanding financial services to underserved populations. We hope that this book serves as a valuable resource for readers interested in the field of AI, fintech, and financial inclusion and inspires further research and innovation in the field. We would like to thank the contributors to this book for their valuable insights and contributions, as well as the editorial and production teams at our publisher for their hard work in bringing this book to fruition. We extend our sincere gratitude to the readers who will show interest in this book. Rajat Gera Manav Rachna International Institute of Research and Studies, India Djamchid Assadi CEREN – Burgundy School of Business, France Marzena Starnawska University of Warsaw, Poland April 5, 2023

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1

Big Data and Artificial Intelligence for Financial Inclusion Benefits and Issues Peterson K. Ozili Central Bank of Nigeria, Nigeria

1.1 INTRODUCTION In the area of sustainable development, artificial intelligence (AI) is paving the way for new developments. Academics and decision-makers in the policy world are paying close attention to it. The United Nations’ sustainable development objectives for the financial sector include a key component called the financial inclusion agenda. However, little is known about how AI can promote financial inclusion and advance the achievement of the UN Sustainable Development Goals. In this chapter, I look at how AI could help or hinder the fight for financial inclusion. Big data, AI, and financial inclusion are all defined at this point. Big data describes data sets that are so enormous and intricate that conventional data processing methods fail (Grable & Lyons, 2018). Big data is defined as data that is large in volume, changes rapidly, and has a wide range of types (Hammer et al., 2017). AI describes software capable of simulating intelligent human behavior (Kok et al., 2009). To put it simply, AI is the display of intelligence by machines. Another approach to defining AI is the simulation of human intelligence in machines that are programmed to think and act like people. Using formal accounts is a definition of financial inclusion (Allen et al., 2016; Ozili, 2020a). The term “financial inclusion” can also refer to the community’s ability to provide and ensure that all residents have equal access to and use of basic financial services. Several studies (Ozili, 2018; Ozili, 2020b) support this theory. Having access to a formal account, such as a bank account, is the first step towards financial inclusion (Allen et al., 2016). Expanding access to financial services has been a top priority for many governments because they believe it is essential for economic growth. A national financial inclusion plan, which aims to give all people and households access to formal financial services, has been formally unveiled by more than 20 national authorities. As of 2013, both the World Bank and the American Film Institute agreed to that. To achieve financial inclusion goals, some authorities have DOI: 10.1201/9781003125204-1

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Artificial Intelligence, Fintech, and Financial Inclusion

recommended the deployment of a variety of strategies, including but not limited to expanded bank enrollment, financial literacy programs, improved payment system features, mobile banking infrastructure, and digital financing schemes. Optimists predict that big data and AI will fundamentally change the effort to promote financial inclusion. However, no government has declared that it will only use big data and AI to accomplish its goals for financial inclusion. This is due to the fact that big data and AI are still emerging technologies. Politicians are reluctant to attempt novel approaches that they are unsure will be successful. At least for the time being, policymakers won’t use AI and big data if they can’t accurately forecast the advantages and drawbacks of doing so on a national scale. To properly appreciate AI and big data’s value for promoting financial inclusion, it is best to think of them as just another instrument that can be employed for this aim. This way of viewing makes it easier to understand what big data and AI may offer in terms of financial inclusion. These are a few ways this chapter advances the body of work. First, it advances the body of knowledge on financial inclusion by illuminating the potential applications of big data and AI as tools for expanding financial inclusion. It also contributes to the body of data science books by illuminating the use of data analytics to problems in development, particularly those pertaining to financial inclusion. By analyzing the potential benefits and limitations of big data and AI for financial inclusion, it also advances the literature in the third and final way. Finally, it contributes to the body of knowledge by offering a perspective for decision-makers to take into account when assessing the impact of AI and big data on financial inclusion objectives. The remainder of the chapter is organized as follows. A review of the material is presented in Section 1.2. Section 1.3 discusses the benefits of AI for financial inclusion. Section 1.4 discusses the issues with big data and AI for financial inclusion. Section 1.5 concludes the chapter.

1.2 LITERATURE REVIEW A few studies look at how data analytics can lead to better development results. Hilbert (2016) demonstrates that the emergence of big data offers an affordable opportunity to enhance decision-making in the growth of the economy, healthcare, and security. Concerns about privacy and a dearth of human resources, especially in developing countries, are just two of the issues that big data brings up, as Hilbert (2016) shows. Data analytics are being used by the telecom industry to help rural India advance, as stated by Peisker and Dalai (2015). According to Gamage (2016), organizations in the public sector can benefit from big data in a number of ways that improve performance. Ali et al. (2016) study the application of big data analytics for human development and show how big data techniques for development have the potential to transform agribusiness, healthcare, and education. They also demonstrate how big data analytics aid in the eradication of poverty and the resolution of violent conflicts and humanitarian disasters. How big data and AI can aid in financial inclusion is the subject of a growing body of research. Bazarbash (2019) argues that Fintech (financial technology)

Big Data and Artificial Intelligence for Financial Inclusion

3

lending has become a viable option for lowering the high cost of loan and boosting financial inclusion as a result of the rapid development of digital and big data technologies. They demonstrate that the core of Fintech financing is AI (or machine learning techniques). Kshetri (2021) claims that Fintech companies in emerging markets are using cutting-edge data science and technological innovation to reimagine conventional approaches to determining a borrower’s creditworthiness. Óskarsdóttir et al. (2019) found that when call-detail records were included alongside conventional data in credit scoring models, lenders’ performance was significantly boosted. According to Kandpal and Khalaf (2020), the delivery of financial services to economically excluded people can be done in a cost-effective and efficient manner by utilizing AI in banking. Additionally, they contend that big data-driven models can be used to conduct psychometric analyses and gather data that can be used to forecast applicants’ performance, attitudes, and debt repayment behavior. Mhlanga (2020) looks into how AI may affect the accessibility of digital finance. According to the survey, Fintech firms are utilizing AI and its many applications to give the underprivileged, women, children, and small enterprises access to the formal financial system. The research also identifies areas where risk detection, measurement, and management are of concern and where AI has a significant impact on digital financial inclusion. The research also reveals that chatbot customer service uses AI to address the issue of information asymmetry. According to Agarwal et al. (2020), the fintech sector’s use of big data and machine learning can help create credit ratings for millennials, who struggle with the issue of having no credit history. Hammer et al. (2017) list three difficulties in managing large data, including poor data quality, challenging data access, and the need for new skills and tools. An increase in the labor force unemployment rate is predicted by Dirican (2015) as a result of the widespread adoption of robotics and AI by businesses looking to automate their processes through the use of hired or purchased robots. Vladeck (2015) demonstrates the significant potential for societal advancement that the big data analytics age offers. However, the use of big data and disruptive tools has a price. One of the downsides of big data is its insatiable appetite for additional information. Philippon (2019) claims that the use of big data and machine learning would lessen human prejudice towards minorities while also eroding the efficacy of current regulations. Qureshi (2020) highlights the importance of data justice to ensure the fair use of entrepreneurs’ data in big data analytics. According to Ozili (2019), government authorities are resisting innovations in data analytics, especially blockchains, for governance and control reasons. Governments can only fight the ongoing blockchain disruption temporarily, according to Ozili (2019).

1.3 HOW AI CAN HELP EXPAND ACCESS TO BANKING SERVICES/ FINANCIAL INCLUSION The advantages of AI for financial inclusion are shown in Figure 1.1. The benefits are discussed in Sections 1.3.1 and 1.3.2.

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Artificial Intelligence, Fintech, and Financial Inclusion

FIGURE 1.1 The role of AI in financial inclusion. Source: Author.

1.3.1 BIG DATA AND DATA ANALYTICS ADVANTAGES FOR FINANCIAL INCLUSION 1.3.1.1

Big Data Facilitates the Creation of Credit Scores for the Excluded Population The development of big data for financial service providers has enabled the availability of “alternative financial information,” such as data from phone bills, utility bills, internet data subscription bills, and other non-bank transaction data. The information is then used by financial institutions to deduce and foresee an individual’s or a family’s future payment behavior, which ultimately results in a credit score. In other words, individuals who lack the necessary bank information to establish a standard credit score might nonetheless establish credit using alternative financial information. People outside of the formal financial system, particularly excluded adults without access to banking information, will gain the most from this. Examples from everyday life include the “Ciginify” program, which forecasts client behavior using data from mobile phone usage and converts the predicted behavior into a credit score. Social media users in the Philippines can utilize the app “Lenddo” to take advantage of their online reputation in order to qualify for loans. 1.3.1.2

Big Data Enables Financial Services Companies to More Effectively Manage Credit Risk Banks may gain more knowledge about their clients through big data analytics, assess their income and spending habits, and keep a close eye on them to spot any changes in their capacity to make timely loan repayments. The providers of financial services can then take preventative measures by employing appropriate credit risk reduction and management strategies.

Big Data and Artificial Intelligence for Financial Inclusion

5

1.3.1.3

Greater Identity Solutions Are Offered by Big Data Considerably More Effectively Than by Know-Your-Customer (KYC) Regulations KYC rules are a barrier to opening formal accounts and raise costs for financial services providers since complicated KYC software is required to perform customer identification, verification, and validation duties (Gardeva, 2012). Especially for people lacking a legitimate national identification card, new technologies that use publicly available data to rapidly authenticate a person’s identity are needed and can be developed thanks to the application of big data analytics to this problem. 1.3.1.4 Improved Marketing of Financial Services Marketers of financial services may need to rethink their strategies in light of the potential impact of big data. In order to accomplish this, data mining is used. Data mining is an effective strategy for delivering targeted advertisements to individual consumers. Using data mining, banks and other financial institutions can target their online advertisements to customers based on their previous web page visits and the types of media they have chosen to view. This approach is used by Google, the most potent search engine in the world, to serve advertisements to customers. “DemystData” aggregates public domain data on individuals using big data analytics, and then segments the data into client groups based on several parameters using statistical analysis. This makes it possible for financial services companies to target specific clientele with their advertising (Gardeva, 2012). This method can be used by financial service providers to target internet customers and easily promote them with financial products and services. 1.3.1.5

Big Data Can provide inputs that Help Policies and Strategies for Financial Inclusion The best policies are those that are founded on verifiable facts (Hammer et al., 2017). Policymakers can collect high-quality data on demand- and supply-side financial inclusion as well as usage of financial services using big data analytics, to support policy and national financial inclusion objectives. Statistics collected from the demand side should include demographic, spending, cultural, and economic data.

1.3.2 ADDITIONAL ADVANTAGES

OF

AI

IN THE

FINANCIAL INCLUSION SECTOR

1.3.2.1

AI Can Make It Easier for Adults without Bank Accounts to Open Accounts By automating and streamlining the process of creating a bank account, AI can assist in removing the onerous documentation requirements that frequently prevent those without access to financial services from opening a formal account. 1.3.2.2

AI Models Can Provide Customers with Smart and Individualized Financial Goods and Services AI systems are useful for businesses to improve their offerings to customers by building on what they already know about those customers. By analyzing people’s spending habits and recommending appropriate courses of action and niche services,

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these AI-powered applications will have a major effect on broadening access to the financial system. 1.3.2.3 AI Will Enhance Communication and Customer Service In the banking sector, AI is primarily utilized in communication and customer service. AI is used by banks to create customized service plans for their clients. Chatbots are another tool that banks use to assist clients with inquiries regarding banking services and other relevant issues. Those who don’t have ready access to a bank or other financial institution in their area may find these services useful. 1.3.2.4 AI Will Assist in Reducing Fraud AI can aid in preventing fraud by bolstering client authentication with extra safeguards. Customers who prefer to access their banking information from afar can now do so. Cross-verification and direct verification are both made possible. A further area where AI can aid financial institutions is in due diligence, specifically in determining a transaction’s true nature and intent. This will improve the ability of financial institutions to detect fraudulent activity at its earliest stages. 1.3.2.5 AI Helps Establish a Credit History People who don’t have bank accounts can still build credit and get loans with the help of AI applications and platforms. Using a person’s location, contact information, and social media activity, AI-powered applications and systems can make predictions about that person’s likelihood of repaying a loan (Cuevas, 2020).

1.4 A FEW PROBLEMS Before AI may be used as a useful tool for fostering high levels of financial inclusion, a number of obstacles and restrictions must be removed.

1.4.1 AI MIGHT KEEP WEAK PEOPLE OUT

OF THE

FINANCIAL SYSTEM

There is a risk that the widespread use of AI will increase financial system exclusion. This problem is not exclusive to AI. The issue is with technology in general. There will be some people left behind as the economy becomes increasingly digital. Elderly folks and those with impairments are among them. Financial institutions ought to try to lessen the effects of AI on their elderly and fragile consumers.

1.4.2 UNCONSCIOUS BIAS IS INCORPORATED INTO THE CREATION OF AI TOOLS, MODELS, AND APPLICATIONS It is crucial to note that applications and models for AI, whether they are new or current, will always have some degree of unconscious bias. Bias can range in intensity from low to severe in various AI models and applications. As a result, the models might not adequately capture the diverse demands of the unbanked. Furthermore, such models might not account for the differences in the socioeconomic status, gender, and racial composition of the unbanked population.

Big Data and Artificial Intelligence for Financial Inclusion

1.4.3 JOB LOSSES

OR

7

EMPLOYMENT TRANSFERS

By automating routine, repetitive operations that an AI system can do in a matter of seconds rather than the hours or days it takes a human to complete, AI has the potential to destroy thousands of employment in industrialized countries once it is fully implemented. There will be repercussions, though. When routine work is automated by computers or robots, positions at the operational level will become obsolete. There will be more people out of work in the financial system as a result. Additionally, labor union opposition could result from this.

1.4.4 THE FEAR

OF

ENTRUSTING AI SYSTEMS

WITH

DECISION-MAKING

We have a fear of entrusting AI systems with our decision-making. Even humans can make mistakes, therefore there is no assurance that AI will develop into a faultless decision-maker. For this reason, despite the high level of execution AI provides, detractors contend that it cannot be a pursuit of perfection.

1.4.5 AI ALGORITHMS MIGHT NOT HAVE BEEN ADEQUATELY TRAINED DATA

WITH

Having an AI algorithm that can finish a task is one thing. It’s another thing to train the algorithm to handle massive amounts of data effectively and efficiently. For instance, in order for AI algorithms to effectively forecast borrowers’ default risk, they need to be educated using data from millions of borrowers (Kshetri, 2021). But the ethical issue will come up. Is it moral to train or test an AI algorithm using data from actual borrowers? Did borrowers grant permission for that use? Such problems will continue to be very difficult.

1.4.6 LACK

OF

SKILLED AI EMPLOYEES

Competent people may be in short supply if AI systems are to be modeled after machine learning algorithms. Skilled AI employees are already in limited supply in developed countries, and this problem will likely be more severe in developing nations than in wealthier ones (Kshetri, 2021).

1.4.7 THE BOARD’S APPROVAL FOR THE AI’S INCLUSION PROCEDURES IS NOT GUARANTEED

IN

OPERATIONAL

In the majority of corporations, the Board of Directors makes the final decisions. Typically, the Board affirms what it has comprehended. What the Board does not grasp may not be approved. The Board’s resistance to adopting AI solutions to organizational problems is frequently the result of their ignorance of technology (Kshetri, 2021).

1.4.8 HANDLING INACCURATE DATA IS

A

PROBLEM

In general, having perfect data at the initial stage of extraction is challenging. This is due to the possibility of data reliability problems, particularly if the data have

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Artificial Intelligence, Fintech, and Financial Inclusion

been gathered from numerous sources utilizing various data warehousing processes and styles. Furthermore, data may be spread across numerous servers, making data extraction a tedious and time-consuming process.

1.4.9 PRIVACY

AND

SECURITY CONCERNS

WITH

CLIENT DATA

The privacy of customer data or information is a major issue with big data. The issue of client protection will surface when employing large data. The appropriateness of certain information for usage by suppliers of financial services must therefore be clearly delineated. Concerns about the safety of client information in the possession of third parties will also arise, whether obtaining the client’s permission is necessary before allowing third parties to access the information and whether third parties are only using the client’s information for the specific objectives for which they have been given permission. These are problems that might be challenging to solve. As privacy rules tighten, AI may not be able to realize its full potential.

1.5 CONCLUSION The main benefits and drawbacks of big data and AI for financial inclusion are outlined in my conclusion. The financial services sector may use the combination of big data and AI in a variety of ways, but one promising use is to increase the sector’s capacity for risk management. Big data and AI provide advantages for financial inclusion because they will make it easier to offer intelligent financial goods and services to adults who are banked and to make the process of opening an account simpler for adults who aren’t banked. One of the issues raised was the need to teach AI algorithms how to understand extremely large customer data. The lack of qualified AI professionals is another problem. The worry that AI would create unemployment in the financial ecosystem is another problem. Moreover, unconscious bias may be included in the design of AI models, apps, and systems, which could lead to the financial exclusion of vulnerable populations including the elderly and people with disabilities. Finally, stricter privacy rules might prevent AI from reaching its full potential. The message is that even if the age of AI and big data holds out a lot of promise for society, there are some concerns that must be properly considered. There are many ways in which big data and AI can improve the delivery of financial services, but one area where they may shine is in the area of risk management. Big data and AI are two possibilities for boosting financial inclusion, but they are not the only ones. Data protection will become increasingly challenging as the big data and AI era increases the thirst for more data. The hazards and rewards of AI and big data should be balanced by policymakers who are interested in these advances. Financial regulators should take precautions to prevent the financial system from becoming unstable as a result of AI-driven advancements in the financial ecosystem. Academics with an interest in these subjects should determine the moral boundaries of AI and big data in the supply of financial services.

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Future studies can look into how blockchain technology affects financial inclusion. The short- and long-term viability of the financial costs associated with implementing big data and AI in financial institutions can be investigated in future studies.

REFERENCES Agarwal, S., Alok, S., Ghosh, P., & Gupta, S. (2020). Financial Inclusion and Alternate Credit Scoring for the Millennials: Role of Big Data and Machine Learning in Fintech. Working Paper, National University of Singapore. Ali, A., Qadir, J., ur Rasool, R., Sathiaseelan, A., Zwitter, A., & Crowcroft, J. (2016). Big data for development: Applications and techniques. Big Data Analytics, 1(1), 1–24. Allen, F., Demirguc-Kunt, A., Klapper, L., & Peria, M. M. (2016). Foundations of financial inclusion. Journal of Financial Intermediation, 27, 1–30. Bazarbash, M. (2019). Fintech in financial inclusion: Machine learning applications in assessing credit risk. International Monetary Fund. Cuevas (2020). AI for financial inclusion: Banking the unbanked. Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia-Social and Behavioral Sciences, 195, 564–573. Gamage, P. (2016). New development: Leveraging ‘big data’ analytics in the public sector. Public Money & Management, 36(5), 385–390. Gardeva, A. (2012). Four ways big data will impact financial inclusion. Centre for Financial Inclusion. Blog Post. Available at: https://www.centerforfinancialinclusion.org/fourways-big-data-will-impact-financial-inclusion Grable, J. E., & Lyons, A. C. (2018). An introduction to big data. Journal of Financial Service Professionals, 72(5), 17–20. Hammer, C., Kostroch, M. D. C., & Quiros, M. G. (2017). Big data: Potential, challenges and statistical implications. International Monetary Fund. Hilbert, M. (2016). Big data for development: A review of promises and challenges. Development Policy Review, 34(1), 135–174. Kandpal, V., & Khalaf, O. I. (2020). Artificial Intelligence and SHGs: Enabling Financial Inclusion in India. In Deep Learning Strategies for Security Enhancement in Wireless Sensor Networks (pp. 291–303). IGI Global. Kok, J. N., Boers, E. J., Kosters, W. A., Van der Putten, P., & Poel, M. (2009). Artificial intelligence: Definition, trends, techniques, and cases. Artificial Intelligence, 1, 270–299. Kshetri, N. (2021). The role of artificial intelligence in promoting financial inclusion in developing countries. Journal of Global Information Technology Management, 24(1), 1–6. Mhlanga, D. (2020). Industry 4.0 in finance: The impact of artificial intelligence (AI) on digital financial inclusion. International Journal of Financial Studies, 8(3), 45. Óskarsdóttir, M., Bravo, C., Sarraute, C., Vanthienen, J., & Baesens, B. (2019). The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics. Applied Soft Computing, 74, 26–39. Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, 18(4), 329–340. Ozili, P. K. (2019). Blockchain Finance: Questions Regulators Ask. In Disruptive Innovation in Business and Finance in the Digital World (International Finance Review, Vol. 20) (pp. 123–129). Emerald Publishing Limited. Ozili, P. K. (2020a). Financial Inclusion Research around the World: A Review. In Forum for Social Economics (pp. 1–23). Routledge. Ozili, P. K. (2020b). Theories of Financial Inclusion. In Uncertainty and Challenges in Contemporary Economic Behaviour. Emerald Publishing Limited.

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Peisker, A., & Dalai, S. (2015). Data analytics for rural development. Indian Journal of Science and Technology, 8(S4), 50–60. Philippon, T. (2019). On fintech and financial inclusion. National Bureau of Economic Research. Working Paper 26330. Qureshi, S. (2020). Why data matters for development? Exploring data justice, microentrepreneurship, mobile money and financial inclusion. Information Technology for Development, 26(2), 201–213. Vladeck, D. C. (2015). Consumer protection in an era of big data analytics. Ohio NUL Rev., 42, 493. World Bank (2013). Financial inclusion strategies database.

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The Contribution of AI-Based Analysis and Rating Models to Financial Inclusion The Lenddo Case for Women-Led SMEs in Developing Countries Hicham Sadok Mohammed V University in Rabat, Morocco

Djamchid Assadi CEREN, Burgundy School of Business, France

2.1 INTRODUCTION Decisions based on algorithms become paramount in many areas such as medical diagnostics, predictive justice, facial recognition, fraud detection, job search, or access to higher education (ACPR, 2018). The world of finance is obviously not immune to this revolution which gives hope for productivity and better management of risks, along with fears of mass destruction jobs and a widening of the digital divide (Bostrom, 2017). AI is not the first technological “disruption” which defies the operating model of traditional banks. As Bill Gates said in 1994: “Banking activities are necessary, but banks are not”1. Obviously, the question of how the bank operates about the emergence of AI is legitimate, especially since the covid crisis reveals that the exclusive consideration of macroeconomic criteria in economic stability is proving to be outdated. The inclusion of humans into the system is more than ever a priority. In this contribution, we address the impact of artificial intelligence (AI) on the analysis of credit risk, generally based on a qualitative approach of business expertise and customer relations. The credit worthiness is a cardinal issue not only for banking sector but also for the economic and societal development. Accordingly, credit rating models impact financial stability, employment and economic growth. DOI: 10.1201/9781003125204-2

11

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Artificial Intelligence, Fintech, and Financial Inclusion

They determine which households can acquire real estate, which companies can finance their investment programs and escape from bankruptcy. We focus more specifically on the process of developing AI-based rating models for granting or refusing loans. AI exploits disparate digital data for a better analysis of credit risk and capital allocation to the excluded whose creditworthiness is generally evaluated by conventional data and statistical score models, often limited to payment history and income (Sadok et al., 2022). We submit the following research inquiry: do AI-based models of analysis and rating which consider social and relational characteristics of applicants lead to the financial inclusion of those that conventional models regularly exclude because of riskiness. The contribution goes beyond the technical subject of access to bank credit and opens the discussion for the economic and societal level of the bottom of the pyramid. To answer this question, we structure the remaining part of the paper as follow: First, we review the literature on the impact of AI solvency and credit granting. We note in this point, first, the effects of the emergence of AI on credit and risk analysis, then, the contribution of Big Data in improving this process before discussing the social and economic impact of the use of AI in the democratization of access to credit enabling economic inclusion. Second, we articulate the main conceptual with the practical elements of a case study. To this end, we synthesise the impact of the new credit rating developed by FinTech Lenddo to facilitate access to financing for the benefit of SMEs led by women in Ethiopia. Then, before concluding, we will analyse the limits of this use.

2.1.1 REVIEW

OF

LITERATURE: AI

AND

CREDITWORTHINESS ANALYSIS

AI is the driving force of the 3rd economic transformation in the 21st century after industry in the 19th century and computers in the 20th century (Baldwin, 2019). The concept of AI is born in 1950 with the Alan Turing test: if a person chatting with multiple parties is unable to discern which is a computer, the computer passes the test (Agrawal et al., 2016). AI is defined in 1956 by Marvin Lee Minsky, as computer programs which devote themselves to tasks requiring mental processes. AI progressively leads to machine learning (ML)2 which enables programs to learn and improve through experience instead of being ruled by human programmers. Deep learning is a subset of machine learning extracts features relevant to a problem (Le Cun, 1987). The emerging trend of AI is fusing data from various origins (Pearl and Mackenzie, 2018). Learning algorithms discover but cannot explain. There is almost unanimity about the impact of AI on the improvement of work efficiency and economic growth around (Accenture, 2017; McKinsey, 2018; Brockbank et al., 2018; Durai et al., 2019, Sakka et al., 2022). Some authors invite however to consider the Solow paradox and postpone the emergence of positive impacts. Gantz and Michaels (2015) estimate that robotics and AI cause only a growth of 0.4% of GDP per year in the seventeen leading industrial countries between 1993 and 2007. Brynjolfsson and McAfee (2014), Furman and Seamans (2018) and the OECD (2019) believe significant AI-based gains of productivity happen beyond the short or medium terms.

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13

The debate on the date of the definitive appearance of productivity gains induced by IA does not cast doubt on the transformation of the organization of work for more agile and less costly new forms of procedures. Baldwin (2019) asserts that AI would intensify the dematerialization and disintermediation of production and trade, accelerate the offshoring of jobs and shorten value creation chains and decisionmaking circuits within organizations and ecosystems. Boston Consulting Group (2018) reports that 32% of banks in China have already adopted AI in their daily process, compared to 22% in the United States and 20% in Germany and France. In the following, we explore the literature to find out how AI impacts the banking operation and processes regarding productivity gain. 2.1.1.1

AI and Productivity Gains in Credit Analysis and Scoring Procedures AI tools modify the relationship between machines and humans and thereby the marginal costs of production, delivery, maintenance, control and communication “tend towards 0” (Rifkin, 2013). In the banking sector, by the fact of improving the analysis of massive data flows, AI and ML techniques improve the operations, credit scoring, portfolio management, detection of fraud, optimization of collection strategies and detection of weak signals and thereby transform the jobs of traditional analysts and reports of credit scoring. Major banks such as HSBC, Citibank and Standard Chartered Bank adopt proceed to intelligent automation of tasks and redefine transactions analysis in real time, trade finance, partner relations management and trust building (Leloup, 2017). The algorithms developed by AI show an undeniable advantage of productivity gains over the usual parametric scoring approaches in particular for analysis of risks (Benkhayat et al., 2015; Mullainathan and Spiess, 2017; Charpentier et al., 2018; Athey and Imbens, 2019; Athey, 2019, El Alami et al., 2015). A comparison between the traditional and the AI-based approaches of credit rating illustrates this statement (Mahboub and Sadok, 2023). The traditional approach building a credit rating report consists in many steps. Frist, the statistician in charge groups discrete explanatory variables and discretizes continuous variables before reducing them to groups of modalities to maximize the discriminating power of the variable. This involves capturing potential non-linear effects and reducing the influence of extreme values or uncorrected outliers. The number of classes and the discretization thresholds are determined by iterative algorithms built with the objective of maximizing a measure of association of Cramer’s V type or chi-square test, between the target variable and the variables explanatory. The second step consists of analyzing to verify whether the predicting variables are not too correlated with each other. The expert then removes redundant variables according to a principle of parsimony. The third step concerns the selection of the explanatory variables. Within the framework of a given score model (for example, a logistic regression), the statistician identifies the variables which best predict the default. This selection can be carried out manually or automatically. Conversely, the use of classification or algorithms based on the decision tree or random models, makes obsolete the work of discretization of continuous variables and the methods of grouping. Essentially, these techniques independently determine

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Artificial Intelligence, Fintech, and Financial Inclusion

the optimal discretization and groupings of modalities. The analysis of correlations between predictors is less critical in the sense that most AI algorithms can incorporate strongly correlated predictors. More generally, the advantage of AI algorithms is precisely to use the data to determine the optimal functional form of the model within the meaning of a certain criterion. This therefore makes the step of selecting the explanatory variables of the score model used by most banks for risk management obsolete. These productivity gains in the risk modeling process associated with AI are now evident in the banking sector. Grennepois et al. (2018) point out that the predictive performance of AI algorithms is generally robust to the non-imputation of missing values, the presence of strong correlations between certain explanatory variables, the non-grouping of categories of discrete variables and the non-discrimination of continuous variables. This robustness therefore potentially makes it possible to limit the preprocessing steps on the data (Mahboub and Sadok 2023). Beyond productivity gains, limiting data preprocessing can also reduce any modeling bias since AI lets raw data express itself. The use of AI thus allows greater automation of the credit granting process, including in the phase of building and reviewing risk models. Based on data on mortgage loan processing times in the United States, Fuster et al. (2018a) show that banks using AI process loan applications around 20% faster than other lenders without noticeable deterioration in the quality of file selection. The democratization of major AI-based algorithms programs like SAS, R, and Python increases the productivity and predictive performance of credit risk modeling. It is by focusing on these AI techniques that the study conducted by Fuster et al. (2018a) show that companies with alike characteristics receive financing via FinTech and rejection by traditional banks. This result suggests that FinTech tools contribute to the financial inclusion. 2.1.1.2 The Contribution of Big Data to the AI Process for Credit Analysis When discussing the contribution of AI to credit analysis, it is difficult to separate the gains that result from algorithms and those that result from the use of new data. In the case of a loan, the explanatory variables generally include information on the nature of the loan, the characteristics of the borrower (age, income, marital status) and his banking history. The typical example is the FICO3 score widely used in the US financial industry to assess the creditworthiness of retail clients. This score is built from different factors such as payment history, amount of outstanding debt, length of credit history, recent opening of new accounts, etc. Conversely, new and big data comes from much more varied sources, often made accessible by digitizing customer relationships (digital fingerprint data) or from data originating from new sources of customer information, such as social networks. Sometimes they can be very disparate sources of information with no apparent link to the creditworthiness of clients. The new data can be collected by traditional financial actors (banks) or by new financial actors using AI (FinTech). We must distinguish here two types of use of this data depending on the type of financial institution considered. The most common use concerns FinTechs similar to credit institutions, such as credit platforms, online

The Contribution of AI-Based Analysis and Rating Models

15

banks, neobanks, as well as certain merchant sites. In this case, the data is used directly to construct scores which internally determine the granting of credit, the financing conditions, and/ or which serve to control the risks of the loan portfolio (El Maknouzi and Sadok, 2021). The second use of this new data is the work of consulting firms whose goal is to build a credit risk score that is sold to a lending institution (Sadok and El Maknouzi, 2021). This outsourcing of the collection and use of new data is therefore similar to the operation of traditional scores such as FICO in the USA but, depending on the nature of the data collected, it raises specific questions in terms of legal liability and regulation. Thus, FinTech using Big Data Scoring offers to integrate social network data relating to the everyday life of the person or company applying for credit, as well as its manager. This data is collected thanks to the browsing mode used (IP address, device used, browsing behavior, etc.) in connection with the requests made by the people concerned by the loan. The start-up NeoFinance uses data relating to the quality of the job held by the loan applicant and the quality of his professional relations via the LinkedIn network. FinTech Lenddo aims to develop financial inclusion in developing countries, by mobilizing non-traditional data to provide both a credit rating (Lenddo score), but also an identity verification (Lenddo verification). Lenddo’s strategy is clearly to bypass the need for an official credit score (like FICO or credit bureau4) to allow as many people as possible to access credit. Their rating mobilizes different sources of information: customer activity on social networks (Facebook, LinkedIn, Twitter, etc.), connections with people at risk, navigation data from smartphones or computers of the person who request the loan. As such, the ZAML (Zest Automated Machine Learning) solution from FinTech ZestFinance, founded by the former IT director of Google, is very illustrative. This technology makes it possible to build a score from very disparate information such as digital fingerprints, the number of times the client has moved, the intellectual level measured by the vocabulary used in writing and the typing errors detected, etc (Jagtiani et al. Lemieux, 2019). This development reflects the informational richness of these new data sources compared to traditional data. Tang (2019) studies the economic value that borrowers attribute to certain confidential information disclosed during a loan application. Using a controlled experiment from a Chinese online lending platform, she estimates that, on average, a borrower can benefit from a reduction of around 9% in the net present value of their loan when they provide ultra-confidential information in return such as the history of these contacts and telephone numbers. Finally, this new data can also be mobilized by other commercial actors to better assess the risk incurred with the stakeholders. Berg et al. (2019) note the case of a large e-commerce company based in Germany which allows its customers to pay for their purchases made online only upon receipt of the goods within a period of fourteen days. Every transaction is like a short-term consumer loan, which assumes that the company is able to accurately assess the creditworthiness of its customers. To do this, this company uses the digital fingerprint data left by customers during their browsing giving rise to an online purchase. These achievements therefore raise the question of whether big data used by AI has a different impact on society when it is implemented as a tool for banking predictions in credit analysis.

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Artificial Intelligence, Fintech, and Financial Inclusion

2.1.1.3 The Socio-economic Impact of AI in Strengthening Inclusion The preceding reflections show that measuring the effects that AI brings to credit analysis by mobilizing new data comes up against the paradoxes attached to AI itself with regard to law and ethics, which we discussed in the point below, and especially their added value when compared to traditional credit analysis models used by banks. On the theoretical level, Wei et al. (2016) assess the impact of AI’s use of social media data on the quality of credit scoring models. The authors conclude that clients who wish to improve their score will be led to establish fewer links in their social networks and to favor contacts with people belonging to the socio-professional category less exposed to the risk of job loss, such as civil servants. Thus, the effect of the strategic behavior of credit applicants can lead banks in their quest for predictive performance through scoring models based on AI to have ambiguous results. On the one hand, the use of social networks tends initially to improve the accuracy of the scores since we increase the body of information by mobilizing diverse and in-depth information on the life and behavior of borrowers.However, this use can generate, with the learning effect that loan applicants will have on the automated process, adverse selection of customers and greater fragmentation of networks, and therefore information, which tends to reduce predictive gains to what we can already know without even resorting to AI. Empirically, the contribution of AI to improving financial decision-making is the subject of much debate. Several FinTechs (Lenddo, Big Data Scoring, etc.) express the validity of the use of AI algorithms to express the masses of data, which is precisely their core business. Others, like ZestFinance, reject the use of AI in the processing of social media data, questioning both its usefulness and the legitimacy of its use. Berg et al. (2019) show that taking into account the customer’s fingerprint can considerably improve the predictive performance of scoring models of an ecommerce company subjected to the problem of delivery before payment of the customer. Similar results were obtained by Frost et al. (2019) in their analysis of the Argentinian platform Mercado Libre, specializing in the distribution of loans to small businesses. The authors show that AI-based credit scoring techniques outperform credit bureaus ratings in predicting loss rates, especially for riskier companies. More recently, Óskarsdóttir et al. (2019) model credit cardholder default by using detailed mobile phone statements to reconstruct the cardholder’s social network. The anonymized data concern 90 million telephone numbers, 2 million bank customers and include various socio-demographic and banking information. Each individual’s network is synthesized by around 200 statistics describing the client’s ties to other clients who have had late payments in the past or other banking incidents. The authors show that taking into account the characteristics of the telephone network makes it possible to increase the precision of the credit and solvency analysis model compared to that obtained only on traditional socio-demographic or banking data, which opens up interesting prospects, especially for developing countries, which have few bank accounts, but where cell phone use is widespread. Bazarbash (2019) argues that traditional banks often refrain from assessing the credit risk of small borrowers because low repayment expectations and potentially

The Contribution of AI-Based Analysis and Rating Models

17

high loan risk do not cover costs. By automating the rating process and using AI and its new sources of data, tech banks can better assess the creditworthiness of riskier smaller borrowers by frequently taking out small loans and monitoring their repayment behavior. Thus, banks can guarantee better access to credit, generally for the financially excluded and small businesses that do not have the financial guarantees required. Schweitzer and Barkley (2017) analyze a large database of loans taken out by small American companies in 2015 and their study shows that companies receiving financing through FinTech platforms share the same characteristics as those to which traditional banks have refused credit. Jagtiani and Lemieux (2019) show that alternative sources of data used by LendingClub’s AI algorithms allow certain clients who would have been classified in the riskiest categories according to traditional criteria to become “good” risks and thus obtain loans on better terms. About 8% of borrowers rated A (best) by LendingClub’s scoring method had FICO scores below 680 (poor or fair), and 28% of borrowers rated B had FICO scores in the same range. Similarly, Berg et al. (2019) find that the fingerprint data used by AI makes it possible to accept borrowers with a fragile profile, but who would not have been accepted solely on the basis of credit scores from traditional databases. In the same line, Bartlet et al. (2019) show that AI contributes to reducing ethnic discrimination in the American mortgage market. They use different databases for this, including the Home Mortgage Disclosure Act, which covers nearly 90% of mortgage loans in the United States over the period 2009–2015. Their results show that traditional lenders charge Latin American and African American borrowers 7.9 and 3.6 basis points, respectively, all other things being equal. Globally, this represents almost $765 million per year in additional interest. However, if FinTechs also discriminate, the discrimination in this case is 40% less than that of traditional banks. Likewise, they observe that traditional lenders reject requests from Latin Americans and African Americans about 6% more often than they reject requests from clients who are not from these minorities. At the macroeconomic level over the period 2009–2015, this represents 0.74 to 1.3 million Latin American and African American clients whose loans could have been accepted if there had not been discrimination. ZestFinance claims that if its AI credit and risk rating tool were applied across the United States, it would reduce the gap in mortgage approval rates by 70% between white and Hispanic borrowers, and the gap 40% with African-American borrowers, allowing more than 172,000 people each year to become homeowners. Consequently, and it is thanks to this financial inclusion due to technologies that the barrier of access to the traditional financial system can facilitate the access of the excluded to financial services. In recent years, thanks to technology, much progress has been made in promoting financial inclusion for the unbanked. World Bank research indicates that in the three years to 2014, the number of unbanked adults worldwide fell by 20%, from 2.5 billion to 2 billion, dropping the percentage of holders of accounts from 51% to 62%5. In developing economies, this represents a 13% increase in account holdings to 55% during this period. However, holding a bank account is not an end but a means to an end. While it is widely recognized as essential for achieving financial

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Artificial Intelligence, Fintech, and Financial Inclusion

inclusion, it is essential for ensuring progress and reducing poverty. It allows the excluded to project themselves into entrepreneurship and develop their business, to invest in knowledge and education, to better manage risks and escape fragility. Financial inclusion therefore has a positive impact on income, employment, consumption, certain aspects of physical and mental health, as well as improved access to financial services, including access to credit. All of this goes a long way to reducing income inequalities and accelerating the country’s growth. This is why, within the framework of the United Nations Sustainable Development Goals, greater financial inclusion is a major objective to enable excluded economic actors to increase their resilience and seize economic opportunities. There is growing awareness of this need and policymakers and regulators are starting to prioritize financial inclusion in development across the financial sector. Several governments are implementing comprehensive measures to achieve this goal. Therefore, the application of technology in general or AI in particular in finance and more particularly in credit analysis can seem powerful and even be a factor of social inclusion. But it can also be frightening if it escapes human control and develops a propensity to make mistakes in a totally different way than humans (Houdé, 2019). “Weak AI” where an algorithm limited to a risk management task for example is now acceptable and operational. But “strong AI” with will, consciousness and without emotions is still a fantasy that raises many questions about the limits of AI.

2.1.2 THE RESEARCH METHODOLOGY CREDIT

OF

CASE STUDY: LENDDO’S UNIVERSAL

In line with our research inquiry relative to the impact of AI on credit scoring for financial inclusion, we adopt the empirical method of case-study to scrutinize the emerging issue of AI-based credit scoring in the real-life settings in absence of prior explanative and insightful theories (Eisenhardt, 1989; Stake, 1995; Galunic and Eisenhardt 2000; Yin, 2017). We investigate the case of Lenddo which in collaboration with the World Bank Group uses AI- FinTech to facilitate psychometric assessment for credit scoring as part of the Women Entrepreneurship Development Project (WEDP). Founded in 2011 by co-founders Jeffrey Stewart and Richard Eldridge, Lenddo initially operated an online lending operation to ascertain customers’ financial stability and improve financial inclusion for at least a billion people in developing countries around the world. They started in three countries: the Philippines (since 2011), Colombia (since 2012) and Mexico (since 2013). In October 2017, Lenddo merged with company Entrepreneurial Finance Lab (EFL), and was subsequently named, LenddoEFL. The company started when Stewart and Eldridge were running technology companies in the Philippines and several other emerging markets. “Their skilled young staff often asked for loans … With repayment rates in microfinance as high as 98 percent in multiple regions, the opportunity seemed obvious.” In January 2015, Lenddo announced expanding its credit scoring and identity verification solutions to third parties globally. Lenddo’s target credit customers are

The Contribution of AI-Based Analysis and Rating Models

19

primarily subject to emerging economies, in which financial records are limited, while social data is prevalent. At the peak of its global operations, the company had an extended presence across Latin America, Africa, South Asia and South East Asia and had on boarded more than 200,000 applicants every month. Launched by the Ethiopian government, the project Women Entrepreneurship Development Project (WEDP) intends to increase the incomes, employment and growth of micro and small businesses (MPEs) directed and owned by women entrepreneurs in Ethiopia6. Since little is known about the use of psychometric elements for assessing credit scoring in perspective of financial inclusion, we use a case study approach. Ridder et al. (2009) state that case studies are useful for generating a theoretical contribution. Hence, grounded in our data, we finally extract a theory about the impact of AI-based credit scoring on financial inclusion. Like in many developing markets, Ethiopia is characterized by the “missing middle” phenomenon, where small firms are neither served by commercial banks nor by microfinance institutions (MFIs). Commercial banks rarely lend in amounts below USD 50,000, whereas MFIs mostly offer group loans that do not exceed USD 1,500. Traditional lending methodologies from financial institutions often require data on loan applicants, including their financial statements, credit history, tax records, and legal status. Women-owned MSEs face greater structural and cultural barriers than their male counterparts because they often lack sufficient reliable financial statements, credit histories and assets for guarantee, and thereby are typically deemed riskier by financial institutions. This situation is compounded in Ethiopia, like in other emerging markets where there is no suitable financial sector infrastructure, such as a credit information system, which can help lenders identify creditworthy borrowers. Faced with such limitations, financial institutions rely on excessively high collateral requirements to minimize their exposure and risk. As a result, many women-owned MSEs are excluded from the financial system, while financial institutions miss the opportunity to tap into a pool of potential borrowers. Lenddo bypasses the need for a historical credit score through its own scoring system, based on social data. To apply for the credit, one must first accumulate a minimum score (called “LenddoScore”) of 300. The user is scored based on 3 main factors—their social network activity using data from Gmail Facebook, LinkedIn, Twitter, and Yahoo accounts, a user’s “Trusted Connections” (character references that will vouch for the borrower), and financial performance, if the person is a repeat borrower. As of December 2019, approximately 36,000 women entrepreneurs registered for WEDP. Of these, USD 158.1 million has been disbursed to 13,870 clients. Moreover, a total of 20,744 women entrepreneurs have participated in entrepreneurial trainings across 10 cities, with a 98.6% completion rate. Demand for loans and training shows little sign of abating, as women entrepreneurs from across various sectors, including trade, services, manufacturing, construction and agriculture, continue to register for the project. To study the impact of WEDP loans using Lenddo’s valuation method, a baseline study was carried out from 2017 on 2,369 women entrepreneurs from six Ethiopian cities. The survey instrument used included questions on personal and

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Artificial Intelligence, Fintech, and Financial Inclusion

demographic characteristics of households, socioeconomic status of borrowers, costs, company sales, level of financial literacy, profits, costs, entrepreneurial profile and business knowledge. An interim survey was carried out about three years later with 2,139 companies, or 90% of the reference sample. Since obtaining a loan is not a random assignment, the study used a propensity score matching (PSM) technique to compare the impact of WEDP loans between borrowers and a statistically matched group of non-borrowers. The interim results showed that obtaining a WEDP loan through the assessment process developed by Lenddo had a positive impact on the profitability of companies receiving loans guaranteed by WEDP. The increase in profits is 40.77% higher compared to a control group three years after taking out the loan. The results of these beneficiaries of this credit through the Lenddo assessment process showed that treatment firms had grown incomes by 67.89% (compared to an end-of-project target of 50%) since baseline. On average, each MSE’s annual earnings increased by USD 4,053. Moreover, WEDP clients increased employment in their firms by 58.60% (compared to an-end-of-project of 30%) since baseline. On average, each MSE has hired 2.95 full-time and part-time employees. In total, WEDP firms are employing 89,272 workers, of which 61 percent are female. The most significant impact was on job creation by companies that received loans according to Lenddo’s algorithm, thus increasing their employment by 55.73% compared to the control group. The credit access assessment process developed by Lenddo and made available to the WEDP program has been widely recognized for its achievements in empowering women entrepreneurs and for raising their profile throughout Ethiopia’s financial system. The project model has demonstrated how an incentives-based strategy, coupled with hands-on management and a robust monitoring and evaluation financial system, can converge to successfully address a persistent constraint. According to the World Bank’s 2015 Enterprise Survey7, access to finance is perceived as the top business constraint by enterprises.

2.1.3 THE LIMITS

OF THE

IA APPROACH

FOR

CREDIT ANALYSIS

AI algorithms for credit analysis can detect finer nuances if enough data is available to train the most relevant model possible. However, this flexibility comes at a cost, that of opacity. Indeed, for some AI methods, it is difficult, if not impossible, to know what are the variables and their proportions which are the basis for the predictions of the model. These algorithms are therefore similar to “black boxes” which associate predictions on the target variable with a set of predictors without knowing the origin and the proportions of these predictions. This is particularly true for aggregate methods like bagging or boosting8 which otherwise often have the best predictive performance. Obviously, this opacity raises strong ethical and legal issues, but also in terms of financial regulation when these models are used for decisions affecting the lives of individuals or companies such as the granting of a loan. The reliance on new data is a source of tension between the will of banks using AI and regulators: on the one hand, the desire to measure risk more accurately, and on the other hand, protects the personal data of customers. Thus, banking prudence

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ratios like that of Basel III/ IFRS9 may seem to come into conflict with, for example, the General Data Protection Regulation (GDPR) which is the reference text in Europe for the protection of personal data. However, in the case of risk assessment, this regulation in no way prohibits the use of most of the new data sources currently in use. There are indeed several data anonymization techniques, often used in medicine, which allow personal data to be shared with third parties. Such sharing can take place today in a perfectly secure manner. Nonetheless, in the case of outsourcing of the construction of the risk analysis and assessment model by another entity, the bank as the processing authorizing officer incurs its own responsibility in the event of a breach of these confidentiality protocols. Likewise, how can we ensure that the data transmitted by the bank to build or use this model is not kept beyond this phase of development? Beyond these legal issues, ethical questions arise regarding the use of some of this new data, in particular network data. Degrading the conditions of access to credit for a particular customer, all other things being equal, because he has bad payers as contacts on the networks, for example, is not legally blameworthy, but it clearly poses an ethical problem. So, how can we decide on the social acceptability of this data? Óskarsdóttir et al. (2019) offer a solution inspired by dashboards. In the field of scoring, it is common to discretize the continuous explanatory variables and to assign a score to each of the segments according to their contribution to the detection of the defect. An ethical use of this principle would be to assign a zero score to segments that would disadvantage borrowers, while leaving positive weights to segments that would facilitate access to credit. Applying such an ethical penalty to variables from new sources of information would certainly lead to deterioration in the model’s predictive performance, but would guarantee the socially acceptable character of the use of AI. However, the ultimate criterion that will resolve this ambiguity about the use of new data by AI is that of customer acceptability. Beyond the moral or legal issue, it is a question of knowing whether customers are ready to have some of their personal data used as part of their loan application. In the case of mortgages, for example, we know that apart from the business cycle and the unemployment rate, one of the most important predictors of default is divorce. Therefore, any variable predicting divorce will be a good predictor of default. If a bank used a score based on the AI analysis of extramarital dating sites, would customers be willing to accept this type of approach in order to obtain more favorable loan terms? Another major risk of association between AI and new data sources is the emergence of bias or unfair treatment (ACPR, 2018). Applied to the field of credit scoring, the question is whether AI algorithms can penalize certain populations, or even exclude them entirely from access to credit, without even being sought by the financial institution. Thus, the AI algorithm can select available predictors of variables considered discriminatory such as gender, ethnicity, sexual or political orientation, etc. By definition, the machine does not know what is moral or not, what is legally permissible or not. It cannot therefore define the concepts of ethnic discrimination, sex or religion. Even by taking care to check the conformity of the source data beforehand, nothing guarantees the absence of discriminatory bias in the scoring models developed by the AI. A human modeler in a financial institution will

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not take the moral, legal or reputational risk of including these types of variables in their scoring model, even if the data is available and explanatory. However, the absence of discriminatory variables in the source data does not guarantee the absence of bias in the scoring models. In fact, biases can appear more subtly in an indirect way, through variables called proxy discrimination (Prince and Schwarcz, 2019). The general idea is that discrimination results from the interaction or triangulation of several variables which do not in themselves appear to be discriminatory. For example, an AI algorithm can cross several legal variables such as income and type of housing to implicitly predict place of residence, and use that information to discriminate against customers residing in sensitive geographies. The risk is all the greater since databases have many predictors, since AI algorithms can then use them to identify interactions between a large number of variables. Using a database of mortgages in the United States, Fuster et al. (2018b) show that when moving from logistic regression scoring to an AI approach, black and Hispanic borrowers are the losers overall, while the results remain neutral for white borrowers. Thus, and beyond legal or ethical aspects, technology has value only to the extent that it is ultimately accepted by the customer. This will undoubtedly be the main obstacle to the development of AI in the field of credit risk management, as in many other fields.

2.2 CONCLUSION Traditional approaches to bank credit analysis combining different data preprocessing and parametric statistical approaches, such as logistic regression, offer very good performance. Thus, when we analyze a constant set of information, AI algorithms only offer marginal performance gains, even if they sometimes allow productivity gains in their operating methods. On the other hand, AI techniques make it possible to mobilize new sources of information, which otherwise could not have been integrated into credit risk management models, due to their large size. The use of Big Data and new sources of information by AI then makes it possible to reveal weak signals, whether in the form of interactions or non-linearities, which, without always knowing how to explain them, seem improve assessment of customer creditworthiness. More fundamentally, these aggregate predictive gains sometimes translate at the microeconomic level into individual gains, notably by improving financial inclusion and access to credit for the most vulnerable borrowers (Sadok, 2020). Thus, research in the field of credit risk modeling should not so much tend to develop new methodological classification approaches as to take advantage of new data sources. However, these new sources of data can raise many biases and especially ethical, legal and regulatory questions without even a bank noticing. These opportunities, but also these risks, undoubtedly call for the implementation of a new form of financial regulation based on the certification of AI algorithms and data mobilized by banks (Sadok, 2023).

NOTES 1 https://www.asianbankingschool.com/our-programmes/centre-for-digital-banking/digitaltransformation-banking-is-necessary-banks-are-not

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2 ML is a “set of algorithms intended to solve problems and whose performance improves with experience and data without a posteriori human intervention” ( ACPR, 2018). 3 FICO is an abbreviation for the Fair Isaac Corporation, the first company to offer a creditrisk model with a score. 4 A credit bureau is a data collection agency that collects information about the accounts of various creditors and debtors and provides this information in scoring form to better illuminate the risk on any future business transaction. 5 http://www.worldbank.org/globalfindex 6 World Bank Report May 2020 “Designing a Credit Facility for Women Entrepreneurs: Lessons from the Ethiopia Women Entrepreneurship Development Project (WEDP)” https://openknowledge.worldbank.org/bitstream/handle/10986/34013/Designing-a-CreditFacility-for-Women-Entrepreneurs-Lessons-from-the-Ethiopia-Women-EntrepreneurshipDevelopment-Project.pdf?sequence=4 7 https://www.enterprisesurveys.org/en/enterprisesurveys 8 Bagging involves training a set of classification trees on subsamples of individuals drawn at random. Boosting consists of iteratively training a base model in order to reduce forecasting errors at each stage.

REFERENCES Accenture (2017), “L’IA à l’horizon 2035” rapport, www.accenture.com/fr-fr/companynews-releaseartificial-intelligence-2035 ACPR (Autorité de contrôle prudentiel et de résolution) (2018), “Intelligence artificielle: enjeux pour le secteur financier”, Document de réflexion, décembre. Agrawal, A., Gans J., & Goldfarb A. (2016), “The Simple Economics of Machine Intelligence”, Harvard Business Review. Athey, S., & Imbens, G.W. (2019), “Machine Learning Methods that Economists Should Know About”, Annual Review of Economics, vol. 11, pp. 685–725. Athey, S. (2019), “The Impact of Machine Learning on Economics”, in The Economics of Artificial Intelligence: an Agenda, Agrawal A., Gans J. & Goldfarb A, pp. 507–547. Baldwin, R. (2019), “The Globotics Upheaval: Globalisation, Robotics and the Future of Work», Weidenfeld & Nicolson. Bartlet, R., Morse, A., Stanton, R., & Wallace N. (2019), “Consumer-Lending Discrimination in the Fintech Era”, NBER, Working Paper, n° 25943. Bazarbash, M. (2019), “FinTech in Financial Inclusion Machine Learning Applications in Assessing Credit Risk”, IMF Working Paper, n° 19-109. Benkhayat, A., Manouar, A., & Sadok, H. (2015), “Firm business strategy and IT strategy alignment: A proposal of a new model”, 2015 Xth International Scientific and Technical Conference “Computer Sciences and Information Technologies” (CSIT), Lviv, Ukraine, pp. 172–178, doi: 10.1109/STC-CSIT.2015.7325460 Berg, T., Burg, V., Gombović, A. & Puri, M. (2019), “On the Rise of Fintechs: Credit Scoring Using Digital Footprints”, Michael J. Brennan Irish Finance, Working Paper Series Research Paper, n° 18-12. Boston Consulting Group (2018), “The Impact of Artificial Intelligence (AI) on the Financial Job Market” https://web-assets.bcg.com/img-src/BCG-CDRF-The-Impact-of-AI-onthe-Financial-Job-Market_Mar%202018_ENG_tcm9-187843.pdf Bostrom N. (2017), “Superintelligence”, Dunod. Brockbank W., Ulrich D., Kryscynski D.G., & Ulrich M. (2018), “The future of HR and information capability”, Strategic HR Review, vol. 17(1), pp. 3–10. Brynjolfsson A. & Mcaffee E. (2014), “Machine, Platform, Crowd: Harnassing our Digital Future”, MIT.

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Charpentier A., Flachaire E., & Ly A. (2018), “Econometrics and Machine Learning”, Economics and Statistics, vol. 505-506, pp. 147–169. Durai D.S., Rudhramoorthy K., & Sarkar S. (2019), “HR metrics and workforce analytics: it is a journey, not a destination”, Human Resource Management International Digest, vol. 27(1), pp. 4–6. El Alami A.H., Sadok H., & Elhaoud N. (2015), “Cloud computing & the organizational performance different approach of assessment”, 2015 International Conference on Cloud Technologies and Applications (CloudTech), Marrakech, Morocco, pp. 1–5, doi: 10.1109/CloudTech.2015.7337007 El Maknouzi M.H. & Sadok H. (2021), “Regulation of virtual currencies in the United Arab Emirates: accounting for the emerging public/private distinction”, Development Studies Research, vol. 8(1), pp. 346–355, DOI: 10.1080/21665095.2021.1980413 Eisenhardt K.M. (1989), “Building Theories from Case Study Research”, The Academy of Management Review, vol. 14(4), pp. 532–550. Frost J., Gambacorta L., Huang Y., Shin H.S., & Zbinden P. (2019), “BigTech and the Changing Structure of Financial Intermediation”, BIS, Working Paper, n° 779. Furman J. & Seamans R. (2018), “AI and the Eeconomy”, SSRN. Fuster A., Goldsmith-Pinkham P., Ramadorai T., & Walther A. (2018b), “Predictably Unequal? The Effects of Machine Learning on Credit Markets?”, SSRN, Working Paper. Fuster A., Plosser M., Schnabl P., & Vickery J. (2018a), “The Role of Technology in Mortgage Lending”, NBER, Working Paper, n° 24500. Galunic, C., & Eisenhardt, K.M. (2000), “Architectural innovation and modular corporate forms”, INSEAD Working Paper, January 2000, available at http://faculty.insead.fr/ galunic/papers/rrcorpweb2.pdf Gantz, G., & Michaels, G. (2015), “Robots at Work”, London School of Economics, CEP Discussion Paper, n° 1135. Grennepois, N., Alvirescu, M.A., & Bombail M. (2018), “Using Random Forest for Credit Risk Models, Deloitte Risk Advisory, September. Houdé, O. (2019), “L’intelligence humaine n’est pas un algorithme”, Odile Jacob. Jagtiani, J. & Lemieux, C. (2019), “The Roles of Alternative Data and Machine Learning in Fintech Lending: Evidence from the LendingClub Consumer Platform”, FRB of Philadelphia, Working Paper, n° 18–15. Le cun Y. (1987), “Modèles connexionnistes de l’apprentissage”, Thèse de Doctorat; Université Paris 6 Pierre et Marie Curie. Leloup L. (2017), “Blockchain, la révolution de la confiance”, Eyrolles. Mahboub, H. & Sadok, H. (2023), “Implementing enterprise digital transformation: a contribution to conceptual framework design”, Nankai Business Review International, vol. 14(1), pp. 35–50. 10.1108/NBRI-06-2022-0067 Mahboub H. & Sadok H. (2023) “Towards a better digital transformation: learning from the experience of a digital transformation project”, International Conference on Digital Economy, Springer International Publishing. pp. 203–2014. Mckinsey (2018), “L’IA va transformer 90% des métiers”, rapport Viva Technology. Mullainathan S. & Spiess J. (2017), “Machine Learning: an Applied Econometric Approach”, Journal of Economic Perspectives, vol. 31(2), pp. 87–106. OECD (2019), “Transformations technologiques et emplois de l’avenir”, rapport au G7. Óskarsdottir M., Bravo C., Sarraute C., Vanthienen J., & Baesens B. (2019), “The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics” https://iris.rais.is/is/publications/the-value-of-big-data-forcredit-scoring-enhancing-financial-incl Pearl J. & Mackenzie D. (2018), “The Book of Why, The New Science of Cause and Effect”, Basic Books.

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Prince A. & Schwarcz D.B. (2019), “Proxy Discrimination in the Age of Artificial Intelligence and Big Data”, Iowa Law Review, available on SSRN: https://ssrn.com/ abstract=3347959 Ridder H.-G., Hoon C., & McCandless A. (2009), “The theoretical contribution of case study research to the field of strategy and management”, in Bergh, D.D. and Ketchen, D.J. (Eds.), Research methodology in strategy and management, Volume 5, Emerald Group Publishing Limited, pp. 137–175. Rifkin J. (2013), “La 3e révolution industrielle du coût marginal”, LLL. Sakka F., El Maknouzi M.E., & Sadok H. (2022) “Human resource management in the era of artificial intelligence: future HR work practices, anticipated skill set, financial and legal implications”, Academy of Strategic Management Journal, vol. 21(S1), pp. 1–14. Sadok H. (2020) “How can inclusive growth be enabled from financial technology?”, International Journal of Business Performance Management, 2021 vol. 22(2/3), pp. 159–179. Sadok H. & El Maknouzi M.E.H. (2021), “The regulation of virtual currencies in comparative perspective: new private money or niche technological innovation?”, Journal of Money Laundering Control, vol. 24(4), pp. 712–724. 10.1108/JMLC-09-2020-0101 Sadok H., Sakka F., El Maknouzi M., & El H. (2022), “Artificial intelligence and bank credit analysis: A review”, Cogent Economics & Finance, vol. 10, pp. 1. DOI: 10. 1080/23322039.2021.2023262 Sadok H. (2023) “Fight Against Corruption Through Technology: The Case of Morocco”, Concepts, Cases, and Regulations in Financial Fraud and Corruption, IGI Global, pp. 302–316. Schweitzer M.E. & Barkley B. (2017), “Is Fintech Good for Small Business Borrowers? Impacts on Firm Growth and Customer Satisfaction”, FRB of Cleveland, Working Paper, n° 17-01. Stake R. (1995), The art of case research. Sage Publications Tang H. (2019), “The Value of Privacy: Evidence from Online Borrowers”, HEC Paris, Working Paper. Wei Y., Yildirim P., Van Den Bulte C., & Dellarocas C. (2016), “Credit Scoring with Social Network Data”, Marketing Science, vol. 35(2), pp. 234–258. Yin, R. (2017). Case Study Research: Design and Methods (6th ed.). Sage Publications.

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Is the Capital Market Based on Blockchain Technology Efficient for Financial Inclusion? Mohamed Lachaari and Mustapha Benmahane LARGESS Chouaib Doukkali University, El Jadida, Morocco El Jadida, Morocco

3.1 INTRODUCTION In finance research, one of the most controversial questions is still waiting for response. The question is how to confirm if capital market is efficient or not? To answer this question we should define the concept of capital market efficiency. Then, classical literature about the theory of finance, which is drift from the neoclassical theory of general market equilibrium and the theory of economic rationality, claimed that capital markets are efficient and highly competitive, where the information is perfectly relevant and transaction’s price is freely determined by “fair game” and market self-regulated mechanism. Recently, one of the innovative technologies called distributed ledger technology (DLT) occurred, which was applied in many fields such as economy, industry, and finance. The potential of Blockchain financial system based on DLT is important, but the question is how actually we can adapt it to real capital market specificities. Most relevant aspect of Blockchain is the huge impact it has on current capital market process using Peer-to-Peer network (P2P). P2P is based on mutual consensus and provides to investors and users of financial services, through technological support, transparency, immutability, and disintermediation. Those DLT principles are similar to market efficiency conditions. Moreover, the efficient Blockchain system, with high support of fintech startups, improves the accessibility of unbanked and excluded people to financial services and accelerates financial inclusion. Indeed, and across the literature review and conceptual analysis, those efficient conditions enhance the efficiency of digital capital market, especially market based on Blockchain technology, and they are similar to the principles related to financial inclusion. Which is the purpose of this chapter. In fact, the analysis would be around many parts: First, we present the conceptual and theoretical framework by summarizing the literature review about the impact of fintech and blockchain technology on capital market efficiency and their impact on financial inclusion, defining theoretical 26

DOI: 10.1201/9781003125204-3

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models, concepts, and explaining the conditions of efficiency in this context with conclusions, and thus, we define the methodological approach to perform our qualitative analysis. After that, we start the conceptual analysis of selected contents towards three main concepts: blockchain technology, market efficiency, and financial inclusion. Then, we discuss the question if the efficient market based on blockchain technology leads to financial inclusion. As a result, we set a proposition of new conceptual model, whose components show the relationship between digital market based on blockchain technology and financial inclusion, explaining that efficient markets are believed to include a maximum number of actors with ease of transactions and free access to financial services leading to financial inclusion. Our conceptual model outlines the possibilities of achieving an efficient market and financial inclusion theories by applying DLT-based tools like Blockchain.

3.2 CONCEPTUAL AND THEORETICAL FRAMEWORK 3.2.1 THE INFLUENCE OF FINTECH AND BLOCKCHAIN TECHNOLOGY CAPITAL MARKET EFFICIENCY

ON THE

Classical literature about the theory of finance is derived from microeconomic theory according to the neoclassical approach, which claimed that economic agent choice (utility maximization for consumer and profit maximization for investor) leads the market prices freely to the general price equilibrium and the optimal choice. Those are mainly the neoclassical microeconomic hypotheses: • Markets are efficient so that market is competitive and market mechanisms are self-regulated; • Economic phenomena can and should be studied using pure science methods; • Agents are rational, their preferences can be identified and quantified; • Agents or consumers maximize their utility, while companies maximize their profit; • Agents act conscientiously and they have free access to complete and relevant information. Classical Finance scholars have continuously tried to provide theoretical models able to be applied into empirical evidences. Stephen Ross discussed the no arbitrage principle1 related to asset pricing in free financial market and jointly with John Cox he proposed the concept of risk-neutral pricing.2 Ross provides a new lecture of those concepts, which are the fundamentals of modern finance and, commonly, of market efficiency. In an efficient market, prices reflect the information that existed in the market and, consequently, use commonly available information to beat the market to not be failed. Moreover, according to Stephen. A. Ross (2002), the basis of neoclassical finance as a part of the classical theory of finance is drift from two principal pillars the

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efficient market and the theory of asset pricing, which are closer to each other.3 In this work, we focus on the efficient market theory or the efficient market hypothesis (EMH), which is a set of hypotheses that express the conclusion that the information of investors or any participant in the capital market is included in and reflected by asset prices.4 The efficient market is defined as the free market characterized by a full accessibility by every investor to the given market data at the same level of information about the pricing of tradable securities, which is almost impossible with human-based supervision. Moreover, there are many recent works confirming EMH5 (Lai et al., 2001; Muhammad and Abdullah, 2009; Alajbeg et al., 2012; Al-Jafari, 2012; Jain and Jain, 2013; Jiang and Li, 2020; Rossi and Gunardi, 2018). Those studies have particularly used the weak form tests in different temporal and geographical contexts. Indeed, Low and Lai (2001) and Albaity and Rahman (2012) argued that Malaysian institutional investors are rational in their investment decision. Studies confirming market efficiency show high level of risk aversion make decisions based on their skills and fundamental analysis and use perfectly the available information for their decisions.6 In contrast, the currently famous conception of market offered by behavioral finance is justified by observable anomalies in the real context of financial markets, regarding market prices as related to the psychological state of investors. However, Ross7 shows that neoclassical theory provides a crucial and simple explanation that resolves many of those anomalies. Indeed, Ross, Cox, and other recent authors provided major contributions to the ongoing debate on market efficiency. However, many empirical studies, especially in developing economies, provide results whose findings are in contrast with earlier studies that drift from EMH theoretical model. Some of the reasons are because of the difficult access to relevant information, the lower level of financial literacy among investors, and then, the organizational context and particularity of such economic environments where empirical studies were conducted.8 Rubinstein (2001) presented investor rationality on the market reaction; he ignored the human factor and stated that all the investors would behave rationally if they had the same information accessibility. The market efficiency is evaluated empirically in different contexts, for example, in the Indian financial market, which reflects a weak and semi-strong form of efficient market hypothesis (Kumar, 2021). Then, the industry and experts are attempting to resolve the issues of announcements, news, and data management through technological tools and automation. DeBondt and Thaler (1989) provided detailed evidence about the linking of company financial profitability and market reaction against each announcement. This linking requires technological support where investors can evaluate it in a live environment. Even the internet and security exchanges try to provide reliable knowledge more quickly. Still, the reporting period to the exchange/broker and the deal confirmation always has a time lag; that gap allows arbitrage for speculative investments. During 2020, the experiments on financial technology represented that the issue is not just the timely information accessibility; it is the integrated views with the complete cycle of news that is important to be implemented into the capital market,

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and especially the revolutional Blockchain technology as one of the important new programming paradigms. The evolution of electronic and decentralized information, and the digital transformation have led operators in the field to target an integrated environment. The use of a triple entry system and cryptography-based business protocol of DLT helps actors and provides easier access to the set of related information in one transaction trail (Ian Grigg, 2005). Blockchain is a type of distributed technology that leads data to be registered and shared by a virtual network. In this configuration, each member retains and saves their copy of data, then all members control and validate any updates immediately. Thus, Blockchain system is permanent, transparent, secured, and independent9 where each updated transaction is stored in a “bloc” and added chronologically to other blocks in a “chain” list. That system called “protocol manager” or DLT “Distributed technology” is responsible for editing, saving, and distributing digital objects. DLT replaces third-party intermediaries as trust agents and allows all members to use complex algorithms to validate and share transactions in real time in order to satisfy any expressed demand as shown in Figure 3.1.10 Digital transactions within a blockchain system are executed between participants according to an electronic protocol equivalent to traditional called “smart contracts” which are automatically executed by users when predetermined conditions are occurred and without any third-party control or intermediation.11 Smart contracts represent a very crucial element in comparison between traditional and blockchain system.12

Financial inclusion

Blockchain

Efficient capital market

Financial technology

Financial services

FIGURE 3.1 New Conceptual Model: Figure 3.1. Financial Inclusion and Blockchain Technology through Digital Financial Market. The Achievement of Financial Inclusion, through Financial Services Based on Blockchain System and Provided by Financial Technology Platforms (Fintech), Will Improve the Digital Capital Market Efficiency. Created by Authors. 24

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Digital financial market platforms using Blockchain technology provide to investors an opportunity of investment including tokens or e-certificates. However, as we have already cited, DLT provides also a disintermediating centralized platforms, its attractiveness for P2P lending and borrowing gave it this famous character of trust and traceability. Indeed, startups such as Waves.co and Nexo.io have offered their users to run leverage with fiat currencies, Ethereum, Bitcoin, or any startup native tokens, so give investors the opportunity to choose their way of investing strategy. Since the introduction of Bitcoin in 2013, cryptocurrencies capital markets have met a great evolution. Last updated statistics according to the platform coinmarketcap.com shows us the high volume of cryptocurrencies market transactions. When we talk about Blockchain digital market, we mean digital market based on DLT, which can be defined as a way of raising funds through Distributed Technology (DLT) by issuing digital objects in exchange for cryptocurrencies. These digital objects are intangible assets issued against a pre-existing cryptocurrency at a price set by a company to finance its activity, and then they are potentially exchangeable on a secondary market. As a result, the impressive application for the Blockchain-based capital market could be explained by a speculative observed growth of cryptocurrency virtual market exchange and particularly Bitcoin and Ethereum in 2019. There would be a positive relationship between the evolution of financial technology and the value of traded cryptocurrencies.13 As a conclusion of what had been exposed, we observed four elements, which characterize a Blockchain system, which are transparency, immutability, and disintermediation. Then, those conditions are so close to the efficient market hypothesis, and consequently, that would be a potential impact of Blockchain technology on the capital market efficiency.14 This can also lead to financial inclusion through financial services provided by Finthecs as shown in figure 3.1.

3.2.2 THE IMPACT OF FINTECH FINANCIAL INCLUSION

AND

BLOCKCHAIN TECHNOLOGY

ON THE

Financial inclusion can be defined by Nooh Alshyab et al.: According to the World Bank: “Financial inclusion means that individuals and businesses have access to useful and affordable financial products and services that meet their needs – transactions, payments, savings, credit and insurance – delivered in a responsible and sustainable way.”15 Financial inclusion overcomes the problem of accessibility of low-level-income people, and who suffer from social and economic difficulties into the banking and financial system by taking into consideration the use of financial services.16 Thereby, financial inclusion has been clearly considered as an important element in poverty reduction and growth strategies; consequently, the problem of financial exclusion can be eliminated.17 The digital transformation in the banking and financial sector came from a contraction of two words: finance and technology. In other word, the concept of Fintech. According to Fortnum et al. (2017), the term fintech is related to companies

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of new technology, which operate outside traditional business models for financial services, seeking to change the way these services are offered. According to Christensen’s (2003) fintech businesses are categorized into two types of companies: “Sustainable Fintechs,” for existed financial companies in traditional market that work to protect their market positions by using new technologies and adapting to the digital transformation and “Disruptive Fintechs” that are new companies and startups that operate only in the digital market by offering new products and services based exclusively in new technologies. Those financial startups undertake a new business model, which provides customers and stakeholders in the financial and banking sectors with more flexibility, security, efficiency, and opportunities than traditional financial services (Gomber et al., 2017). Following the digital transformation, banks and traditional financial providers will be used only for deposits, while other financial services will be done using fintech companies (Gomber et al., 2017). Eduardo Z. Milian et al. (2019) affirm according to their content analysis whose 115 publications related to Financial Services were identified, indeed, their main objective is the discussion of research subjects concerning the center of the finance, such as the operation and regulation of financial services, financial inclusion and innovations for products and services as well as business models. The works on services operation were grouped with financial risk management and operational risk management, in addition to other research problems related to operational aspects of company business in the financial industry. Similarly, 62 articles were identified whose focus on technologies that can be applied in the field of finance. In the same study, around 8% of publications are about financial inclusion and discuss the inclusion of the populations with no access to the most basic financial services. Then, the work shows the role of financial technology in accelerating financial inclusion. Moreover, more than 82% of DLT markets take place on Ethereum (Watchlist, 2019) because of the advanced form of Smart Contracts used in this blockchain cryptocurrency platform which is developed in the easiest way to standardize DLT process (Wuyts, 2018). Therefore, the majority of assets traded into DLT market are cryptocurencies and most of transactions are executed in cryptocurrency Ether. Furthermore, the amount of funds was with over $20 billion raised by December 2019. Otherwise, this emerging way of financing based on innovation and digitalization improves the financial inclusion,18 because it has the opportunity to include all categories of society, also the unbanked people and people who cannot definitively have access to financial services provided by banks and traditional financial institutions, into an alternative financial system based on P2P. So that explains how Blockchain technology leads to financial inclusion, in observing markets based on DLT which are situated between traditional capital market and crowdfunding.19 Crowdfunding is one of the financial models encouraging financial inclusion. Furthermore, according to experts,20 blockchain technology encourages financial inclusion through many points, such as: • Opening an account in a traditional financial institution is costly and challenging for unbanked people, they need to provide identification documents and an initial deposit, but with blockchain technology, they

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would be able to open an account and deposit money on their phone, and thus avoiding the travel and the costs. • Usability of an account is more flexible with platforms based on Blockchain technology, because low costs, no minimum required and fast transfers make blockchain interesting for unbanked people to achieve their transactions. Moreover, DLT is useful for decentralized financial services (called DeFi) as a way to financial inclusion. Goldman Sachs experts argue that: “Decentralized finance refers to financial services and products built on public Blockchain or on DLT networks that do not require intermediaries.” According to Ozili (2018), financial inclusion is improved by financial innovations such as P2P loans or new credit scoring methods and new forms of business. Thereby, financial platforms using blockchain technology are not obliged to adopt a specific payment system. For example, Mobile money operators act on a private network, which required the same payment network to transfer funds between mobile money operators. Instead, blockchain decentralized networks lead everyone to transfer funds across different payment systems. In conclusion, and according to what had been exposed, digital market based on Blockchain technology represents a new emerging form of financial models with enormous potential (Pust, 2018). Then, it would be an important way to improve the financial inclusion.

3.3 METHODOLOGICAL APPROACH: TOWARD A NEW CONCEPTUAL MODEL This work is based on the model proposed by us in our article entitled: “Capital market based on Blockchain technology and efficient market hypothesis: theoretical and conceptual analysis” published in the international journal of business performance management.21 Also, the present work focuses on a literature review, scientific documents, and professional financial markets reports analyses based on financial technology, financial inclusion, and Blockchain technology following a qualitative approach using conceptual analysis so as to explore the influence of an efficient Blockchain market on financial inclusion. Also, according to the literature review, especially the works of Milian et al. (2019) and the works of Ozili (2020), pioneers in the study of the relationship between financial technology, financial inclusion, and Blockchain technology. Based on the documental and conceptual analysis the research purpose is to explore how an efficient capital market based on Blockchain technology can influence positively the acceleration of financial inclusion. Otherwise, the subjective nature of knowledge about Blockchain capital market, and the need to build a new theoretical model based on blockchain technology efficiency toward a financial inclusion, led us to adopt an inductive and constructive approach based on practices and researches conclusions because of lack of theories about Blockchain-based capital market and financial inclusion. Moreover, advancing a new understanding of relationship between financial technologies, financial

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inclusion, and efficient Blockchain market leads to building a new theoretical model based on practices and observation.

3.4 CONCEPTUAL ANALYSIS: BLOCKCHAIN TECHNOLOGY, MARKET EFFICIENCY, AND FINANCIAL INCLUSION According to the literature review conclusions discussed in previous sections, and based on our previous work about efficient Blockchain market22 focused on a documentary and qualitative analysis of 45 documents from online databases (Google Scholar, Proqeust, Science direct, SSRN, …) including recent literatures, dissertations, scientific documents, entrepreneur’s whitepapers, and professional reports. Also, based on Milian et al. in their article “Fintech: A literature review and research agenda,” published on the Electronic Commerce Research and Applications in 2019, by “Science Direct,” through a systematic literature review of 179 most relevant works in the domain of fintech from the indexed databases “web of knowledge” and “Scopus” for the period from 1980 until the end of February 2018. The literature about financial services and Blockchain technology are the most cited. Our study is also based on Ozili’s recent works based on his previous article published by “Science Direct” in 2018, which highlights the impact of digital finance on financial inclusion. Among the analyzed documents and reports, four articles written by me and coAuthor, Milian et al., and Ozili, which are the most relevant and focused, are reported, with details in Table 3.1. According to a recent systematic study,23 the majority of publications between 2016 and 2018 focused on different subjects grouped in Financial Services (115 works) and Financial Technology (115 works), the most relevant keywords based on qualitative analysis are as follows: Financial technology, Financial inclusion, Blockchain technology, Electronic money, and Cryptocurrency. Moreover, based on the analyzed studies (Table 3.1), the keywords blockchain, banking/financial inclusion, and financial technology are most frequent. In this qualitative analysis, the words: bitcoin, cryptocurrency, and electronic money appear grouped around the word blockchain, showing the frequency and the proximity of these terms. TABLE 3.1 Sample Articles Bibliometrics Articles

Σ Citations

Eduardo Z. Milian

1

141

Ozili, Peterson. K

3

525

Lachaari, M. and Benmahane, M.

1

1

Authors

Source: By Authors (Data from Google scholar index).

Focus Financial technology and financial services Financial technology and financial inclusion Blockchain technology and market efficiency

Period 2019 2018–2020 2021

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Artificial Intelligence, Fintech, and Financial Inclusion

TABLE 3.2 Sample Articles Summarized Analysis Authors

Data

Methods

Period of Analysis

Eduardo Z. Milian (2019)

179 documents

Systematic review

Ozili, Peterson. K (2020)

Most literature related to financial inclusion 45 documents

Literature review 2002–2020

Lachaari, M. and Benmahane, M. (2021)

1980–2018

Content Analysis 2015–2020

Conclusion Blockchain technology and financial inclusion are the most relevant keywords which are related to financial services with fintech Financial innovation and technology promote Financial inclusion Blockchain technology improves the capital market efficiency

Source: By Authors.

Indeed, Table 3.2 shows us the summary and conclusions provided by the three most relevant works. The framework of the literature shows that financial inclusion is more related to Fintech Services, while Blockchain/Cryptocurrencies is more linked with Financial Technology. Furthermore, because Fintech and Financial technology are two sides of the same coin, the previous qualitative analyses confirm that Financial technology, Blockchain technology, and Financial inclusion are the most related and closer concepts in recent researches about new financial models.

3.5 TOWARD A NEW CONCEPTUAL MODEL: DISCUSSION AND CONCLUSION Based on previous sections concerning, respectively, the literature review and conceptual analysis, in this part we focus on drawing new conceptual model that reveals the impact of efficient markets on financial inclusion in general. Table 3.3 shows some common characteristics of financial inclusion environment and efficient digital market based on Blockchain technology, which leads to market efficiency. Indeed, according to figure 3.1, table 3.2, and table 3.3, those relevant similarities lead us to draw a conceptual model of an efficient digital capital market with four components: Blockchain, Fintech, Financial Services, and Financial Inclusion. According to our conceptual model, a digital financial market based on the blockchain system, as an important part of financial technology, provides ease of transactions and accessibility to financial services, which will improve the financial inclusion and market efficiency.

Is DLT-Based Capital Market Efficient for Financial Inclusion?

35

TABLE 3.3 Efficient Blockchain Market and Financial Inclusion Ecosystem Similarities Conditions Transparency

Disintermediation

Immutability

Explanation

Financial Inclusion

Efficient Blockchain Market

Include all categories of population, even unbanked, poors, and excluded ones. Financial services are accessible without any intermediation (Decentralized and independent)

YES

YES

YES

YES

include a maximum number of actors, with anonymity and/or atomicity

YES

YES

Source: By Authors.

Thereby, Fintech as a global platform of financial technologies providing financial services represents the most important manifestation of financial inclusion, because they give the population from all social classes the possibility to act indirectly into the financial system and lead to the efficiency in free capital market. Thus, the efficiency of a capital market based on blockchain technology could also improve the financial inclusion. Moreover, the anonymity, transparency, immutability, and disintermediation that characterized the DLT protocol enhanced the efficiency of digital capital market.25 Furthermore, one of the famous business model based on financial technology, called crowdfunding, is a meaningful tool to reach financial inclusion. Therefore, low-size and medium startups will get the opportunity to finance their projects through crowdfunding. In addition, blockchain technology would allow projects based on crowdfundig to be accessible to unbanked and excluded people with less requirements and low costs. Despite The advantages provided by a digital capital market based on DLT, cryptocurrency exchanges are forbidden, especially in developing countries. Otherwise, despite the announcement of the Moroccan National Exchange Office on 20 November 2017 that transactions in cryptocurrencies are prohibited in Morocco, crowdfunding Law (15–18 law) adopted by Moroccan authorities on 22 August 2019 has defined the legal framework related to collaborative funding to finance the youth entrepreneurship with innovative projects. Finally, we can affirm that the adoption of new technologies and the application of those technologies within the capital market and the financial system may resolve many controversies and lead to market efficiency, and consequently to financial inclusion. However, financial literacy and communication towards the legal and technological patterns are the best way to protect investors, especially small startups, fragile and low-income people.

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Artificial Intelligence, Fintech, and Financial Inclusion

NOTES 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23 24 25

Ross, A. S. (2002). Ross, A. S. (2002a). Ross, A. S. (2002b). Fama E. (1970, 1990). Lachaari, M. and Benmahane, M. (2021). Lachaari, M. and Benmahane, M. (2021a). Ross, A. S. (2002c). Subramanian and Al, (2017). Grech et al., (2017). (Grech et al., 2017a; Karen L. Jones, 2019). Alexis Collomb and Klara Sok.(2016). Grech et al., (2017b). ( De Quénetain, 2019). Lachaari. M and Benmahane, M. (2021b). N. Alshyab et al. (2021). N. Alshyab et al. (2021a). Kumar et al. (2021). Ozili Peterson K. (2020). New form of financial networking is based on financial technology and includes all categories of society, even those who are definitively unbanked. Blockchain and financial inclusion: the role Blockchain technology can play in accelerating financial inclusion, white paper, March 2017. To cite our article: Lachaari, M. and Benmahane, M. (2021) “Capital market based on Blockchain technology and the efficient market hypothesis: Theoretical and conceptual analysis,” IJBPM, 22(2/3), 199–218. Published on IJBPM (Inderscience editor) in 2021 (V (22) issue (2/3)). Eduardo Z. Milian et al. (2019). Inspired from works of Ozili, Peterson. K (2018) and Lachaari, M. and Benmahane, M. (2021). Lachaari, M. and Benmahane, M. (2021c).

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Baruri, P. (2016). “Blockchain powered financial inclusion”, in Presentation at the International Monetary Fund Global Symposium on Innovative Financial Inclusion: Harnessing Innovation For Inclusive Finance. Cao, S. et al. (2017). “A review of researches on blockchain”. in WHICEB, p. 57. Christensen, C. M. (2003). “The innovator’s dilemma: The revolutionary book that will Changed the way you do business”. The American Political Science Review. Collomb, A. & Sok, K. (2016). “Blockchain/distributed ledger technology (DLT): What impact on the financial sector?”. DWEJ, 103, 93. Danatzis, S. (2019). “Initial Coin Offering, ICO, Cryptomonnaies, Bitcoin, Blockchain, Finance, vente de token, décote de placement, Asymétrie d’information” (Thèse). Louvain School of Management. De Bondt, W. F., & Thaler, R. H. (1989). “Anomalies: A mean-reverting walk down Wall Street”. Journal of Economic Perspectives, 3(1), 189–202. Fama, E. F. (1970). “Efficient capital markets: A review of theory and empirical work”. The Journal of Finance, 25(2), 383. Fama, E. F (1990). “Stock Returns, Expected Returns, and Real Activity” .Journal of Finance, 45(4), 1089–1108. Fama, E. F. (2003). “Profiles of world economists”. BIATEC, XI, 21–23. Fortnum, D., Pollari, I., Mead, W., Hughes, B., & Speier, A. (2017). “The pulse of fintech Q1 2017: Global analysis of investment in fintech”. KPMG Technical Report, 2017. New York. Available at: https://assets.kpmg.com/content/dam/kpmg/xx/pdf/2017/04/pulseof-fintech-q1.pdf Goldman Sachs (June 2021). “Digital Assets: Beauty Is Not in the Eye of the Beholder”. Insights: Investment Strategy Group report. Gomber, P., Koch, J.-A., & Siering, M. (2017). “Digital Finance and FinTech: Current research and future research directions”. Journal of Business Economics, 87, 537–580. Guo, Y. & Liang, C. (2016). “Blockchain application and outlook in the banking industry”. Financial Innovation, 2(1), 24. Giudici, Milne, A., & Vinogradov, D. (2019). “Cryptocurrencies: market analysis and perspectives”. J. Ind. Bus. Grech, A. & Camilleri, A. (2017). “Blockchain in education”. Luxembourg: Publications Office of EU commission. (JRC, Science for Policy Report),1-137. Grigg, I. (2005). Triple entry accounting. Systemics Inc, pp. 1–10. Hodnett and Hsieh (2012). “Capital market theories: Market efficiency versus investor prospects”. International Business & Economics Research Journal, 11(8), 849–862. ICO Watchlist (2019). https://icowatchlist.com/ (accessed 1 December 2019). Jain, D. K. and Jain, P. (2013) “Empirical study of the weak form of EMH on Indian stock market”. Journal of Management and Social Sciences Research (IJMSSR), 2(11), 52–59. Jan Smets, Governor National Bank of Belgium. (2016). “Fintech and the Future of Retail Banking”. Presented at the colloquium of the Belgian Financial Forum in cooperation with SUERF, The European Money and Finance Forum and Eggsplore, Brussels. Jones, K. L. (2018). “Blockchain: Building Consensus and Trust across the Space Sector”. p. 12. Jiang, Jinjin, & Li, Haiqi (2020). “A new measure for market efficiency and its application”. Finance Research Letters, 34, 1–7. Karen Jones, L. “Blockchain: Building Consensus and Trust across the Space Sector”. 35th Space Symposium, Technical Track, Colorado Springs. Colorado - USA. April, 8, 2019. The Center for Space Policy and Strategy. Kumar, A., Nguyen, T. H., & Putniņš, T. J. (2021). “Only gamble in town: Stock market gambling around the world and market efficiency”. Available at SSRN 3686393. Kurihara, Y. & Fukushima, A. (2017). “The market efficiency of Bitcoin: A weekly anomaly perspective”. Journal of Applied Finance and Banking, 7(3), 57.

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Lachaari, M. & Benmahane, M. (2021). “Capital market based on Blockchain technology and the efficient market hypothesis: Theoretical and conceptual analysis”. IJBPM, 22(2/3), 199–218. Lai, M. M., Low, K. L. T., & Lai, M. L (2001). “Are Malaysian investors rational?”. Journal of Psychology and Financial Markets, 2, 210–215. Lewis, R., McPartland, J., & Ranjan, R. (2017). “Blockchain and financial market innovation”. Economic Perspectives, 41(7), 1–17. Mcmillan, J. (2014). “The End of Banking – Money, Credit and the Digital Revolution”. Editions Zero/One Economics GmbH. Milian, E. Z., Spinola, M., de M., & Carvalho, M. M. de. (2019). “Fintechs: A literature Review and research agenda”. Electronic Commerce Research and Applications, 34, 1–21. Mori, T. (2016). “Financial technology: Blockchain and securities settlement”. Journal of Securities Operations & Custody, 8(3), 208–227. Muhammad, N. M. N., & Abdullah, M. (2009). “Investment decision-making style: Are Malaysian investors rational decision-makers”. Interdisciplinary Journal of Contemporary Research in Business, 1, 96–108. Nguyen, Q. K. (2016). “Blockchain-a financial technology for future sustainable development”. In 2016 Third International Conference on Green Technology and Sustainable Development (GTSD). IEEE, pp. 51–54. Ozili, P. K. (2018). “Impact of digital finance on financial inclusion and stability”. Borsa Istanbul Review, 18(4), 329–340. Ozili, P. K. (2020, January). “Financial inclusion research around the world: A review”. In Forum for Social Economics. Routledge, pp. 1–23. Pilkington, M. (2016). “Chapter 11: Blockchain technology: Principles and applications”. Research Handbook on Digital Transformations, 225–253. Pollari, I. (2016). “The rise of Fintech opportunities and challenges”. The Journal of Applied Science in Southern Africa, 3, 15.10. Pust, L., n.d. (2018). “The future of money: How Bitcoin and its underlying Blockchain technology could affect the financial sector”. Pust, L., n.d. (2018). PhD Research Proposal | Thesis zum Promotionsvorhaben. Quénetain, S. (2019). “Qu’est-ce qu’une ICO? [Article en ligne]. Récupéré le 31 Décembre 2019”. https://www.blockchains-expert.com/quest-ce-quune-ico/#Lever_des_fonds_ et_faire_connaitre_un_projet Ross, S. A. (2002). “Neoclassical finance, alternative finance and the closed end fund puzzle”. European Financial Management, 8(2), 129–137. Rossi, M., & Gunardi, A. (2018). “Efficient market hypothesis and stock market anomalies: empirical evidence in four European countries”. Journal of Applied Business Research, 34(1),183–192. Rubinstein, M. (2001). “Rational markets: Yes or no? The affirmative case”. Financial Analysts Journal, 57(3), 15–29. Subramanian and Velnampy, T. (2017). “Rationality: A central point between traditional finance and behavioral finance”. International Journal of Research – GRANTHAALAYAH, 5, 389. 10.5281/zenodo.821706. Tinn, K., n.d. (2018). “Blockchain and the Future of Optimal Financing Contracts”. (Thesis) Imperial College Business School. Wales, K. (2015). “Internet finance: Digital currencies and alternative finance liberating the capital markets”. Journal of Governance and Regulation, 4(4), 10–22495. Wuyts, G. (2018). “The New Kid on the Block: ICO” [Document électronique PDF]. Récupéré le 31 Décembre 2019. https://www.financialforum.be/doc/doc/review/2019/ bfw-digitaal-editie2-2019-03-artikel-guntherwuyts.pdf Yoo, S. (2017). “Blockchain based financial case analysis and its implications”. Asia Pacific Journal of Innovation and Entrepreneurship, 11(3), 312–332.

4

Exploring the Regulatory Contexts of Fintech Innovation for Financial Inclusion The Case of Distributed Ledger Technologies in India Saon Ray ICRIER, India

Sandeep Paul University of Texas at Austin, Texas, USA

Smita Miglani Institute of Economic Growth, India

4.1 INTRODUCTION Technology-enabled innovations are not new to the financial sector (Schindler 2017). The sector has witnessed previous waves of innovation. The Automated Teller Machines (ATMs) adoption of the 1960s or the more recent adoption of information technology are examples of interlinkages between finance and technology (Arner et al., 2015; Schindler, 2017). Emerging fintech innovations, however, differ much from the earlier technological advancements. The two most noteworthy differences are the nature of technological integration and the entry of non-finance technical firms into the sector. Unlike the earlier advancements of technology in the financial sector, there is a large cluster of innovations (Schindler, 2017) occurring simultaneously with an adoption rate much faster than previous waves of innovation in the sector (BIS, 2017). This has implications for regulation as we see below. While the impact of digital technologies such as Distributed Ledger Technologies (DLT),1 machine learning, artificial intelligence, cognitive computing, etc., on DOI: 10.1201/9781003125204-4

39

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Artificial Intelligence, Fintech, and Financial Inclusion

financial inclusion is largely studied (Schuetz & Venkatesh, 2020) (Lichtfous et.al, 2018) (Baruri, 2016) (di Prisco, D., & Strangio, 2021) to the best of our knowledge, much less, if any, research is accomplished on the regulatory environments which enable those innovations to appear in the benefit of financial service provides or financially excluded customers in the Indian context. The objective of this paper is to explore the emerging forms of fintech regulation (with reference to DLT), concerns, and challenges it brings forth to demonstrate the kind of regulatory environment that is required to spur innovation in the fintech space without succumbing to the risks it poses. The regulatory approach to ushering in new technology like blockchain has to keep the two objectives of balancing innovation and ensuring compliance. While there is a danger of excessive regulation stymieing the potential, at the same time, the growth of new products cannot be left to the market forces because of the potential implications on financial stability (FSB, 2017). The DLT is a novel way of sharing data across multiple data stores (or ledgers) (World Bank, 2017). The shared database allows peer to peer transaction without the requirement of a central authority. The methodology used in this chapter is a systematic review of the global experience on fintech regulation and potential challenges. A case study of India is adopted to demonstrate the evolution of fintech regulation and anticipated challenges. The paper is organized in the following manner: the next section presents a review of the literature on the global experience in balancing innovation and risk in the fintech space followed by a discussion on potential challenges to fintech regulation in Section 3. Section 4 explores the evolution of fintech regulation in India and the emerging challenges. Section 5 concludes.

4.2 BALANCING INNOVATION AND RISK – A REVIEW OF LITERATURE ON THE GLOBAL EXPERIENCE Though an industry with an immense potential to transform financial services, the growth of fintech depends a lot on how well these companies are integrated into current banking systems. The word ‘fintech’ is loosely used to refer to a wide array of services and products. The financial stability board (FSB) which defined it as “technologically enabled financial innovation that could result in new business models, applications, processes, or products with an associated material effect on financial markets and institutions and the provision of financial services” (FSB, 2017). The scope of these innovations covers not only the core banking services but also a range of market support services related to sectoral innovations (BIS, 2018) (FSB, 2017). The fintech products not only overlap with the traditional financial sector services but also provide new tools and processes to support them. This was made possible by interrelated drivers of innovation (FSB, 2017; Schindler, 2017). Arner et al. (2015) argue that the financial regulation and market structure in the post-2008-2009 crisis have greatly aided the emergence of fintech companies. For example, the increased capital requirements have altered the lending behavior of commercial banks (FSB, 2017). The emergence of alternative lending platforms, peer-to-peer (P2P) lending, etc. has to be seen within this context.2 The technological solutions offered by fintech firms were very attractive to the banking sector since it helped to bring down operational costs and increase

Exploring the Regulatory Contexts of Fintech Innovation

41

efficiency. This greatly prompted banks to unbundle their services and collaborate with new entrants who also benefited from rapid market access and easy scalability of the products. On the demand side, the diffusion of innovation was greatly aided by shifting consumer preferences and demographics. The rapid spread and access of mobile and internet technology and familiarity with digital modes of finance have already lowered the barriers to entry. This, in many ways, ensured a potential demand for improved services making diffusion a faster process. The efficiency and cost-effectiveness of these innovative products and services were also highly attractive to the tech-savvy millennials who eased the adoption and enabled market penetration significantly (FSB, 2017). At the same time, the innovative technological applications developed by these firms often result in new business models, processes, or products. This puts national governments and regulators in a difficult situation. The fact that the banking and financial sector is often a tightly monitored one with high barriers to entry also necessitates some sort of initial developmental support for new players. Regulators and supervisors across the globe have accepted these challenges and efforts are underway to facilitate innovation and better integration into existing markets. Such efforts can be grouped into three major categories: regulatory sandboxes, accelerators, and innovation hubs. While the level of support and nature of interaction may vary from jurisdiction to jurisdiction, they all seek to provide regulatory guidance to innovative companies (BIS, 2018). In this section, we discuss the regulatory sandbox and innovation hubs briefly. A regulatory sandbox refers to an institutional arrangement that allows fintech companies to live-test new products and services in a controlled environment with the active cooperation of the supervisor (BIS, 2018). This allows the companies to experiment with their products and improve their viability in the existing market and regulatory structure. Hand holding by the supervisor helps entrants address barriers to entry and information asymmetries in the market that would have otherwise limited their growth and development. From the regulators’ perspective, the arrangement allows insights into potential risks and benefits arising out of innovative products and business models and assesses the existing framework of regulation and supervision. In a sandbox setup, there is generally a prior application process and selection by the supervisor (BIS, 2018) before it engages with the firm which may or may not be currently regulated. A sandbox would also have given eligibility criteria for firms wanting to join, a well-defined space and duration for testing of the product, and appropriate boundary conditions to ensure the protection of interests of all parties involved, including consumers and the rest of the industry (RBI, 2018) (BIS, 2018) (Ray, 2019).3 The sandbox approach is currently the most favored mechanism globally to promote and facilitate fintech innovation. The most noteworthy initiatives are by the United Kingdom (UK), Australia, and Singapore. The UK was the first country to set up a regulatory sandbox in May 2016. Here the sandbox provides a safe testing environment was provided to ‘test and learn’ new and innovative products with limited risk. The program was established by Financial Conduct Authority (FCA) under their project innovate program. The UK also has an innovation hub under the same program (GPFI, 2017). This engagement allows the regulator to identify areas

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where the regulatory framework needs to adapt and structural barriers if any should be removed to enable further innovation in the interest of consumers. Though formed with similar objectives, the sandbox models in the UK and Australia differ significantly, especially concerning criteria for joining the sandbox. The Australian model does not require companies to obtain individual approvals like the UK model does: in the UK model, the application process involves testing applicants against a set of criteria including ingenuity of the innovation, benefit to consumers, readiness of the product to be tested, and need of guidance for the testing process (EBA, 2017) (ASIC, n.d.). In the UK, the Financial Conduct Authority (FCA) operates on a cohort basis with six-month test periods per year, where the selected firms are provided with ‘sandbox tools’ to conduct the test within a regulatory framework (FCA, 2017).4 The Australian model, on the other hand, uses a whitelist approach (EBA, 2017), where companies meeting the criteria are allowed to test their concepts without requiring a license. In particular, the Australian model has three components: (a) relying on existing statutory exemptions or flexibility in the law – such as by acting on behalf of an existing licensee; (b) fintech licensing exemption – which applies to certain products or services; and (c) tailored, individual licensing exemptions to a particular business (ASIC, 2017) (ASIC, n.d.).5 The Regulatory Sandbox operated by the Monetary Authority of Singapore (MAS) is another successful initiative.6 Innovation hubs, on the other hand, are more informal arrangements than sandboxes and usually play an advisory or supportive role for the entire industry without differentiating between new and incumbent firms. They can be described as information exchange regimes on fintech matters (BIS, 2018) which may play a larger developmental role. An accelerator, in general, refers to programs funded and run by experienced private players. They can take various forms, but mostly follow a mentorship model where start-ups with innovative ideas are provided platforms to develop and scale their products or services, sometimes with initial seeding. The table below (Table 4.1) that many countries have already set up sandboxes and innovation hubs TABLE 4.1 Regulatory Sandboxes and Innovation Hubs in Select Countries Country

Regulatory Sandbox/ Agency in Charge

Innovation Hub (State-Driven)

Australia

Australian Securities and Investments Commission (ASIC)

Australian Securities and Investments Commission (ASIC)

Brazil

No

The Securities and Exchange Commission of Brazil (CVM)

Canada European Union (EU)

Canadian Securities Administrators (CSA) No

Ontario Securities Commission (OSC) Launch Pad Proposed: EU Fintech Lab

France

No

ACPR-Fintech Innovation Unit

Exploring the Regulatory Contexts of Fintech Innovation

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TABLE 4.1 (Continued) Regulatory Sandboxes and Innovation Hubs in Select Countries Country

Regulatory Sandbox/ Agency in Charge

Innovation Hub (State-Driven)

Germany

No

BaFin

Indonesia Italy

Bank Indonesia (Central Bank) Proposed

No Bank of Italy, Fintech channel

Japan

Proposed, Financial Service Agency

Fintech Demonstration Testing Hub

Mexico Republic of Korea

Proposed Financial Services Commission (FSC)

Yes Fintech Center - Financial Services Commission (FSC)

Singapore

Monetary Authority of Singapore (MAS)

Monetary Authority of Singapore (MAS)

United Kingdom United States of America

Financial Conduct Authority (FCA) Proposed

Financial Conduct Authority (FCA) No

Source: Compiled by authors (various sources).

It is clear from the global experiences that the approach to fintech regulation is largely similar across countries with governments keen to maximize the benefits of emerging technologies regardless of the stage of maturity the domestic industry is in and bring in regulatory clarity. This has led regulators to strive toward an optimal regulation that would promote innovation without risking the financial system or harming consumer interests. The regulatory sandbox appears to be the most promising and popular approach, as it can pave way for reduced information asymmetries and regulatory costs (Cornelli et.al, 2020). The familiarization of innovators with market realities and better capital access are additional gains observed in this respect. Hence, sandboxes could become a crucial policy tool for harvesting the benefits of financial innovation.

4.3 CHALLENGES TO FINTECH REGULATION While fintech growth can bring in many benefits, it also carries a wide variety of risks. A major opportunity and challenge posed by the emerging technologies are that they might displace the current governance framework and business structure of the financial market. Distributed Ledger Technologies (DLTs) like Blockchain is one such fintech sector with immense potential for disruption not only for the market but also for the regulator system. Given the peer-to-peer nature of the technology, it is possible to have trustless exchanges without any intermediaries while maintaining high levels of security, transparency, and efficiency (Ray et.al, 2019). As Ray et al., (2018), illustrate a future possible scenario where blockchain technology is applied to the payments process and compare it with the current payment process in India (via the Immediate Payment Service (IMPS)).7

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However, these kinds of situations pose an immense challenge to the regulator and existing industry practices. For example, algorithmic digital currencies may appear as viable competitors to central bank fiat currency, and their presence in the marketplace may disrupt the traditional financial architecture forcing the central banks to revamp their monetary policies (Raskin and Yermack, 2016). Similarly, increased adoption of these technologies might have implications for corporate governance. Lower cost, greater liquidity, more accurate recordkeeping, and transparency of ownership offered by blockchain may significantly impact the constitution of power and accountability among various parties involved (Yermack, 2015). While the potential of the DLT application is numerous, the fact that it is a nascent technology remains to pose valid concerns regarding issues such as transaction speed, verification process, security control, and integration with the current system (Ray et.al, 2019). Another commonly cited issue regarding DLT is the lack of clearly defined global standards and Application Programming Interface (API) development protocols. This could streamline and promote innovation further as there is no universal guideline on API development or protocols associated with it. Though a very prescriptive approach may have unintended consequences, a minimum agreement to reduce technical complexities and ensure data security and consumer protection is desirable. As the IDRBT (2017) has pointed out, interoperability between different blockchains is a highly desirable feature. Also, these technologies by their very nature have a global reach. While companies operate globally, regulations are country-specific and change drastically with the location. As OICO-IOSCO (2017) points out, this creates conflict in terms of regulatory consistency, cross-border supervision, and enforcement. Though the initial use cases of blockchain are likely to be mostly in supply chain management, financial services use cases can also gain popularity if there is adequate governmental and intergovernmental support. While global efforts are already underway in this direction, there is a need for more proactive involvement and participation of industry and policymakers, especially from emerging economies. The most notable effort so far in this direction is the constitution of a technical committee by The International Organization for Standardization (ISO/TC 307) for the international standardization of blockchain and distributed ledger technologies.8 The setting of international standards-setting applies also to AI and ML applications. Some international efforts are already underway for algorithmic trading, to counter any systemic risks that may arise from it (FSB, 2017). The International Organization of Securities Commissions (IOSCO) made recommendations for consideration while the Senior Supervisors’ Group (SSG) issued principles for supervisors to consider when assessing practices and key controls over algorithmic trading activities at banks (FSB, 2017). Concerns have also been raised by regulators; especially the USA and the EU who have called for efforts to avoid market abuse and prevent the strategy from contributing to, or causing, disorderly market behavior (FSB, 2017). The API Exchange (APIX), a global, open-architecture platform promoted by ASEAN Financial Innovation Network (AFIN) launched in November 2018, is another promising initiative. More such efforts are required to connect market players, design experiments collaboratively, and deploy new digital solutions that support financial innovation and inclusion.

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In many countries, the fintech sector is already closely regulated especially in some sub-sectors like peer-to-peer (P2P) lending platforms. To mitigate credit risk/ investment risk, many countries have placed caps on the lender/ investor and borrower/ issuer side in P2P platforms (OICO-IOSCO 2017). There may also be caps on the maximum amount of capital an issuer/ borrower can raise via P2P lending and equity crowdfunding in a particular period (for example, in the EU). Similarly, many countries have established minimum capital requirements as a buffer against operational risk or have mandated the use of a third-party custodian and have restricted the platforms from handling customer money. In Italy, for example, the platform must identify the source of operating risks, adopt suitable procedures and controls, and provide backup facilities (OICO- IOSCO 2017). Concerns regarding cybersecurity, data protection, and consumer protection are another set of issues that have attracted much attention in recent times (PetrPetralia 2019alia et al, 2019). As with all forms of financial innovation, technology, and digital financial services also entail new challenges for consumers. Disclosure requirements and fraud/mis-selling represent the most important recognized policy concerns. Access to complaint-handling mechanisms, data privacy, and security are other issues (OECD, 2017). According to OECD (2017), consumers can be exposed to new threats including the risk of digital fraud and abuses, misuse of personal financial data, lack of transparency, inadequate information on products and related redressal mechanisms, data privacy, and security vulnerabilities, cybercrime, etc. Additional potential consumer risks can derive from the digitally delivered product itself (for example, product unsuitable for the customer, or overindebtedness in case of digitally delivered credit) or from the way the product is delivered (e.g., mis-selling by agents with limited or no knowledge about the product). Excessive exposure to these risks can undermine consumers’ confidence and trust, thus compromising the potential of the industry itself. To sum up, the regulatory approach to ushering in new technology like blockchain has to keep the two objectives of balancing innovation and ensuring compliance. While there is a danger of excessive regulation stymieing the potential, at the same time, the growth of new products cannot be left to the market forces because of the potential implications on financial stability (FSB, 2017).Regulatory sandboxes are useful in this context and if developed carefully can contribute much to the establishment of regulatory space that supports innovation. As Cornelli et al. (2020) show, in the context of the UK, firms entering the UK regulatory sandbox raise significantly more capital in the quarters after entry. With respect to cross-border fintech growth, there is a lack of clearly defined global standards and API development protocols, which could streamline and promote innovation further.

4.4 FINTECH POLICY AND REGULATION IN INDIA – THE CASE OF INDIA In this paper, we present a case study of India with respect to distributed ledger technology. As discussed earlier, the methodology used in this paper is a systematic review of the global experience on fintech regulation and potential challenges.

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Like in other nations, the growth and development of the fintech industry have evoked much interest in the regulatory sphere in India, too. As an industry that holds immense potential as well as disruptive ability, the growth of fintech firms is being keenly watched by regulators across the board. The initial spurt came largely from private players such as large commercial banks or IT companies. State support is also catching up with a variety of support programs by both regulators and the government. Many State governments are also fast scaling up promotional activities and regulatory support to promote fintech growth in their respective states. The fintech valley9 project being implemented in Vishakhapatnam, Andhra Pradesh, and Maharashtra financial technology policy are some early notable initiatives of such nature. The fintech space in India is currently not regulated by a single entity. The fintech companies in India currently report to major regulators like the Reserve Bank of India (RBI), Securities and Exchange Board of India (SEBI), Telecom Regulatory Authority of India (TRAI), and Insurance Regulatory and Development Authority (IRDA) depending on the domain of their operation. For example, the fintech companies involved with payment and settlements are regulated by the Department of Payment and Settlement Systems of RBI through the Payment and Settlement Systems Act, 2007, and Payment and Settlement System Regulations, 2008. RBI-initiated organizations like National Payments Corporation of India (NPCI),Reserve Bank Information Technology Pvt Ltd (ReBIT), and Institute for Development and Research in Banking Technology (IDRBT) are the other major entities involved in the policy formation in this newly emerged sector. DLT and Blockchain technologies currently constitute the most keenly explored fintech sector, especially by the banking and financial services industry. Sensing the importance, all stakeholders in the Indian BFSI industry are exploring the use cases of the blockchain Currently, it is mostly tested in information sharing-based applications. The popular use cases which have gained traction in the Indian industry are intra-bank applications, authentication and document management, trade finance, and invoice discounting. The major difference the blockchain brings is that it enables the transacting parties to avoid multiple ledgers that they maintain. The DLT technology allows the execution of the real-time transaction by making an irreversible/undeletable entry into distributed ledger copy which is available to each participating entity in the chain. The technology can also provide a central Know Your Customer (KYC) that can be used for faster authentication (Ray, et al. 2018). The policy space has been fast evolving in the last few years with increased efforts to promote and regulate the growing fintech industry. The most important effort in this direction was the setting up of an inter-regulatory Working Group on Fintech and Digital Banking by RBI in July 2016 to study the regulatory issues of fintech and digital banking in India. The working group which was established following the recommendation of the Financial Stability and Development Council Sub Committee of the central bank provided an extensive report on evolving fintech scenario in India, opportunities, and challenges, and associated regulatory challenges (RBI, 2017). The committee in its report tabled in November 2017, also recommended RBI to set up a Regulatory Sandbox in India. It has identified the Institute for Development and Research in Banking Technology (IDRBT), an RBI research institute, to take lead in this regard. A similar recommendation was made

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earlier by the Watal Committee (2016) which enquired into the digital payments sector. The whitepaper on ‘Application of Blockchain technology to banking and financial sector in India’ brought out by IDRBT in January 2017, was another significant work that contributed to regulatory efforts in this direction. The Institute is heavily involved in promoting and mainstreaming promising use cases in Industry. The Proof-of-Concept (PoC) of BCT for trade finance application with the active participation of NPCI, Commercial banks, and the technology partner was one of the programs that established the feasibility of blockchain technology for the Indian banking and finance sector (IDRBT, 2017). In August 2019, the RBI released the final framework for setting up a regulatory sandbox to enable innovations in financial technology. This will allow fintech companies to test their applications in a flexible regulatory environment before introducing them to consumers. Following this lead of RBI other regulators like SEBI and IRDIRDAI, Insurance Regulatory and Development Authority of India 2019AI have also announced regulatory sandboxes for innovative fintech solutions in their respective markets (SEBI, 2019) (IRDAI, 2019).10,11 According to SEBI’s discussion paper on the framework for a regulatory sandbox, the initiative will be initially open only to entities regulated by SEBI under the SEBI Act of 1992. The main applicant in this initiative should be a market participant already registered with SEBI. This raises concerns about fintech startups with relevant innovations who may not be currently registered with SEBI nor have any collaborations with registered entities. While regulatory sandboxes are a tool in the right direction to help identify risks like the mitigation of principal and systemic risks, questions need to be raised regarding the current approaches in India. All the main regulators are active in supporting innovative fintech solutions but seem to be following a siloed approach with little interaction between them. The current trend in India is that of differentiated and focused sandboxes. While this suits our existing market and regulatory arrangement, it may also potentially exclude innovations that may be cross-cutting the regulatory domains. Also, it can be assumed that there is a tendency to understand the fintech companies using existing categories in the financial services market. Application of the existing framework of laws and regulations may harm the growth of the industry and stifle the pace of innovations within them. The evolution and development of P2P lending market regulation in India is an example of such trends. The RBI Master Directions for NonBanking Financial Company – Peer to Peer Lending Platform in 2017 and later revision in 2019 have also been initiated in a similar spirit (RBI, 2019). According to the latest directions, aggregate exposure of a lender to all borrowers at any point of time, across all P2P platforms is now capped at Rs 50 lakh and for making investments beyond Rs.10 lakh, the lender must establish a minimum net worth of Rs 50 lakh. Such efforts might be required in other fintech sectors also as the industry progresses. However, going by the experience of P2P lending market regulation in India, setting up a regulatory framework is never a single-shot game.12,13 Similar issues have arisen elsewhere also. For example, Slattery (2013) in a discussion of P2P lending in the US market argues that the interventions and requirements initiated by the Securities and Exchange Commissions in the late 2000s brought in substantial compliance costs, barriers to entry, and risks to

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consumers in the P2P lending market. In both scenarios mentioned above, the core concern follows a similar thread – there is a tendency for regulators to misunderstand the nature of these innovative products. Application of the existing framework of laws and regulations may harm the growth of the industry and stifle the pace of innovations within them. While a dedicated fintech policy/regulation is yet to be developed in the country, there is a general agreement that regulation should keep pace with technological change without hampering innovation. This was also a major finding of the much-discussed RBI publication, Report of Working Group on Fintech, and Digital Banking (RBI, 2018). The report points out that, “… as of now, the Fintech risks are being looked at more in terms of what is associated with the traditional IT systems, such as cyber-security risks” (RBI, 2018). This is certainly only a part of the concerns that fintech growth brings with it and warrants a more concentrated regulatory approach to balancing innovation and risk.

4.5 CONCLUSION In this paper, we have explored the emerging regulatory regime for the fintech industry and the challenges it may face with India as an illustrative case study. The application of fintech technology is still at a nascent stage and while there are success stories, there are also issues in widespread use both globally and in India. While there is much to gain from emerging innovative technologies, there is always a dilemma about the nature and scope of regulatory response. The objective of this paper is to highlight the balance that is required in the regulatory approach that encourages both innovation and ensures compliance. There is a general agreement that regulation should keep pace with technological change without hampering innovation. At the same time, the growth of new products cannot be left to the market forces because of the potential implications on financial stability and society at large. The use of regulatory sandbox by many countries has highlighted their usefulness and remains the most promising regulatory approach that requires more concentrated attention. Many countries are also using innovation hubs to foster innovation in the fintech space. One limitation of the current paper is that it does not quantitatively evaluate the regulatory structure of fintech regulation in India. This is partly due to the lack of data as well as few use cases currently available for in depth analysis. As India starts implementing DLT-based applications and other fintech technologies more widely, it needs to be seen whether the current regulatory framework is adequate to promote innovation along with managing the risks. This can be explored in the future.

NOTES 1 The DLT is a novel way of sharing data across multiple data stores (or ledgers) (World Bank, 2017). The shared database allows peer to peer transaction without the requirement of a central authority. 2 The global fintech landscape can be mapped across eight broad categories: Payments, Insurance, Financial Planning, Lending, Blockchain, Trading & Investments, Data & Analytics, and Security (OICU-IOSCO, 2017). In this paper, we focus on the largely on payments and the use of blockchain and artificial intelligence for payments.

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3 Draft guidelines for the regulatory sandbox by RBI in August 2019. ( https://www.rbi.org.in/ Scripts/BS_PressReleaseDisplay.aspx?prid=47869 4 The period of six months is recommended but is not a set timescale. 5 http://download.asic.gov.au/media/4420907/rg257-published-23-august-2017.pdf 6 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6910&context=lkcsb_research 7 IMPS is an instant interbank electronic fund transfer service through mobile phones. It is also being extended through other channels such as ATM, Internet Banking, etc. Such a system does not require maintaining multiple ledgers by the transacting parties. The DLT would allow real-time transactions by making an irreversible/ undeletable entry into the distributed ledger copy available to each of the chain’s participants. The use of DLT may also divest the need for a clearinghouse. The central authority would be just another participating entity with regulatory and governance functions. A consensus mechanism can be implemented using algorithms for the definition of an authentic ledger thus guaranteeing transaction security and integrity. 8 The Secretariat is managed by Australia as the proposal came from Standards Australia ( Standards Australia, 2017). The committee has six working groups which are looking into issues of terminology, reference architecture, taxonomy, and ontology, use cases, security and privacy, identity, and Smart Contracts. 9 The most notable has been the case of Andhra Pradesh which has planned a fintech hub in Vishakhapatnam. Details can be found at http://www.fintechvalleyvizag.com/ 10 SEBI | Discussion Paper on Framework for Regulatory Sandbox 11 PRESS RELEASE (irdai.gov.in) 12 https://www.techinasia.com/p2p-lenders-india-to-regulate-or-not 13 https://economictimes.indiatimes.com/startups/peer-to-peer-lending-startups-seekclarity-on-guidelines-issued-by-rbi/articleshow/52069933.cms

REFERENCES AITF, Artificial Intelligence Task Force (2018). The Report on the Artificial Intelligence Task Force. Constituted by Ministry of Commerce and Industry, Government of India. Available at http://dipp.nic.in/sites/default/files/Report_of_Task_Force_on_ ArtificialIntelligence_20March2018_2.pdf Arner, D.W., Barberis, J.N., and Buckley, R.P. (2015). The Evolution of FinTech: A New PostCrisis Paradigm? Available at http://hdl.handle.net/10722/221450, accessed on 8th May 2018. ASIC, Australian Securities and Investments Commission (n.d). Regulatory sandbox. Available at http://asic.gov.au/for-business/your-business/innovation-hub/regulatorysandbox/, accessed on 19th April 2018. ASIC, Australian Securities and Investments Commission (2017). Regulatory Guide RG 257 Testing fintech products and services without holding an AFS or credit license. Available at http://download.asic.gov.au/media/4420907/rg257-published-23-august2017.pdf, accessed on 19th April 2018. BIS, Bank of International Settlements (2018). Sound Practices: Implications of fintech developments for banks and bank supervisors. Available at https://www.bis.org/bcbs/ publ/d431.htm, accessed on 15th April 2018. EBA, European Banking Authority (2017). Consultation document Fintech: a more competitive and innovative European financial sector. European Banking Authority, Brussels, Belgium. Available at https://ec.europa.eu/info/sites/info/files/2017-fintechconsultation-document_en_0.pdf, accessed on 19th April 2018.

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FCA, Financial Conduct Authority (2018). Global Sandbox. Available at https://www.fca. org.uk/firms/regulatory-sandbox/global-sandbox, accessed on 18th April 2018. FCA, Financial Conduct Authority (2017). Regulatory sandbox lessons learned report. Available at https://www.fca.org.uk/publication/research-and-data/regulatorysandbox-lessons-learned-report.pdf, accessed on 19th April 2018. FSB, Financial Stability Board (2017). Financial Stability Implications from Fintech: Supervisory and Regulatory Issues that Merit Authorities’ Attention. Available at http://www.fsb.org/wp-content/uploads/R270617.pdf, accessed on 19th April 2018. GPFI, Global Partnership for Financial Inclusion (2017). 2017 Financial Inclusion Action Plan. Available at http://www.gpfi.org/sites/default/files/documents/2017_g20_financial_ inclusion_action_plan.pdf, accessed on 15th April 2018. IDRBT. Institute for Development and Research in Banking Technology (2017). Applications of blockchain technology to banking and financial sector in India. IDRBT, Hyderabad, India. IRDAI, Insurance Regulatory and Development Authority of India (2019). Regulatory Sandbox Approach. Press release. Ref. No: IRDAI /INT/ Reg Sandbox/ 08/ 2019. Date: 22-08201. Available at https://www.irdai.gov.in/ADMINCMS/cms/frmGeneral_Layout.aspx? page=PageNo3880 OECD (2017). G20/OECD INFE Report on ensuring financial education and consumer protection for all in the digital age. Available at http://www.oecd.org/finance/financialeducation/G20-OECD-INFE-Report-Financial-Education-Consumer-ProtectionDigital-Age.pdf, accessed on 30th April 2018 OICO-IOSCO (2017). IOSCO Research Report on Financial Technologies (Fintech). Available at https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf, accessed on 28th December 2017. Petralia, K., Philippon, T., Rice, T., and Veron, N. (2019). Banking disrupted? Financial Intermediation in an era of Transformational Technology, ICMB and CEPR, Geneva Reports on the World Economy 22. Ray, S. (2019). RBI’s Sandbox rule needs more clarity, Financial Express, May 3. https://www.financialexpress.com/opinion/rbis-sandbox-rules-need-more-clarity/ 1566297/ Ray, S., Paul, S., and Miglani, S. (2018). Application of Innovative Technologies for Promoting Financial Inclusion. Report submitted Ministry of Finance, Government of India. Ray, S., Paul, S., and Miglani, S. (2019). Use of Blockchain and Artificial Intelligence to promote financial inclusion in India. Asia Pacific Tech Monitor, Jan-March. RBI, Reserve Bank of India (2018). Reserve Bank of India, RBI (2018). Report of the InterRegulatory Working Group on FinTech and Digital Banking, Mumbai. Available at: https://www.rbi.org.in/scripts/BS_PressReleaseDisplay.aspx?prid=43097 RBI, Reserve Bank of India (2019). Review of Master Directions - Non-Banking Financial Company – Peer to Peer Lending Platform (Reserve Bank) Directions, 2017. RBI/ 2019-20/121, DOR.NBFC(PD) CC.No.106/03.10.124/2019-20. December 23, 2019. Accessed from https://www.rbi.org.in/Scripts/NotificationUser.aspx?Id=11764& Mode=0 SEBI, Securities and Exchange Board of India (2019). Discussion paper on framework for regulatory sandbox. Reports for public comments. Accessed from https://www.sebi. gov.in/reports/reports/may-2019/discussion-paper-on-framework-for-regulatorysandbox_43128.html Schindler, J. (2017). FinTech and Financial Innovation: Drivers and Depth. Finance and Economics Discussion Series 2017-081. Washington: Board of Governors of the Federal Reserve System, 10.17016/FEDS.2017.081.

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Slattery, P. (2013). Square pegs in a round hole: SEC regulation of online peer-to-peer lending and the CFPB alternative. Yale Journal on Regulation, Vol. 30, 233. Standards Australia (2017). Blockchain Standards Initiative. Available at https://www. standards.org.au/getmedia/dd5321aa-2a74-4799-bb04-766122b6669d/BlockchainStandards-Initiative.pdf.aspx, accessed on 19th April 2018.

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Financial Inclusion through the Sphere of Solidarity in Corporate Governance The Cases of Digital Crowdfunding and Conventional Microfinance Djamchid Assadi Université Bourgogne Franche-Comté, France

Jack Wroldsen California Polytechnic State University, United States

5.1 INTRODUCTION Microfinance and non-equity crowdfunding ventures present two paradoxical and combustible characteristics, both of which can be resolved through greater financial inclusion, augmented by artificial intelligence (AI). First, microfinance and nonequity crowdfunding both rely on supporters who are not equity owners yet who nonetheless contribute financial resources to the venture. Second, both types of business ventures juxtapose for-profit interests against mission-driven entrepreneurship. Bitter conflict arises when such ventures attract investment that benefits shareholders but excludes non-equity contributors. These contributors, who are often among the venture’s earliest and most ardent supporters, perceive their financial exclusion as betrayal. And capitalizing on the power of social media, the betrayed supporters then voice their disapproval and tarnish a company’s reputation. Using the case study method, we highlight two examples of such financial exclusion and betrayal in crowdfunding: (1) the acquisition of Minecraft by Microsoft and (2) the acquisition of Oculus Rift by Facebook. We also highlight two examples in microfinance: (1) the public stock offering of SKS Society in India and (2) the Banco Compartamos stock offering in Mexico. In each of the case studies, the company excludes and thereby alienates some of its most enthusiastic supporters. 52

DOI: 10.1201/9781003125204-5

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As a solution, we propose a strategic management paradigm based on the philosophical concept of “recognition” and the “sphere of solidarity.” A strategy based on recognition and the sphere of solidarity leads to management decisions that are financially inclusive and build long-term and trusting relationships with non-equity contributors. A recognition strategy can also seamlessly incorporate AI into decision-making through algorithms that measure and value non-equity contributions made via social media. In non-equity crowdfunding, the recognition problem arises because a venture accepts financial contributions from supporters who believe in the venture’s mission, but who do not receive any equity ownership in exchange for their financial contributions. Then, when founders of a crowdfunded venture sell the company at a significant profit, shareholders celebrate the financial success while non-equity supporters feel betrayed and excluded. Thus, founders end up alienating those stakeholders who were first to believe in the mission of the venture and undermining trust and long-term relationships with supporters. A similar problem occurs in microfinance. Often motivated by a humanitarian mission to help low-income people pursue entrepreneurship, microfinance institutions (MFIs) attract deposits from, and provide loans to, individuals who often lack access to credit through traditional banks. But MFI borrowers—who are also contributors to the MFI’s capital balances through their deposits—are not MFI shareholders; thus, when MFIs sell stock in the public markets, the low-income depositor-borrowers do not share in the profits. Although no legal violation occurs and shareholders welcome the profits, many feel the MFI’s social mission is betrayed when the institution acts in the same manner as a typical for-profit company and excludes non-equity supporters (i.e., the depositor-borrowers) from the financial benefits of equity ownership. Thus, similar to crowdfunding, MFI executives end up alienating and excluding precisely those stakeholders with whom they desire to build trusting, long-term relationships and on whom the success of the business model depends. Conventional conceptualizations of corporate governance—which endorse and codify shareholder-centric conduct—underestimate, or even ignore outright, the betrayal that crowdfunding and microfinance supporters feel under the circumstances described above. Our contention in this chapter is that the conventional conceptualization is deficient and short-sighted in the context of industries, such as crowdfunding and microfinance, that rely to a significant degree on non-equity supporters. As exemplified in the four cases highlighted in this chapter, we claim that disregarding the interests of non-equity supporters in the context of microfinance and crowdfunding is a critical oversight in the theory and practice of corporate governance and strategic management. Consequently, we suggest integrating the philosophical concept of recognition, which multiple social sciences increasingly espouse but that the strategic management literature largely ignores, into the corporate governance of crowdfunding ventures and MFIs. We propose the concept of the sphere of solidarity (which originates from the German philosopher Axel Honneth’s work on recognition) to argue for greater financial inclusion of financial supporters who lack shareholder rights (i.e., non-equity contributors). Furthermore, we propose that companies integrate AI into recognizing non-equity contributors fairly and efficiently.

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The philosophical concept of recognition enriches the literature and the practice of strategic management and corporate governance in several ways. First, it facilitates reflections on the recurrent controversies between shareholders’ pecuniary objectives and supporters’ subjective values, particularly in collaborative and crowd-based organizations like microfinance institutions and crowdfunding ventures. Second, it deepens and broadens the perimeter of corporate governance and strategic management by linking formal legal rights with human relationships of trust, morality, and mutual interest that exist outside the scope of formal legal rights. Third, our research expands an emerging inter-disciplinary inquiry regarding how human, social, and ethical values might be constructed within entrepreneurial and managerial frameworks. Our discussion is structured in three parts, with the overriding goal of improving financial inclusion by uniting management research on corporate governance with philosophical research on recognition. First, we present microfinance and crowdfunding cases that contain controversies between the profit goals of shareholders and the value expectations of non-equity supporters. These cases provide the basis for discussing the concept of recognition. Second, we provide a targeted account of the literature on corporate governance to explore its suitability for integrating the philosophical concept of recognition. We also briefly review the philosophical literature on recognition, especially Honneth’s conceptualization of the sphere of solidarity. Last, we suggest practical ways to implement the concept of the sphere of solidarity in the context of crowdfunding and microfinance to increase financial inclusion and leverage the efficiencies of AI.

5.1.1 CASE STUDIES VALUES

OF

CONFLICT

BETWEEN

FINANCIAL INTERESTS

AND

SOCIAL

We rely on the case study method to suggest a recognition-based theory of corporate governance and strategic management because the case study method supports theory building from specific contextual analyses (Eisenhardt & Graebner, 2007; Edmondson & McManus, 2007). In contrast to the deductive approach, which relies on a representative sample of a target population, the inductive approach examines particular phenomena in the context of individual cases (Eisenhardt, 1989). The phenomenon we study is the tension between the profit goals of shareholders and the mission-oriented interests of non-equity supporters in the context of MFIs and crowdfunding. Namely, we examine the MFI cases of SKS Society and Banco Compartamos and the crowdfunding cases of Oculus Rift and Minecraft. Furthermore, to analyze the cases we adopt the methodology of “interpretivism,” which prioritizes the meaning and values that participants attach to their endeavors instead of focusing on empirical data through a “positivist” approach (Leitch et al., 2010). 5.1.1.1

Banco Compartamos: The Case of Helping Low-income Entrepreneurs Banco Compartamos, a Mexican MFI whose name means “Let’s share” in Spanish, began as a non-profit organization and later converted into a for-profit entity, leaving 51% of its shares to non-governmental and social mission groups (Eldar, 2015).

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In 2007, Compartamos decided to go public with the objective of expanding the reach of funds to low-income micro-entrepreneurs and to its non-profit equity holders with a social mission, as well as to benefit other shareholders, including company executives. The initial public offering (IPO) met with success and raised over $450 million. The non-profit organization, Acción, as an example, turned its original $1 million investment into $135 million after selling only half of its Compartamos shares on the public market (Malkin, 2008). Executives of Compartamos sold stock holdings worth $150 million. The reproach of supporting stakeholders was, however, immediate and pervasive. Critics asserted that only equity investors benefitted from the IPO while the local low-income and frequently female micro-entrepreneurs, who paid interest rates of approximately 90% annually and thereby helped generate profits for the investors, were excluded (Malkin, 2008). 5.1.1.2

SKS Microfinance: The Case of Two Visions of Helping Low-income Entrepreneurs Vikram Akula, who was born in India and raised in New York, worked in rural India in the 1990s as a community organizer of women’s self-help groups. He set up SKS Society as a non-profit microfinance institution (MFI) in 1997 for loaning to local micro-entrepreneurs. In 2005, the for-profit “SKS Microfinance” was added to SKS Society to channel the capital markets to the underserved. The combined SKS operations made up India’s largest microfinance institution with $1.2 billion in loans. In 2006, Mr. Akula was named by Time Magazine as one of the world’s 100 most influential people and was recognized as the Ernst & Young Entrepreneur of the Year. Despite the success and growth, Mr. Akula found that all MFIs combined in India could only meet 10%–15% of the $50 billion the indigent households needed in credit. Thus, he decided to file for an IPO in July 2010 to secure additional funding for growth. Despite the financial success of the IPO, raising some $350 million and being more than 13 times oversubscribed, the IPO encountered controversy from a social and value perspective (Knowledge@Wharton, 2010). Many directors resigned in protest against steering funds toward SKS rather than to the poor (Strom & Bajaj, 2010). Muhammad Younus, who pioneered the microfinance field in Bangladesh and received the Nobel Peace Prize for his achievement in 2006, criticized the IPO for making money off poor people while insisting that microfinance should remain mission-driven (Knowledge@ Wharton, 2010). By October 2010, SKS’s stock price declined 91% from its peak and the rate of loan collection fell sharply. Mr. Akula resigned from the board and his operational roles in 2011. However, he retained SKS stock options worth potentially $50 million after having previously sold shares of $13 million (Strom & Bajaj, 2010). After two years of incurring losses of $250 million, outside directors downsized the company by closing numerous branches and cutting costs, with the elimination of 1200 jobs, in hopes of returning the publicly traded microfinance institution to profitability (Bandyopadhyay & Unnikrishnan, 2013).

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5.1.1.3 Oculus Rift: The Case of Forgetting the First Supporters The controversy between the competing perspectives of financial returns and mission-driven interests is not the monopoly of conventional offline MFIs. Crowdfunding projects, which issue an open call to the public for funding and support via the internet, experience a similar conflict in pursuing collective-interest projects. The Oculus Rift is a headset for virtual reality applications that the young American, Palmer Luckey, first developed in his parents’ garage in California. To fund research and development of the headset, Mr. Luckey launched a crowdfunding campaign on Kickstarter to appeal to virtual reality enthusiasts. The campaign was a smashing success. Over 9,500 people contributed nearly $2.5 million, almost 10 times the amount initially sought. In return, the crowdfunding supporters received tokens of appreciation, like T-shirts or the promise of receiving one of the first Oculus Rift headsets once a consumer version was ready. But supporters did not receive any stock or other financial interests because supporters were legally barred from purchasing stock (due to government restrictions on the sale of unregistered securities through crowdfunding) (Wroldsen, 2016). Oculus’s financial feat continued even after the Kickstarter campaign. One year later, in 2013, Oculus attracted $91 million in venture capital investment. Then, in 2014—less than two years since the initial crowdfunding campaign on Kickstarter— Luckey agreed to sell the company to Facebook for $2 billion. The earliest backers and fans of Oculus who did not share in the substantial profits that founders and investors enjoyed instantly protested on social media. There was an outburst of hundreds of angry messages like: “I cannot put into words how betrayed I feel by this” or “[t]he community brought you here, and your disingenuous posts are f … insulting,” and “F … everything about this” (Makuch, 2014; Stuart, 2014). Thus, many of those who began as impassioned supporters, financial backers, and future customers of Oculus ended as embittered enemies. 5.1.1.4 Minecraft: The Case of Excluding the Enthusiastic Community Markus Persson, a Swedish video game programmer, created Minecraft in 2009 out of his design studio, Mojang Studios. Minecraft is a computer game in which participants build and destroy virtual creations using electronic blocks. From the beginning, Persson cultivated a community ethos. He released the software code as open source (Chaboud, 2016) and encouraged crowdsourced ideas to improve the Minecraft experience. Enthusiasts also financed the early development of the game by purchasing the early versions at $13 long before the game was marketed broadly. Minecraft became the best-selling video game of all time and Persson enjoyed tremendous credibility due to his dedication to the gaming community. However, Persson shocked his devoted community when he sold Minecraft to Microsoft for $2.5 billion in 2014, a mere six months after Facebook acquired Oculus (Ovide & Rusli, 2014). The shock was especially poignant because after Facebook acquired Oculus, Persson had posted on his blog that he “did not chip in ten grand to seed a first investment round to build value for a Facebook acquisition” (Chaboud, 2016). Persson’s criticism of the Oculus transaction, however, did not prevent him from consummating the transaction with Microsoft. And thus,

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similar to the reactions to the Oculus transaction, early Minecraft developers, gamers, and supporters who did not share in the financial fruits condemned the Minecraft sale as a betrayal of community values and of open collaboration, viewing Minecraft as completely incompatible with Microsoft’s corporate culture of proprietary protections. 5.1.1.5 Case Study Analysis Through an Interpretive Lens In keeping with the interpretivist rather than positivist/empirical approach (Leitch et al., 2010), our analysis of the case studies highlights the betrayal that non-equity stakeholders experience. We ask, therefore, what subjective meaning do non-equity stakeholders attach to their involvement with crowdfunding and microfinance ventures, even though such stakeholders lack formal legal rights? Furthermore, should that subjective meaning be of concern to managers? If so, what conceptual view should executives and directors adopt to account for that meaning in their decision-making? We contend that a corporate governance issue lies at the heart of these questions. We also contend that the philosophical concept of recognition can properly address such questions. Accordingly, following a brief overview of relevant research, we propose the philosophical notion of the sphere of solidarity as the lens through which managers should address the financial exclusion that non-equity contributors experience in crowdfunding and microfinance ventures.

5.2 LITERATURE REVIEW: CORPORATE GOVERNANCE AND THE SPHERE OF SOLIDARITY Our literature review first briefly frames the long-standing debate in corporate governance between shareholder and stakeholder rights. Then we introduce the philosophical concept of recognition as the necessary scaffolding for incorporating the sphere of solidarity into the theory and practice of corporate governance and strategic management in crowdfunding and microfinance.

5.2.1 CORPORATE GOVERNANCE For nearly 100 years, one of the foundational questions of corporate governance has remained: for whose benefit is a corporation run? More specifically, do managers solely owe a duty to maximize profits for shareholders (Berle, 1932), or do managers also owe duties to other stakeholders, such as employees, customers, suppliers, creditors, and communities (Dodd, 1932)? From the perspective of shareholder governance, a corporation’s singular goal and responsibility is to increase shareholder profits, so long as the corporation is engaged in open, free, and lawful competition (Friedman, 1962, 1970). In this view, pursuing the objective of shareholder profit leads to the greatest net benefit to society; therefore, that objective should not be hindered by alternative social goals (Bainbridge, 2003; Smith, 2008). From the perspective of stakeholder governance, in contrast, a corporation should pursue the multi-faceted goal of not only creating profit for shareholders but

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also benefitting other stakeholders, like customers, suppliers, employees, communities, and the environment (Greenfield, 2008, 2014). The two views can be harmonized to some extent when management perceives that a decision that benefits other stakeholders will also improve long-term profitability (such as an increase in employee engagement, motivation, and productivity that results from paying higher salaries or developing a positive reputation with customers through financial support of environmental causes). Acknowledging the interests of stakeholders can even be seen as essential, such as when certain stakeholder contributions are particularly important to the company’s success or when failing to secure relationships with certain stakeholders could threaten the company’s survival (Lehman, 2007). Similarly, taking the interests of other stakeholders into account is not an option but rather the strategic responsibility of managers in the case of a virtual organization that relies on extensive networks of partners, where the partners are proponents of the organization’s own profitability (Hany, 1995; Davidow & Malone, 1992). Another important insight from the corporate governance literature is that some shareholders are motivated by more than just profit: the achievement of non-financial goals has even been found to compensate for lower financial returns for certain investors (Rubaltelli et al., 2015). Thus, in certain situations, stakeholders have heterogeneous social, ideological, or identity-based interests that are as important or more important than the stakeholders’ financial interests. Naturally, those situations create more complex governance and management considerations in which one-dimensional management theories are inadequate. The inadequacy of one-dimensional models has thus given rise to more multi-faceted and contextualized approaches (Verran, 2001; Wolfe & Putler, 2002; Rowley & Moldoveanu, 2003; de Bakker & den Hond, 2008; de Bakker et al., 2013). Consistent with a more contextualized approach, we contend that the multi-faceted stakeholder interests present in crowdfunding and microfinance call for incorporating the philosophical notion of the sphere of solidarity into the theory and practice of corporate governance and strategic management as a means of improving financial inclusion of non-equity contributors.

5.2.2 THE SPHERE

OF

SOLIDARITY

Our application of the sphere of solidarity embraces the deeply rooted idea that people thrive in supportive community. Honneth’s conception of the sphere of solidarity—which we apply to corporate governance in this study—grows out of the philosophical concept of recognition, which refers to the meaning or self-worth that people obtain from mutually-respectful and reciprocal interactions with others (Haacke, 2005; Soroko, 2014; Honneth, 1996; Fukuyama, 1992; Habermas, 1984, 1987, 1992). Recognition, in turn, originated from the emergence in modern Western philosophy of the concept of the individual (Mead, 1934; Fukuyama, 1992; Honneth, 1996), which for its part, for some authors, arose out of the JudeoChristian assertion of individuals deriving their rights and worth from God (Dent, Jr., 2004; Seligman, 1997).

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As such, definitions of recognition capture a wide range of philosophical thought and express a similar sentiment to the Judeo-Christian ideals of loving your neighbor as yourself and doing to others as you would have them do to you (Leviticus 19:18; Matthew 7:12). For instance, one description of recognition is that “recognizing persons is inseparable from being obliged to treat them in a certain way: according them respect” (Taylor, 1991). Similarly, Honneth defines recognition concisely as “the reciprocal limitation of one’s own egocentric desires for the benefit of the other” (2012). That is, recognition involves restraining one’s own interests, promoting the interests of others, and, correspondingly, receiving the same respectful and unselfish treatment in exchange. Recognition, then, is a two-way street: it requires that a person both recognize (i.e., value and accept) others and be recognized (i.e., be valued and accepted) by others in reciprocal fashion (Pippin, 2000; Habermas, 1984, 1987, 1992). Tension and conflict can arise, therefore, when recognition is not given or reciprocated, whether in interpersonal relationships or societal conflicts (Taylor, 1994; Fukuyama, 1989, 1992, 1995), or even, as we assert in this chapter, in the corporate governance of crowdfunding and microfinance ventures. Expressing the concept of recognition in terms of the sphere of solidarity is a way of succinctly capturing the reciprocal nature of recognition (Honneth, 1996). Maintaining solidarity with others creates a sense of social acceptance and undergirds a person’s self-worth because the members of the sphere of solidarity mutually recognize each other’s capabilities, contributions, and uniqueness (Honneth, 1996). Although recognition is often understood by analyzing negative feelings of nonrecognition, such as contempt or humiliation, Honneth argues that people are also motivated by positive principles of recognition. The modern individual seeks recognition through love in affective relationships, through solidarity linked to the individual’s contribution being considered useful and valuable, and through equality in legal status and relationships (Honneth, 1996, 2004). Since the 1990s, the philosophical sense of recognition has been discussed in law and social sciences: for example, justice in solar geoengineering (Hourdequin, 2019), economic justice and equality for the disadvantaged (Fraser, 2003), workers’ rights (Minda, 2013), criminal law (Carvalho, 2012), international law (Tourme-Jouannet, 2013), family law (Aloni, 2014), and transitional justice (Haldemann, 2008). But the use of the term in management sciences is limited. For example, one study illuminates the importance of esteem-seeking as a primary motivation in business contexts and notes that not receiving esteem (i.e., not receiving “recognition”) disincentives performance (Brennan & Pettit, 2000). A different study finds that members of open-source communities develop a shared basis of formal authority but limit that authority using democratic mechanisms and look to shift authority over time to different members of the community (O’Mahony & Ferraro, 2007). The O’Mahony and Ferraro study does not address the concept of recognition directly, though the themes in the study are compatible with recognition because they seek less conflictual and dialogbased modes of governance for businesses that rely on crowds and communities. More commonly, where the term “recognition” is used in management sciences, it is mainly employed in its non-philosophical meanings, such as when the word “recognition” means acknowledgment (e.g., recognition of free speech rights),

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identification (e.g., recognition of faces), or appreciation (e.g., recognition of an achievement). For instance, several papers relate to appreciation of collaborators in innovation processes (Bhatnagar, 2014), entrepreneurial opportunities for women (Orser et al., 2013), products in EU countries (Sievers & Schmidt, 2015; Schmidt, 2007; Nicolaïdis, 2007; Lavenex, 2007; Poiares, 2007; Pelkmans, 2007), ASEAN regional qualification (Hamanaka & Jusoh, 2018), trade agreements (Jang, 2018), corporate opportunities (Mahnke, 2007), employee stock options (Kuo et al., 2014), revenue recognition (Okaily et al., 2019; Lu & Wang, 2018; Hu et al., 2014; Haraldsson, 2017; Ragothaman & Lavin, 2008; Stallworth & Braun, 2007; Agarwal, 2006; Altamuro et al., 2005), and investments in human capital (Sriram, 2018; Sarra, 1999). Other studies use the term in reference to the engagement of scientists and engineers (Kirtchik, 2019), justice for individuals (Elias & Jensen, 2014), land tenure rights (Fitzpatrick, 2005), and acknowledgment of cultural diversity (Angelucci et al., 2019; Saeys et al., 2019).

5.2.3 COMBINING CORPORATE GOVERNANCE

AND THE

SPHERE

OF

SOLIDARITY

We believe it is valuable to adapt Honneth’s concept of the sphere of solidarity into the corporate governance literature in the areas of crowdfunding and microfinance because the sphere of solidarity justifies and explains management’s inclusion of stakeholders who lack formal shareholder rights yet nonetheless support a venture financially. When such stakeholders are embraced within the sphere of solidarity, they become some of the company’s most ardent backers, and potential customers and evangelists. But when such stakeholders are excluded—that is, denied recognition—they become especially embittered and not only may damage the company’s reputation through social media but also may boycott the company and switch to a competitor’s products or services. In sum, alienating a community of supporters converts them into disinterested observers or even antagonistic adversaries, but including them within the company’s sphere of solidarity preserves their support, even when no legal obligation to do so exists.

5.3 DISCUSSION: THE SPHERE OF SOLIDARITY IN CROWDFUNDING AND MICROFINANCE As exemplified through the four case studies presented in this chapter—SKS, Compartamos, Minecraft, and Oculus—we assert that in crowdfunding and microfinance, legal rights alone should not dictate the way management treats stakeholders. Instead, managers should go beyond legal rights in developing relationships with stakeholders. We posit the sphere of solidarity as the prism through which managers should view the contributions of non-equity stakeholders. Furthermore, we assert that greater financial inclusion of non-equity contributors can be enhanced through the efficient use of AI. Non-equity crowdfunding contributors share an affinity with the ventures they support, beyond bottom-line financial interests (Mollick, 2016; Gerber & Hui, 2013). And microfinance ventures, for their part, are viewed as straddling the

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separate arenas of profit-seeking firms and mission-based efforts to help lowincome individuals. In both cases, non-equity stakeholders of crowdfunding and microfinance ventures experience a sense of exclusion and betrayal when the ventures pursue equity transactions in the capital markets. This perceived betrayal occurs both because the initial community of supporters was motivated by nonfinancial outcomes and because the non-equity stakeholders are excluded from the financial fruits of the capital markets transactions. Conventional theory and practice in corporate governance and strategic management privilege financial returns among equity shareholders and marginalize or outright ignore non-equity supporters’ values. We posit the sphere of solidarity to address the misalignment between the financial priorities of equity shareholders and the non-financial values of non-equity stakeholders. In the philosophy of recognition, the sphere of solidarity recognizes an individual’s unique characteristics, values, and identity within social interactions. From this perspective, non-equity contributors in crowdfunding and microfinance seek recognition of their personal value (i.e., their individual self-esteem and even self-worth) through solidarity with other like-minded people who support crowdfunding or microfinance ventures. Our contention is that managers are wise to show respect to such contributors and honor their values and participation. In short, to include them. Doing so cultivates trusting and long-term relationships with those whose support both helped the venture grow and remains critical to the venture’s ongoing sustainability. Not doing so excludes valuable contributors, and even if excluding them may increase a venture’s short-term profits, it comes at the expense of alienating some of the most passionate supporters of the venture’s core mission.

5.3.1 PRACTICAL STEPS

TO IMPLEMENTING THE

SPHERE

OF

SOLIDARITY

We propose a four-step model as a roadmap for the application of recognition and the sphere of solidarity in strategic management and corporate governance decisions. The roadmap can be applied retroactively if necessary, so long as the measures are genuine and not utilitarian. The goal is to increase the inclusion of nonequity contributors who provide valuable support to a business but who lack the legal rights of equity owners. First, managers must cultivate the intent to include non-equity supporters and respect their motivations, values, and financial interests. Second, managers must develop a nuanced understanding of non-equity supporters’ values, priorities, and contributions. It is here where AI may help managers measure the value of non-equity contributions. This can be accomplished efficiently through algorithms that capture a non-equity contributor’s influence on social media. For example, upon analyzing social media data, managers may place more value on a non-equity contributor who attracted numerous additional contributors from his or her sphere of influence compared to a non-equity contributor who attracted fewer additional followers to the venture. AI could also be used to predict which non-equity supporters are more likely to create value for the venture, thus helping the venture to target its social media marketing efforts to those supporters.

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Third, managers must design tangible actions that include non-equity supporters in corporate decisions and honor the interests, values, and contributions of nonequity supporters. Finally, managers must publicize and implement the recognition strategy through social media in a way that maximizes access and impact for non-equity supporters. For example, in each of the cases highlighted in this chapter, managers could have embraced non-equity contributors’ authentic priorities and even found ways to include the non-equity contributors in the capital markets transactions that the organizations pursued. Doing so not only could have avoided public outcry but also could have fostered trusting relationships with non-equity stakeholders for years to come. In the cases of SKS and Compartamos, a strategy based on the sphere of solidarity could have resulted in sharing a certain amount of the funds raised through the IPO with low-income micro-entrepreneurs who had consistently made timely loan payments. This approach could have been carried out in numerous ways, such as, at a minimum, granting new loans with reduced interest rates to deserving borrowers. More aggressively, SKS and Compartamos could have allocated some of the profits from the IPO to be disbursed directly to qualifying borrowers as a onetime or even ongoing bonus. These financial incentives could have helped avoid non-equity stakeholders’ bitterness, frustration, and resentment. Apart from financial incentives, the Oculus and Minecraft cases reveal how a strategy based on the sphere of solidarity could have provided some of the six nonfinancial benefits that crowdfunding participants have been found to seek: entertainment, political expression, patronizing of the arts, altruistic giving, community building, and crowdsourcing collaboration and creativity (Schwartz, 2015). For example, Oculus and Facebook could have jointly sponsored an event at Facebook’s campus for fans to experience Oculus’ new virtual reality technology or carried out an invitation-only live demonstration. Either approach would tap into the non-financial crowdfunding values of entertainment, community, collaboration, and creativity. A recognition strategy based on the sphere of solidarity also could have involved the non-equity supporters in the further development of the Oculus Rift instead of sequestering the development of the product into Facebook’s private corporate processes. Notably, four years after Facebook’s multi-billion-dollar acquisition, and prior to launching the company’s first commercial virtual reality system in 2016, Oculus attempted to reengage crowdfunding supporters and offered a free headset to those who had contributed at least $275 (Musil, 2016). But this limited offer, coming four years after the acquisition, appears to be more of a utilitarian offer than a recognition strategy that seeks to create genuine connections with a community. In the case of Microsoft’s acquisition of Minecraft, most non-equity supporters also felt left out of the transaction. Their exclusion is particularly unfortunate because Minecraft is a product that largely depends on users’ co-creative efforts. A strategy based on the sphere of solidarity, though, could have avoided the feelings of betrayal that the Minecraft community experienced and instead replaced those feelings with loyalty and long-term support. Apart from monetary rewards (like sharing in some fashion a portion of the $2.5 billion purchase price with the Minecraft community), a recognition strategy could have taken the form of a meaningful and memorable experience. For example,

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all-expenses-paid flights ferrying long-time Minecraft supporters to a festival, possibly in Minecraft’s birthplace of Sweden, would have enhanced social ties with nonequity stakeholders and created a joint memory to unify supporters for years. Instead of social media uproar, the internet would have been abuzz over Minecraft and Microsoft’s generous commitment to supporters. A conception of corporate governance enhanced by the philosophical notion of the sphere of solidarity rejects that non-equity contributors may be disregarded solely because they lack formal legal rights. Philosophically, there is no conceptual difference (although there may be a difference in degree or severity) between marginalizing certain people in society and marginalizing non-equity stakeholders in a crowdfunding or microfinance venture. In both cases, the sphere of solidarity is broken, and the marginalized group is alienated. This chapter, then, advocates for applying the concept of the sphere of solidarity in the context of microfinance and crowdfunding. Specifically, microfinance and crowdfunding ventures should find creative and meaningful ways to recognize their supportive communities of non-equity supporters by showing reciprocal appreciation and loyalty, regardless of the supporters’ lack of formal legal rights.

5.4 CONCLUSION In this chapter, we address a gap in the theory and practice of strategic management and corporate governance in relation to non-equity stakeholders who actively support and contribute to crowdfunding and microfinance ventures, but whose financial interests and mission-based values are disregarded in management’s pursuit of profit incentives. To address this gap, we propose the philosophical concept of recognition, and more specifically, the sphere of solidarity. Through four case studies, we highlight how the concepts of recognition and the sphere of solidarity can help reduce the acrimony created by management’s disregard of both the financial interests and the mission-based values of non-equity supporters. In lieu of acrimony, integrating the sphere of solidarity into the strategic management and corporate governance of crowdfunding and microfinance ventures reinforces unity with the venture’s supportive community and encourages the creation of long-term relationships of trust. We contend that incorporating the sphere of solidarity into strategic management and corporate governance will help managers pursue profitable, long-term strategies on behalf of shareholders and, simultaneously, recognize and sustain the long-term value of building authentic relationships with non-equity contributors. We suggest further research on the applicability of recognition and the sphere of solidarity in a variety of contexts both within and outside of microfinance and crowdfunding. Apart from the case study method we used in this chapter for the purpose of inductive theory building, future research might adopt an empirical approach to test the concept of recognition and the sphere of solidarity statistically and deductively.

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6

Why Do Bank Customers Adopt FinTech Solution The Case of India Neha IILM University Gurugram, Haryana, India

Mamta Sharma Chandigarh Group of Colleges, India

Thomas Monteiro K R Mangalam University, India

6.1 INTRODUCTION FinTech, as the name suggests, is the blend of finance and technology. Technology has always played a predominant role in every field. As far as financial sector is concerned, technology has influenced it tremendously. Consider, for illustration, introduction of credit card, i.e., the Central Bank of India introduced the first credit card in the 1980s in India (Anwar et al., 2020), and HSBC Bank introduced the first ATM in India in the year of 1987 (Goldstein et al., 2019). Hence, the stride at which new technologies are proven and introduced into finance is more rapidly than ever before. Nevertheless, even more significantly, this FinTech revolution is distinctive in that drastic change is happening from outside the financial industry, as young start-up firms and large multinational technology firms are endeavoring to disrupt the incumbents, presenting new products and technologies and providing a substantial new measure of competition. Just step into a practitioner-oriented FinTech conference: with its audience composed largely of people in their twenties from Silicon Valley and Silicon Alley, there is clearly something new in the air (Goldstein, 2019). Born in context to economic needs, the arena of finance has been driven by an integral stimulus to absorb valuable elements, containing technological innovation, to benefit its growth. The development of India’s financial sector has been complemented by endless technological progress. After financial informatization and electronization, Internet Finance arose to grow rapidly in India and gained widespread recognition since 2017. In India, we have seen a drastic increase in the FinTech market. Walmart Inc’s PhonePe app accounts for 47% of online money transactions in India, while domestic Paytm accounts for 10%. Alphabet DOI: 10.1201/9781003125204-6

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Inc.’s Google Pay, which controls 37% of the market, is using its search expertise to influence customers‘ choice of bank deposits (Niti Aayog Report, 2021). India’s civic digital infrastructure, specifically Unified Payments Interface has successfully demonstrated how to challenge conventional incumbents. As pointed out in the opening section, Unified Payment Interface (UPI) transactions measured have surpassed Rs 4 trillion in value (April 2021). Aadhaar validations have passed 55 trillion (April 2021). Finally, India is at the tip of operationalizing its own digital platform. These indices exhibit India has the technology heap to fully facilitate technology (Niti Aayog Report, 2021). There are more than 2100 FinTech firms in India, out of which more than 67 percent have been commenced in the last five years. India’s FinTech segment has also grasped exponential development in funding; investments worth more than US$ 8 billion were received across various stages of investment in 2021. According to Amitabh Kant (CEO, NITI Aayog), the Indian FinTech sector has a cumulative funding of over US$ 27.6 billion and is anticipated to be valued at over US$ 150 billion by 2025. Currently, worldwide, there are 187 FinTech unicorns of which 18 Unicorns are in India. These are Policy Bazaar, Paytm, RazorPay, BillDesk, Zoho, Zerodha, Cred, Chargebee, Digit, Groww, Pine Labs, Zeta, BharatPe, CoinDCX, OfBusiness, Upstox, Acko and Slice. Slice was the latest entry in the FinTech unicorn list, hovering US$ 220 million in a series-B round (Naina Bhardwaj, 2021). In the present scenario, FinTech is the requirement of many developing economies such as China, India, Korea, UK (Kim et al., 2016). In India, the FinTech market is developing and has a promising future (refer Table 6.1). The FinTech market of TABLE 6.1 FinTech Trends in India 2 Fintech Trends in India Particulars

Business Firm

Year

Source

Introduction of Credit card

Central Bank of India

1980

www.rbi.com

Introduction of ATM

HSBC

1987

www.rbi.com

Introduction of Banking website Introduction of Electronic TradingGeojit Securities was the first to go online

ICICI Bank National Stock Exchange (NSE)

1998 2000

www.rbi.com www.sebi.com

Paypal Makes its debut

PayPal Payments Private Limited

2017

www.paypal.com

Introduction of Google Pay – Google launched a payments app in India known as Tez, utilizing the Unified Payments Interface (UPI).

Google

2017

www.google.com

Introduction of Crypto currency – Central Bank Digital Currency (CBDC)

Central Bank

2021

www.rbi.com

Why Do Bank Customers Adopt FinTech Solution

71

India has gained much attention from the banking industry, policymakers, and researchers. Hence, it is a subject that requires more discussion in conferences and business forums. However, not much empirical research has been conducted in this context to learn about FinTech and its application in banking sectors related to the current Indian economy scenario. The reasons that lead bank customers to choose FinTech solutions are now a subject of great interest, and further study is merited in this field. The researchers have taken into account a number of variables that affected customers’ decisions to employ FinTech services. Surveys among bank clients were used to gather the information for this study. The study produced some really intriguing findings that will be highly useful in formulating regulations for Indian officials, bankers, and scholars working in the banking sector. While the drastic growth of FinTech solutions in India is extensively described and studied, less is done on the FinTech customer behavior to the best of our knowledge. The research inquiry here consequently address is: what are the factors that impact the customer decision of using FinTech Services? The researchers collect data through surveys among bank customers to identify elements of the answer to the question.

6.2 LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT By using FinTech, banks will extend the scope of various services to their end customers; consequently, FinTech is not a plain combination of financial services and information technology, but a technology solicitation for conventional services to extend the scope (Lee & Teo, 2015; Nafis, 2022). For clients, FinTech creates many new revel in opportunities and facilitates customers greater handy to transact (Chen, You, & Chang, 2021). Undeniably, FinTech can support banking services on mobile devices, viz mobile phones and tablets. Thus, consumers can custom banking services everywhere, as a replacement for having to go to the customary counter (Shaikh, 2016). Hence, it can be believed that FinTech services play a very significant role in the banking sector (Shaikh, 2016; Chen, You, & Chang, 2021), and all together bring several customer benefits (Demertzis, Silvia, & Guntram, 2018). To excel in FinTech services in the banking segment, it is imperative to contemplate factors that affect customers’ aim to adopt FinTech services. As a result, banks will enlarge market share and advance operational efficiency. Regarding factors affecting customers’ intention to use FinTech services, focusing on four factors: perceived usefulness (PU), perceived ease of use (PEU), customer trust (CU), and social influence (SI).

6.2.1 PERCEIVED USEFULNESS (PU) (IUF) SERVICES

AND INTENTION TO

USE FINTECH

The utility of the service can considerably influence the customer’s intention to use FinTech services. FinTech services provide consumers with several pros, primarily

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reduced cost, time, and effort to realize financial transactions (Ozili, 2018; Nawayseh & Mohammad, 2020). Furthermore, FinTech service can offer an added remarkable experience for clients, so overcoming the limitations of customary banking services. Hence, it can be assumed that the perception of usefulness can positively influence customers’ intention to practice FinTech services. This consequence is also found in various experiential studies, viz (Oladapo, Hamoudah, Alam, Olaopa, & Muda, 2021; Lee & Kim, 2020; Chen & Sivakumar, 2021). Based on this basis, the following hypotheses are proposed: H1: Perception of usefulness (PU) has a positive impact on the intention to use FinTech (IUF) services.

6.2.2 PERCEIVED EASE SERVICES

OF

USE (PEU)

AND INTENTION TO

USE FINTECH (IUF)

The sensitivity of ease of use can be understood as the degree to which users feel comfortable and contented. FinTech service, if used effortlessly, can offer remarkable experiences for consumers, in that way, the individual needs of each customer can meet. On the contrary, if the use of FinTech services is complex, consumers will be very prone to bump into errors when using, and this can cause monetary losses for consumers. Hence, the sense of ease of use is one of the essential factors that lead to users’ intention to use services (Bartlett, Stanton, & Wallace, 2022; Nasfi, Yunimar, & Prawira, 2022). Therefore, the ease of use can positively affect the consumer’s intent to use FinTech services. This outcome is also found in empirical studies of Roh, Yang, Xiao, & Park, 2022; Lagna & Ravishankar, 2022; Abbasi, Sandran, Ganesan, & Iranmanesh, 2022). So, the research hypothesis proposed by the authors is as follows: H2: The perception of ease of use (PEU) has a positive impact on the intention to use FinTech (IUF) services.

6.2.3 THE TRUST OF CUSTOMERS (CU) AND INTENTION TO USE FINTECH (IUF) SERVICES Trust is considered a decisive factor for technology adoption, particularly those technologies intended for financial transactions (Kaushik et al., 2015). In an extremely competitive financial industry, there is a prominence on trust to shape strong relationships with customers (Stewart & Jürjens, 2018). Trust in FinTech services means that customers have optimism in the belief, reliability, and munificence of these applications (Kewell & Michael Ward, 2017; Müller & Kerényi, 2019; Nangin, Barus, & Wahyoedi, 2020). In contrast, since the substituting cost to customary financial systems is high, trust is regarded as crucial for financial transactions (Nawayseh & Mohammad, 2020; Pratama, 2021). Certainly, the consequence of trust in such indeterminate and precarious situations is reduced risk and

Why Do Bank Customers Adopt FinTech Solution

73

thus positive aims toward using new technologies (Dinev & Hu, 2007; Garrouch, 2021). Consequently, the following hypothesis is proposed: H3: Customer trust (CU) has a positive impact on the intention to use FinTech (IUF) services.

6.2.4 SOCIAL INFLUENCE (SOI) AND INTENTION TO USE FINTECH (IUF) SERVICES Customers’ choices to use firsthand technology, chiefly in the social media era, are prejudiced mainly by the views of other people nearby them (Schneider, 2018; Woolley & Howard, 2018). Family, relatives, and coworkers are all bases of favorable recommendations about new technologies that may embolden customers to adopt them (Kulviwat, Bruner & Al-Shuridah, 2009). When regulars observe that individuals in society use FinTech services, they are apt to use it (Zetsche et al., 2017). Social influence has a positive and significant impact on FinTech applications such as mobile payment and banking and intentions to use them (Isaac, Abdullah, Aldholay, & Ameen, 2019; Darmansyah et al., 2020) but this is matter to stipulations, such as earlier experience. Given the collectivistic philosophy of Indian society, the following hypotheses are suggested: H4: Social influence (SOI) has a positive impact on the intention to use FinTech (IUF) services.

6.3 METHODOLOGY The chapter emphasizes understanding FinTech in the banking sector in India. To unravel this research objective, the authors analyze FinTech development trends in the banking sector in India. To assess the quality of FinTech services at banks, the researcher conducted consideration of elements affecting customers’ intention to use FinTech services. To realize this, the researcher collected data through a survey of customers of banks in Chandigarh City, which ranks first or top performer in SDG Index among union territories. The Index for Sustainable Development Goals (SDGs) evaluates the progress on three key parameters viz social, economic, and environmental (Niti Aayog Report, November 2021). The survey period is from January 2022 to February 2022 according to the pre-designed survey questionnaire. The Sample size is 400 customers of banks. The sample size is calculated by Slovin’s formula. The total population of Chandigarh is 1.19 million. The sample size came out to 399.865 approximately 400. For analysis, the authors used multivariate regression to anticipate the research model. Before proceeding with regression analysis, the researchers conducted Cronbach’s Alpha test and Exploratory Factor Analysis (EFA) to limit the appropriate factors to lay into the research model. The research model (Figure 6.1) is constructed based on the outcomes of preceding studies and the research hypotheses that the researchers have proposed. Consequently, the dependent variable is the intention to use FinTech (IUF) service. Independent variables are perception of usefulness (PU), perceived ease of use (PEU), customer trust (CU), and social impact (SOI) (Table 6.2).

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customer trust (CU)

percepon of usefulness (PU), the intenon to use fintech (IUF) perceived ease of use (PEU)

social impact (SOI)

FIGURE 6.1 Proposed Research Model

TABLE 6.2 Variables Description in the Research Model Variables Perceived Usefulness (PU)

Perceived Ease of Use (PEU)

Customer Trust (CU)

PU1 PU2

Explanation

Source

FinTech service can meet the needs of customers Customers save a lot of time when using FinTech services

Ozili, 2018; Nawayseh & Mohammad, 2020; Oladapo, Hamoudah, Alam, Olaopa, & Muda, 2021; Lee & Kim, 2020; Chen & Sivakumar, 2021

PU3

Using FinTech services increases customer work efficiency.

PU4

Customers can access many utilities attached when using FinTech service. The operations performed in FinTech service are quite simple for customers.

PEU1

PEU2

Instructions on the FinTech service system are clear and easy to understand.

PEU3

Customers can interact with FinTech service system everywhere.

CU1

FinTech service has good information security ability. FinTech service is provided by reputable units only.

CU2 CU3

Customers feel confident when using FinTech services.

Bartlett, Stanton & Wallace, 2022; Nasfi, Yunimar & Prawira, 2022; Roh, Yang, Xiao & Park, 2022; Lagna & Ravishankar, 2022; Abbasi, Sandran, Ganesan & Iranmanesh, 2022

Kaushik et al., 2015; Stewart & Jürjens, 2018; Kewell & Michael Ward, 2017; Müller & Kerényi, 2019; Nangin, Barus, & Wahyoedi, 2020; Nawayseh & Mohammad, 2020; Pratama, 2021; Dinev & Hu, 2007; Garrouch, 2021

Why Do Bank Customers Adopt FinTech Solution

75

TABLE 6.2 (Continued) Variables Description in the Research Model Variables Social Impact (SOI)

Explanation

Source

SOI1

Neighbors (such as relatives, friends, colleagues… ) often use FinTech services.

SOI2

Customer’s work/study environment supports FinTech services. FinTech service is in line with the development trend of society.

Schneider, 2018; Woolley & Howard, 2018; Kulviwat, Bruner & Al-Shuridah, 2009; Zetsche et al., 2017; Isaac, Abdullah, Aldholay, & Ameen, 2019; Darmansyah et al., 2020

SOI3

6.4 EMPIRICAL RESULTS To verify the suitable variables that need to be included in the present research model, Cronbach’s alpha test (which is required to check the internal consistency) and EFA (which is required to trace the relative factors among the selected variable) have been implied. The outcome of the results is shown in Table 6.3. In Table 6.3, there are independent variables that can impact the intention of customers to use the FinTech services. These are Customer Trust, Perceived Ease of Use, and Social Impact. On these factors, Multiple Regression analysis has been applied to check the impact and set up a research Model.

TABLE 6.3 Outcome of Regression Model Variables

Cronbach Alpha

Component 1

Perceived Usefulness (PU)

PU1

0.901

PU2 PU3 PEU1 PEU2

Customer Trust (CU)

CU1 CU2

SOI3

0.881

0.886 0.882 0.87

0.843

CU3 SOI1 SOI2

4

0.766 0.894

PEU3

Social Impact (SOI)

3

0.878 0.751

PU4 Perceived Ease of Use (PEU)

2

0.895 0.878 0.881

0.791

0.821 0.798 0.771

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Artificial Intelligence, Fintech, and Financial Inclusion

TABLE 6.4 Estimation of Regression Coefficients Dependent Variable (Intention of Customers to Use the FinTech Services) (ICUF) Variables

Beta

Sig.

Perceived Usefulness (PU) Perceived Ease of Use (PEU)

0.457*** 0.459***

0.0004 0.0034

Customer Trust (CU)

0.558***

0.0487

Social Impact (SOI) N

0.398***

0.0084 400

Anova

0.0004***

R Square

85%

ANOVA results explain that the outcomes of estimating the model of research are significant at 5% level of significance. The value of R square is 85%, which shows 85% of variation in the intention of customers to use FinTech services due to the selected independent variables. With the result of above Table 6.4, the following equation can be obtained. A model could be drawn that has been shown in Figure 6.2. ICUF = 0.667CU + 0.459PU + 0.397SOI + 0.557PEU

FIGURE 6.2 Estimation of Outcomes of the Regression Coefficients.

Why Do Bank Customers Adopt FinTech Solution

77

6.4.1 EFFECT OF PERCEIVED USEFULNESS OF FINTECH SERVICES (PU) ON INTENTION OF CUSTOMERS TO USE FINTECH (ICUF) SERVICES

THE

The outcome of the analysis depicts that the perception of usefulness of FinTech services positively affects the intention of customers to use FinTech Services. The results are also steady with the earlier studies of Wonglimpiyarat (Lee, 2017; Oladapo, Hamoudah, Alam, Olaopa, & Muda, 2021; Lee & Kim, 2020; Chen & Sivakumar, 2021). It shows that Simple usage of FinTech services gives a sign that innovation is planned not to make it hard for the wearer, however, the utilization of innovation makes it simpler for somebody to complete their work. All in all, somebody who utilizes innovation will work simpler than somebody who utilizes a manual framework (Sijabut, Huttabullu, and Sihombing, 2019).

6.4.2 EFFECT OF PERCEIVED EASE OF USE OF FINTECH SERVICES (PEU) INTENTION OF CUSTOMERS TO USE FINTECH (ICUF) SERVICES

ON THE

The Study revealed the positive impact of Perceived Ease of Use of FinTech Services on the intention of customers to use FinTech services. This is additionally very reliable with the attributes of moderately new innovations in financial markets, for example, FinTech. Since, while planning to utilize the assistance, clients frequently pose the inquiry like “Is it simple to utilize or not?”. In like manner, if the FinTech administration is agreeable and simple to utilize, clients won’t hold back while making a goal to utilize this help. The positive effect of the apparent convenience on FinTech aims is additionally steady with past perceptions of Roh, Yang, Xiao & Park, 2022; Lagna & Ravishankar, 2022; and Abbasi, Sandran, Ganesan & Iranmanesh, 2022.

6.4.3 EFFECT OF CUSTOMER TRUST IN FINTECH SERVICES (CU) ON THE INTENTION OF CUSTOMERS TO USE FINTECH (ICUF) SERVICES The study has also shown the positive impact of Customer trust on the intention to use Fintech services. This variable’s result is also consistent with the earlier researches by Kesharwani and Bisht (2012), Hanafizadeh et al. (2014), Hu et al. (2019); Kewell & Michael Ward, (2017), Müller & Kerényi, (2019); and Nangin, Barus, & Wahyoedi, (2020). In Indian Financial market, we have seen many banks providing FinTech services to their customers. The banks that are providing many services as FinTech services create trust among the customers to use FinTech services. Social Influence (SI) Impact on the intention of customers to use FinTech (ICUF) services: The variable Social Influence has also found positive impact on the intention of customers to use FinTech (ICUF) services, because when a person see other people to use FinTech, it increases the tenderness among the customers to use these services. In the earlier studies Kim et al. (2016), Koksal (2016), and Oliveira et al. (2016); Isaac, Abdullah, Aldholay, & Ameen, (2019); Darmansyah et al. (2020) same tenderness has been seen which shows the greater impact on using FinTech services.

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6.5 CONCLUSION The results indicate that FinTech services are vital for the Indian banking sector as a developing country. The study has found the success factors that positively and significantly impact the intention of customers to use FinTech services with the help of multivariate regression. The factors which are identified are a perception of usefulness (PU), perceived ease of use (PEU), customer trust (CU), and social impact (SOI). With the help of the factors identified, banks need to pay attention to these elements more so that they can improve the usability of FinTech services more. Indian government and Banking sectors together formed some policies to increase the usability of FinTech services. The authors offer the following policy implications based on the findings of this study to foster the development of FinTech services in the banking sector in India: • Banks must continue to develop the features of FinTech services, which are largely focused on boosting the service’s usefulness for all consumer segments. At the same time, users should be able to complete transactions quickly and easily using FinTech services. • Banks must have a strategy in place to improve their image and reputation in the market. Simultaneously, banks must actively promote and spread knowledge about products and services to the general public in order to increase market share and reduce transaction risks. • Banks must deepen their partnerships with FinTech firms in order to capitalize on the latter’s technological advantages, with the goal of improving the quality of high-tech application services and providing better client experiences. Banks will be able to diversify their products and services by utilizing cutting-edge technology, boosting client access at a low cost, as a result of this collaboration. At the same time, banks should invest more, upgrade their technical infrastructure, and improve system security. • Banks must improve their training programmes for high-quality human resources. This human resource must be proficient not only in specific knowledge but also in financial services applications based on modern technology. • In addition, the authorities must complete and supplement the legal structure, process, and regulations governing FinTech activity. Building FinTech development policies, on the other hand, must be linked to monetary and economic policies. Although the research goal was met, it was nevertheless limited because it did not address a number of other aspects that could influence FinTech customer service, such as the information technology platform, consumer financial capability, and hazards associated with using the service (Alam et al. 2019).

NOTES 1 Chandigarh Tricity includes Mohali, Chandigarh, and Panchkula. 2 Table 6.1 is compiled by the authors.

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Kim, Yonghee, Jeongil Choi, Y-J. Park, and Jiyoung Yeon. “The adoption of mobile payment services for “Fintech”.” International Journal of Applied Engineering Research 11, no. 2 (2016): 1058–1061. Koksal, Mehmet Haluk. “The intentions of Lebanese consumers to adopt mobile banking.” International Journal of bank marketing 34, no. 3 (2016): 327–346. Kulviwat, Songpol, Gordon C. Bruner II, and Obaid Al-Shuridah. “The role of social influence on adoption of high tech innovations: The moderating effect of public/private consumption.” Journal of Business Research 62, no. 7 (2009): 706–712. Lagna, Andrea, and M.N. Ravishankar. “Making the world a better place with FinTech research.” Information Systems Journal 32, no. 1 (2022): 61–102. Lee, David Kuo Chuen, and Ernie G.S. Teo. “Emergence of FinTech and the LASIC principles.” Journal of Financial Perspectives 3, no. 3 (2015). Lee, Jin-Myong, and Hyo-Jung Kim. “Determinants of adoption and continuance intentions toward Internet-only banks.” International Journal of Bank Marketing (2020). Lee, Sangmin. “Evaluation of mobile application in user’s perspective: case of P2P lending Apps in FinTech industry.” KSII Transactions on Internet and Information Systems (TIIS) 11, no. 2 (2017): 1105–1117. Müller, János, and Ádám Kerényi. “The need for trust and ethics in the digital age–Sunshine and shadows in the FinTech world.” Financial and Economic Review 18, no. 4 (2019): 5–34. Naina Bhardwaj. India Briefing Report, (December, 2021). Nangin, Meryl Astin, Irma Rasita Gloria Barus, and Soegeng Wahyoedi. “The effects of perceived ease of use, security, and promotion on trust and its implications on FinTech adoption.” Journal of Consumer Sciences 5, no. 2 (2020): 124–138. Nasfi, Nasfi, Yunimar Yunimar, and Adi Prawira. “The role of Fintech in Sharia rural bank West Sumatra.” International Journal of Social and Management Studies 3, no. 2 (2022): 13–19. Niti Aayog. “Annual Report 2020-21 of Niti Aayog.” (2021). Oladapo, Ibrahim Abiodun, Manal Mohammed Hamoudah, Md Mahmudul Alam, Olawale Rafiu Olaopa, and Ruhaini Muda. “Customers’ perceptions of FinTech adaptability in the Islamic banking sector: comparative study on Malaysia and Saudi Arabia.” Journal of Modelling in Management (2021). Oliveira, Tiago, Thomas, Manoj, Baptista, Goncalo, and Campos, Filipe. “Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology.” Computers in Human Behavior, 61 (2016): 404–414. Ozili, Peterson K. “Impact of digital finance on financial inclusion and stability.” Borsa Istanbul Review 18, no. 4 (2018): 329–340. Pratama, Jimmy. “Analysis of factors affecting trust on the use of FinTech (P2P lending) in Indonesia.” Jurnal Sisfokom (Sistem Informasi dan Komputer) 10, no. 1 (2021): 79–85. Roh, Taewoo, Young Soo Yang, Shufeng Xiao, and Byung Il Park. “What makes consumers trust and adopt FinTech? An empirical investigation in China.” Electronic Commerce Research (2022): 1–33. Schneider, Florian. China’s digital nationalism. Oxford University Press, 2018. Shaikh, Aijaz Ahmed. “Examining consumers’ intention, behavior, and beliefs in mobile banking adoption and continuous usage.” Jyväskylä Studies in Business and Economics 172 (2016). Sijabat, Yacobo P., Dinar, Melani Hutajulu, and Pardongan, Sihombing. “Determinasi Technology Acceptance Model Terhadap Niat Penggunaan Fintech Sebagai Alat Pembayaran (Payment).” Prosiding Seminar Nasional Fakultas Ekonomi Untidar 2019 (2019).

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Stewart, Harrison, and Jan Jürjens. “Data security and consumer trust in FinTech innovation in Germany.” Information & Computer Security (2018). Woolley, Samuel C., and Philip N. Howard, eds. Computational Propaganda: Political parties, politicians, and political manipulation on social media. Oxford University Press, 2018. Zetsche, Dirk A., Ross P. Buckley, Douglas W. Arner, and Janos N. Barberis. “From FinTech to TechFin: The regulatory challenges of data-driven finance.” NYUJL & Bus 14 (2017), 393.

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A Scientometric and Bibliometric Review of Impacts and Application of Artificial Intelligence and Fintech for Financial Inclusion Rajat Gera CMR University, India

Priyanka Chadha Amity University, India

Ashima Saxena NorthCap University, India

Saurav Dixit Peter of Great St. Petersburg Polytecchinc University, Russia

7.1 INTRODUCTION With the availability and cost of data and processing power increasing, Artificial Intelligence (AI) applications in finance are expanding in fields like asset management, algorithmic trading, credit underwriting, or Blockchain-based financial services. Artificial intelligence is penetrating and impacting all aspects of business domains such as smart city, Blockchain, IoT, deep Learning, and Fintech thereby creating a sustainable competitive advantage for the organizations. Deployment of AI to fulfill and complete financial tasks by use of robo-advisors, and digital brokers in tax management and trade decision-making, adds value by automation and improves efficiencies thereby reducing financial intermediation costs. A digital technology is an AI that banks and non-banking companies are implementing to increase access to even current clients of traditional financial institutions (Alameda, 2020; Peric, 2015). 82

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The financially disadvantaged may be able to access finance thanks to AI. By utilizing alternate data sources, such as public data, which can be utilized to manage difficulties related to information asymmetry, adverse selection, and moral hazard, AI and machine learning can ease credit risk assessments of less privileged clients. As a result, it is possible to verify the repayment capacity of clients who are financially excluded, enabling them to receive credit that is not otherwise available. AI is able to assess consumer behavior and verify borrowers’ capacity to repay loans by using alternative sources of public data, such as satellite images, company records, and data from social media like SMS and messenger services interaction data (Biallas & O’Neill, 2020). According to Biallas and O’Neill (2020), AI is thought to have enhanced loan choices, evaluated dangers to financial institutions, aided in meeting regulatory requirements, and addressed funding gaps encountered by consumers in emerging markets. Fintech is defining all aspects of banking, commerce, payments, and money by increasing the efficiency in financial markets, and banking transaction. Fintech is impacting raising, allocating, and transferring capital. Recent years have seen an acceleration of the use of AI in the economy (Agrawal, Gans, & Goldfarb, 2019; Brynjolfsson, Rock, & Syverson, 2021; Furman & Seamans, 2019), including as a general technology in the financial services sector (Biallas & O’Neill, 2020; Bredt, 2019). Recently, numerous articles on the opportunities, difficulties, and effects of AI have been published. The bibliometric factors, however, have not been assessed in prior study (Casadesus-Masanell & Ricart, 2011) for a quantitative examination of this research stream) (Junquera & Mitre, 2007). Employing bibliometric analysis about a specific research topic allows for the identification of leading authors, publications, journals, and countries in the field. Keyword analysis techniques of Word Mapping and cluster analysis using topic dendrogram allow for understanding the relationship between the authors, documents, and journals in this field (Casadesus-Masanell & Ricart, 2011). Implementation of science mapping techniques like thematic mapping leads to an understanding of the themes driving research in this field (Aria & Cuccurullo, 2017). This chapter aims to complement previous literature reviews on the subject (e.g., Bahrammirzaee, 2010; Jadhav, Hongmei, & Jenkins, 2016; Königstorfer & Thalmann, 2020; Mikalef, Pappas, Krogstie, & Giannakos, 2018) by mapping and evaluating the research domain of AI applications in Fintech for financial inclusion. This chapter aims to provide a bibliometric analysis of literature on application of AI, Fintech, and Financial Inclusion from 2018–2021 using Biblioshiny package in R Studio. The study’s research questions are as follows: Q1: What is the global trend of scientific publications on applications of AI in Fintech for financial inclusion? Q2: What is the conceptual and intellectual structure of this field’s research? Q3: What are the upcoming directions of research in this subject? This bibliometric analysis is based on 42 scientific studies that were published and extracted from scopus database on July 10, 2022. It uses the bibliometric R package and

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Biblioshiny application to map and evaluate the quantitative data of various selected articles by analyzing factors like the number of articles per author/journal and authors h and g index to identify the productivity and impact of top authors/journals in this research field; to evaluate the intellectual and conceptual structure of this research stream through Word Cloud, topic dendrogram, co-citations analysis, and thematic map; The objectives of this study are as follows: 1. To map the state of research currently being done on using AI in Fintech to promote financial inclusion. 2. To assess the intellectual and cognitive framework of research on the use of AI and Fintech for Financial Inclusion. 3. To identify future research directions on the application of AI and Fintech for financial inclusion. The next section is a review of literature of key concepts followed by a conceptual approach and description of the methodology adopted for this study. The study’s results are then presented, followed by a discussion, conclusions, and study limitations. Based on the results of this study, future research directions are then suggested.

7.2 LITERATURE REVIEW 7.2.1 FINANCIAL INCLUSION According to the United Nations (2018) and Bruhn and Love (2014), financial inclusion is the sustained provision of affordable financial services to enable the underprivileged to participate in the formal economy, primarily through formal bank accounts, with the goals of reducing poverty and promoting economic growth. It involves providing banking services in a way that makes them cheap for many members of disadvantaged groups, particularly low-income earners, according to Leeladhar (2005), as well as providing disadvantaged groups with financial services through official financial institutions. By ensuring “easiness of access, availability, and use of formal financial services to all the individuals in the economy,” according to Thorat (2007) and Sarma (2008). Financial inclusion is a policy priority in various Asian and Australian countries, according to Ozili (2020), and many of these countries have embraced the UK model of financial inclusion. According to numerous authors, financial exclusion is defined as “the factors that prevent members of societies and different socioeconomic groups from having access to the official financial system.” Leyshon and Thrift (1995) found that people have difficulty getting the necessary financial services. “The incapacity and unwillingness of groups of people in a society to be able to utilise mainstream financial services,” according to Sinclair (2001) and Carbó et al. (2005). The World Bank group views financial inclusion as a critical component in eradicating extreme poverty and fostering shared prosperity, and it plays a significant part in the World Bank’s Sustainable Development Goals (The World Bank Financial Inclusion, 2020). It’s a key objective of the financial sector reforms being

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carried out by the IMF and World Bank, especially in emerging economies like Asia, Africa, and South America (Arslanian & Fischer, 2019). Making long-term plans and preparing for unexpected emergencies is made possible by the availability of basic financial products and services, such as payments, remittance, savings, loans, and insurance, to individuals and small enterprises at reasonable prices. Financial inclusion is particularly crucial for encouraging inexpensive access to formal financial services, which can speed up overall economic growth and wellbeing, in emerging countries of Asia, Africa, and South America (Nawaz, 2018). Low-income families and small enterprises, who frequently require financial services, may have limited or no access to these services (Yin et al., 2019) The aim of financial inclusion in the Middle East and North Africa, commonly known as MENA countries, is to guarantee that low-income populations have complete access to the financial markets. Age, degree of education, income, racial makeup, gender, and marital status are just a few of the variables determining financial inclusion (Mhlanga & Denhere, 2021). To increase the number of individual borrowers on the credit market and to ensure its stability, access to credit markets is what drives financial inclusion in Europe (Ozili, 2020).

7.2.2 FINTECH Abraham Leo Bettinger first used the word “Fintech” in 1972 to describe a combination of banking knowledge and experience with computer technology. “Fintech” is defined as “traditional financial services that are offered via information technology” in the Oxford Dictionary. According to various definitions in the literature, fintech is defined as “the composition of companies or group of companies providing the modern, innovative, and financial services through technologies, (Dorfleitner et al., 2017)” and “the promotion of the use of technology to enhance the use of financial services and the promotion and growth of digital consulting (Sanicola, 2019).” Hence, Fintech or “finance technology.” might be operationalized as “the supply of financial and banking services using cutting-edge technical innovation, powered by computer programs and algorithms.” An individual or company that uses a technological platform, whether online or off, to deliver new financial services or improve the delivery of present financial services is referred to as a fintech service provider. Ecosystems of fintech companies gather a lot of data, benefit from economies of scale, and involve a variety of stakeholders (Mok & Saha, 2017). Fintech lowers or eliminates the expenses associated with financial intermediation. Companies like PayActiv18 are eliminating the middlemen in the payday loan business, which reduces borrowing prices by about 90%, (Das, 2019). Fintech can develop better credit models that so that FSPs can better segment and target customers who are excluded from access to formal finance. It allows lenders to offer services to the otherwise financially excluded and underserved segments by innovative approaches like using friendship networks in peer-lending programs (Lin, Prabhala, and Viswanathan, 2013). Wei, Yildirim, Bulte, & Dellarocas (2016) identified good clients using social media interactions. The automated administration of investments and

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portfolio management provided by robo-advisors increases the accessibility of financial advisory services (Belanche, 2019). 4.0 Industry Technology like artificial intelligence, machine learning, cognitive computing, and distributed ledgers can help both established businesses and new competitors acquire a competitive edge (Lopes & Pereira, 2019).

7.2.3 ARTIFICIAL INTELLIGENCE (AI) A system that can solve issues or coming to logical conclusions in order to attain goals in any real-world setting is referred to as AI. AI is defined as “the theory and development of computer systems able to do tasks that historically have required human intellect” by the Financial Stability Board (FSB), a global organization established by the G-20. “Audio processing, knowledge representation, speech-totext, deep learning, expert systems, natural language processing, machine learning (ML), robotics, and symbolic logic are some examples of AI technologies that can be used in the Fintech sector to enhance financial inclusion” (Paul, 2019). AI is faster and less expensive than human agents at providing a borrower assessment that is more accurate. For example, TymeBank in South Africa, which is a “completely digital bank,” uses AI to communicate with its clients online and through kiosks, call centers, and branches through AI-powered Chabot on the app that responds to inquiries about financial management and gives users access to their credit reports (Mhalinga, 2019). Leo, a banking Chabot from Nigeria’s United Bank for Africa (UBA), helps users with a range of services including bill payments, airtime purchases, money transfers, and account balance checks (mTransfersHQ, 2018). Similarly, Konfio, a Mexican company that offers online financial services for SMEs, disburses loans to small and midsize businesses in around 24 hours as opposed to the months that traditional banks require. Konfio uses powerful processing tools, artificial intelligence, data science, and alternative data sources to manage large amounts of data fast and analyze risk cases more efficiently (Mhalinga, 2019). AI can lessen knowledge asymmetry between the FSP and potential clients, enabling the extension of loans and other financial services to microbusinesses or individuals who traditional banks are unable to support owing to the lack of collateral (How et al., 2020). AI can be used for calculating different outcomes and evaluating hypothetical what-if scenarios thus enabling provision of financial services to potentially high risk customers “AI has a strong influence on digital financial inclusion in areas related to risk detection, measurement, and management, addressing the problem of information asymmetry, availing customer support and helpdesk through Chabot’s, fraud detection, and cyber security,” according to the Association of Computing Machinery (2019) and Mhlanga (2020).

7.2.4 CONCEPTUAL STRUCTURE AI and big data technology can be employed to analyze and classify the data generated by users. By combining data on customers’ consumption preferences, financial status, and behavior patterns, customers’ needs can be accurately targeted and predicted, and relevant consumer profiles can be developed. Accurate positioning can

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Smart Marketing Fintech or Financial Technologies AI (ML, NLP, Expert systems, AR/VR, robotics,

Lower costs of intermediation ,better credit appraisal, affordable access to financial services, access to digital financial services through mobile phones, mobile apps, effective prediction of default risk,

Financial inclusion Outcomes

Financial Inclusion of underserved and unsaved populations

FIGURE 7.1 Conceptual Model of Application of AI in Fintech for Financial Inclusion.

enhance the effectiveness of marketing, compared to traditional marketing. An extensive framework for the use of AI in customized marketing was proposed by Kumar et al. (2019). Organizations can utilize AI technology to assess consumer data and offer customized goods and services. Managers can use AI to help them continue to learn and enhance their value proposition to clients. Marketers can use AI for relevant market segmentation, more accurate prediction of consumers’ needs and preferences, and deployment of personalized products, communication, pricing, and delivery strategies. The conceptual approach of the application of AI in fintech for financial inclusion is depicted in Figure 7.1 wherein the adoption of financial technologies like AI by organizations like banks can be leveraged for smart marketing to lower costs of service delivery and disintermediation, more effective segmentation and targeting of potential customers, better risk management for credit appraisal and lending decisions, better prediction modeling of credit default, customized and personalized marketing approaches which can lead to effective financial inclusion of sections of society which are underserved or unserved.

7.3 METHODOLOGY Bibliometrics is the process of applying quantitative methods and analytical tools to bibliographic data (Pritchard, 1969). Using quantitative tools to “convert scientific quality—into something real and meaningful” is the core goal of bibliometric analysis, according to Wallin (2005). These resources are not influenced by the subjective opinions of the authors. Performance analysis and science mapping are two types of analysis included in the bibliometric technique (Cobo et al., 2011). Performance analysis evaluates the productivity of individuals, institutions, articles, journals, and countries while science mapping identifies the themes in a scientific field. Performance analysis is conducted through measures of constituents such as publications and their citations per year, most cited authors, most productive and cited journals, institutions of corresponding authors, and countries. The performance of various research constituents, such as

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authors, institutions, countries, and publications, is typically evaluated in reviews, so that the constituents can be profiled. Science mapping or Scientometric analysis aims to extract major themes through the pattern of commonality between the content of two articles for example by the by the degree of shared references between them (Kessler, 1963; Wallin, 2005; Weinberg, 1974). The intellectual structure of the topic can be identified through co-authorship analysis at both the individual and national levels. Techniques like citation analysis, co-citation analysis, bibliographic coupling, co-word analysis, and co-authorship analysis are used to examine intellectual exchanges and structural links between research parts. These methods show the subject’s bibliometric organization and intellectual structure when combined with network analysis (Baker, Pandey, Kumar, & Haldar, 2020; Tunger & Eulerich, 2018). The five steps of the science mapping process are study design, data collecting, data analysis, data visualization, and interpretation (Aria & Cuccurullo, 2017; Zupic & Cater, 2015). The study’s research questions were developed after a review of the literature on AI, Fintech, and financial inclusion.

7.3.1 DATA COLLECTION For this study, articles were sourced from the online Scopus database, which, according to Okoli and Schabram (2010), is a multi-disciplinary resource useful for scholars of information systems (IS) and includes works that have been indexed and ranked by both Scopus and the Institute for Scientific Information (Oakleaf, 2010). The primary keywords “Artificial Intelligence (AI),” “Fintech,” and “Financial Inclusion” were applied to the online Scopus database to extract the articles which define the domain and their relationships. The initial search for the keywords resulted in 387 articles published in Scopus database.

7.3.2 DATA ANALYSIS From the initially extracted 387 articles, 42 peer-reviewed scientific documents from literature were selected by applying exclusion and inclusion criteria. The study was limited to the years 2018–2021 because it is addressing a new and developing subject and because the analysis also aims to demonstrate a research trend from the most recent years. Inclusion criteria applied in the search process were “peer-reviewed document type,” English language, and the subject area of “Business, Management, and Accounting.” Conceptual articles, opinion-based documents, thesis, and conference papers were excluded from further analysis. After applying exclusion and inclusion criteria, 42 articles that met the inclusion and exclusion criteria were considered in final sample for full-text analysis.

7.3.3 DATA VISUALIZATION

AND INTERPRETATION

The data was analyzed by using the free and open-source statistical program software R and Biblioshiny application. In this stage, researchers conducted a descriptive and Scientometric bibliometric analysis using the R software and the

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Biblioshiny app to develop figures and tables for the analysis. As part of the results analysis, the knowledge structure was visualized using the data reduction technique. An explanation of the primary bibliometric statistics precedes the study of the bibliometric findings. The examination takes into account the authors, document and author quality indicators, data trends, journal citations, and the research countries. The next step is to thoroughly analyze each of the categories using the following elements: document type, annual scientific production, scientific sources, source growth, number of papers per author, number of papers per journal source, number of papers per country, and author’s keywords are some of the factors considered. The intellectual and conceptual structure was analyzed using the most frequently occurring words, word cloud, topic dendrogram developed for the study and co-citation network, and thematic analysis maps using Biblioshiny app.

7.4 RESULTS AND ANALYSES The following section presents the performance analysis of the sample papers selected for review.

7.4.1 CITATION STRUCTURE

AND

PUBLICATIONS TREND

Figure 7.2 shows the annual scientific yield analysis and literature trend from 2018 to 2021. The research output has shown a remarkable increasing trend from 2019 onwards with an annual growth rate in articles of 71% in the research area of AI, Fintech, and Financial Inclusion between 2018 and 2021. The year 2019 witnessed the highest average citations of 12+ (Figure 7.3) followed by a decline in average citations per year in 2020 and 2021 to around 5+ in 2020 and less than 5 in 2021. According to Figure 7.2, research output has shown a remarkable increasing trend in 2019 onwards with an annual growth rate in articles of 71% in the research area of AI, Fintech, and Financial Inclusion.2019 had the highest average citations

FIGURE 7.2 Annual Scientific Production.

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FIGURE 7.3 Average Article Citation per Year.

of 12+ in this field (Figure 7.2). Average citations per year were highest in 2019 while annual scientific production peaked in 2020 but has shown a declining trend from 2020 onwards which points to the emerging nature of the subject with fluctuating interest and variable quality of the articles being published in this period.

7.4.2 JOURNALS Most productive journal in this subject in Scopus database was “International Journal of Scientific and Technology” (Figure 7.4) with three published articles in 2018–2021 while the top cited Journal is “Management Science.” Most cited journals were “Management Science” and “Production and Operations management” with 28 local citations each followed by “Journal of Business research (JBR)” (26 local citations), “Journal of Finance (JF)” (18), Financial research letters (16), and “Information systems research” (13) (Figure 7.5). Top three ranked journals according to the number of documents published are “International journal of scientific and technology research” (3 publications), “Electronic commerce research and applications” and “European business organization law review” with two publications each (Figure 7.4). Bradford’s law of scattering or the Bradford distribution shows how articles published on a particular subject are scattered where few of the sources represent most of the relevant information about a topic, while most sources have only a few relevant articles (Bradford, 1934). The seven core journals in this domain as per Bradford’s Law are as follows: “International journal of scientific and technology research” (IJSTR), “Electronic commerce research and applications” (ECRA) and “European business organization law review” (EBOLR), Journal of property investment and finance (JPIF), Strategic Change (SC), Technology forecasting and Social change (TFSC) and WSEAS Transactions on business. IJSTR is an open-access worldwide journal with a multi-disciplinary focus on new research, development, and their applications. ECRA is a multi-disciplinary publication on the evolving e-commerce environment. The goal of EBOR is to

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FIGURE 7.4 Most Relevant Sources.

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FIGURE 7.5 Most Local Cited Sources.

advance scholarly discussion on all kinds of businesses across the European Union on “topics of corporate laws, firm theory, capital market theory, and other related legal matters.” To promote discussion and debate on “property valuation and investment, property management, and decision-making in all property markets,” JPIF was established. Strategic Change is an inter-disciplinary journal whose motivation is to enable start-ups and small enterprises, make decisions, and enhance business performance. Technology forecasting and social change is a trans-disciplinary journal that publishes articles on “methodology and practice of technological forecasting and future studies as planning tools as they interrelate with social, environmental and technological factors.” WSEAS Transactions on business is a multi-disciplinary journal involved with the international dimensions of “business, economics, finance, history, law, marketing, management, political science, and related areas.” The most impactful journal based on number of citations is “Management science” followed by “Production and operations management” and Journal of Business Research (JBR) (Figure 7.5) based on number of local citations. Management science publishes cross disciplinary and multi-disciplinary scientific research on all aspects of theory and practice of management from both normative and prescriptive perspectives. “Production and Operations Management is a leading journal which publishes scientific research on operations management in manufacturing and services.” “JBR is a multi-disciplinary journal with aim of theoretical and empirical advancement in various disciplines of buyer behavior, finance, organizational theory and behavior, marketing, risk and insurance and international business.” The impactful journals in this subject are either cross-functional and multi-disciplinary journals with focus on application of theory and practice of management, business decisions processes and activities or disciplinary journals in financial research, operations management, supply chain management, and information systems. The cross-functional and interdisciplinary subject of “AI applications in Fintech for financial inclusion” is a relatively novel field of study with scholars adopting varied theoretical perspectives to understand the numerous applications and its implications for financial inclusion.

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7.4.3 MOST RELEVANT AUTHORS Most impactful authors in this field of research are identified through their h and g index (Figures 7.6 and 7.7). The h index is a measure of author’s productivity and citation impact of their publications and is based on authors most cited papers and the number of citations according to them in other publications (Hirsh, 2005). The g index is an author-level metric given in 2006 by Leo Egghe and is the unique largest number received by the top g articles with g2 citations. Hence a g index of 10 indicates that the top 10 publications of an author have been cited at least 100 times. Ashta, Khan, and Rabbani are one of the most impactful authors with h index and g index of 2 each (Figures 7.6 and 7.7). Dr Ashta A. is a professor in Université Bourgogne Franche-Comté, Dijon, France, and his co-authored publication in the

FIGURE 7.6 Author Impact by H Index.

FIGURE 7.7 Author Local Impact by G Index.

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journal “Strategic Initiative” is a qualitative study of experiences and perspectives on AI adoption by incumbents and challengers (Fintech start-ups) and identifies the opportunities and risks of financial technologies for banking, investments, and microfinance. They are the leading contributors to the on-going debate on whether AI and Fintech primarily benefits existing businesses or drives new business models and whether AI creates competitive advantage for incumbents or challenger startups driven by financial technologies. Rabbani, M.R., a professor at Kingdom University, Bahrain and his co-author, Khan, S., at University College of Bahrain with h and g index of 2 each, in their research paper have classified research on Islamic Fintech into Islamic Fintech, Islamic Financial technology opportunities and challenges and Cryptocurrency/ Blockchain Sharia compliance and law/regulation. Their study contributes to the discussion on role of Islamic Fintech and how Islamic Fintech organizations can partner with Islamic Financial Institutions (IFI’s) leading to their increased efficiency, transparency, and customer satisfaction. Scholars with h and g index of 1 are A Basha S, Abu Daqar M A, Abuzayad B M, Arqawi S, Babzide A A, Barton M E, Belanche D, Caglar U, Casalo L V, Chan Y J, Cheah S M., Chen, L, Coetze, Z, Coreja, Dahl A J, Das S R. Gomber P., et al. art titled “On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services” published in 2018 has the highest number of citations (760) point out “how the fintech sector will evolve over time, and how IS researchers can contribute to new knowledge in domain of technology innovation, process disruption, and services transformation.” In their review study on the factors affecting and performance of online peer-topeer lending in emerging and developing markets, A. Basha, S., Professor at Qatar University, Doha, Qatar, and Abuzayed B M highlight the “shift from using logit and survival analysis methods to examine funding success and default predictions, towards applying artificial intelligence and are major contributors to the contentious debate on adoption of a self-regulatory approach or stricter financial institute-based regulations.” Costello A M, University of Michigan, United States along with other authors in their study, emphasize the growing prominence of AI-based lending models in diminishing human role in evaluating creditworthiness of borrowers and demonstrate through a randomized, controlled experiment that augmenting the machine with discretion is useful in some cases only. In his article, Das, S.R., a professor at Santa Clara University in the United States, discusses the development of the financial technology (Fintech) industry as well as the various financial paradigms and technologies that underpin it. They assert that the opportunity for data-driven models to take the place of theoretically derived models is offered by machine learning (ML). “AI will manage intelligent payments and process payment functions while learning from client behaviour” (IPM). Using new personal financial management apps that leverage contextual awareness, evaluate spending habits, and internet footprints to produce individualized advice, AI will assist users in making daily financial decisions and monitoring expenditure. Customer data will be automatically mined by algorithms, and cross-selling of financial items will be done. Belanche, D. et al., University of Zaragoza, Zaragoza, Spain, To better understand the use of robo-advisors by a variety of potential consumers and the modifying impact of

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personal and sociodemographic variables, offer a research approach (familiarity with robots, age, gender, and country). The strategic ramifications of fintech on South African retail banks were recognized by Coetzee, J., University of the Free State, South Africa in their qualitative study. In their study titled “Identifying Startups Business Opportunities from UGC on Twitter Chatting: An Exploratory Analysis,” Correia M. B., Coordinator Professor at the School of Management, Hospitality and Tourism (ESGHT) of the University of the Algarve (Portugal), and other authors identified investment opportunities for investors in Indian start-ups through, a threestage data mining method consisting of a Latent Dirichlet Allocation (LDA) model, text analysis, and sentiment analysis (TA). Thus, the research domain on this subject is mostly influenced by exploratory and qualitative research methods in key areas of, for example, adoption of robo-advisory services, financial technologies disruption of traditional banking, investment and microfinance, opportunities and risks of financial technologies for banking, opportunities for Islamic fintech, key factors of Indian startup ecosystems. The high-impact empirical studies are aimed at evaluating the impact of AI on organizational decision-making and performance and how regulation financial services will be effected.

7.4.4 MOST INFLUENTIAL ARTICLES The top ten cited articles in this study have a technology-centric focus and are mostly conceptual and review articles published in inter-disciplinary domains of industrial management, production and operations management, information systems, social change, services digitalization, financial technologies, and services. As per Table 7.1, the three most cited articles on this subject: in their quantitative empirical paper “Artificial Intelligence in Fintech: Understanding robo-advisors adoption among customers,” Belanche, Casaló, & Flavián (2019) empirically propose and evaluate a research framework to of robo-advisor adoption by potential customers and predict the personal and sociodemographic moderator variables (familiarity with robots, age, gender, and country). The second most cited paper, “Research in OM and IS Interface,” by Kumar, Mookerjee, & Shubham (2018), is a review paper on the intersection of operations management and information systems and how “Fintech,” driven by mobile digital payment systems and distributed ledger technologies through blockchains, is favorably disrupting the financial ecosystem, particularly with regard to financial inclusion. The third-most-cited paper is titled “Diversification in the Age of the Fourth Industrial Revolution: The Role of Artificial Intelligence, Green Bonds, and Cryptocurrencies” and was authored by Huynh, Erik Hille, and Muhammad Ali Nasir“ in 2020. It uses econometric analysis to show the specific contribution that AI and robotics stocks make to portfolio diversification. The fourth most referenced study, “Relationship Banking and Information Technology: The Role of Artificial Intelligence and Fintech,” by Jaki, M., and Marin, M., assesses how distance, AI, and behavioral biases contribute to relationship banking’s competitive advantages. The 10 most cited articles (Table 7.1) are predominantly exploratory and review papers that propose a conceptual framework for future research based on qualitative research methods. Some of the topics explored are probable impact of AI and Fintech

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TABLE 7.1 Most Cited Articles Sr No. 1 2

Title “Artificial intelligence in Fintech: understanding robo-advisors adoption among customers” “Research in operations management and information systems interface”

Total Citations

TC Per Year

Normalized TC

90

22.5

2.1687

57

11.4

2.3425

3

“Diversification in the age of the 4th industrial revolution: the role of artificial intelligence, green bonds and cryptocurrencies”

51

17

4.4156

4

“Relationship banking and information technology: the role of artificial intelligence and Fintech” “The evolution of the financial technology ecosystem: an introduction and agenda for future research on disruptive innovations in ecosystems” “An artificial intelligence and NLP based Islamic Fintech model combining zakat and qardh-al-hasan for countering the adverse impact of Covid 19 on SME”

51

12.75

1.2289

40

13.333

3.4632

5

6

27

9

2.3377

7

“The future of Fintech”

24

6

0.5783

8

“Digital sterilization value co-creation framework for AI services: a research agenda for digital transformation in financial service ecosystems” “How should we understand the digital economy in Asia? Critical assessment and research agenda”

22

11

5.1562

22

7.333

1.9048

“Access to finance for artificial intelligence regulation in the financial services industry”

12

4

1.039

9 10

on financial inclusion, relationship banking, Islamic fintech, financial services ecosystem, and value co-creation in digital economy. For example, the paper titled “Digital sterilization value co-creation framework for AI services: a research agenda for digital transformation in financial service ecosystems” by Payne et al. (2021) proposes “a theoretical framework for digital sterilization research for AI services within the financial services industry.” Due to the emergent status of research in this field most impactful papers of exploratory nature and review of literature for proposed conceptual frameworks to guide future research in this field.

7.4.5 COUNTRIES Countries of most cited corresponding authors in the selected article of this study are geographically concentrated in developed countries like USA, Spain, and Slovenia (Figure 7.8). The countries with the highest number of citations are the USA (119) followed by Spain (92), Slovenia (51), Italy (22), the UK (19), France (12), S Africa (11), Hungary (5), Singapore (5), China (1), and India (1) which is contrary to

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FIGURE 7.8 Most Cited Countries.

expectations as developed countries have more citations and impact in this subject as compared to developing economies. The developing countries in top 16 cited countries of corresponding authors of papers (Figure 7.8) are China, India, and South Africa which are predominantly BRICS (Brazil, Russia, India, China, S Africa) countries where accelerated adoption of digital technologies has unlocked huge potential in the realm of digital financial inclusion (RBI report 2021) or developed countries which have implemented innovative and strategic policy and regulatory measures towards financial inclusion and financial capability. For example, the USA which has the highest citations for a country, has taken various policy measures like “Connect ALL Initiative” to provide safe and affordable financial services to 7% of American households who are unbanked and another 20% who are under banked (obamawhitehouse.archices, 2016). Spain, the second highest cited country, has 95% financial inclusion but faces the challenge of provision of basic financial services to the “unpopulated rural Spain” and has taken various measures for the same (Santander.com). Thus, countries whose governments have a strategic and innovative approach are leading contributors to research on digital financial inclusion.

7.5 SCIENTOMETRIC ANALYSIS 7.5.1 AUTHORS KEYWORDS This section documents the keywords used by authors on the relationship of AI with Fintech and Financial Inclusion to identify research gaps and determine the research trends and discussions on the subject. The word cloud presents the authors’ keywords and consists of the most frequent keywords used by the authors and their trends over time (Secinaro et al., 2021). Wordcloud highlights the most frequently occurring authors keywords and we find that key terms such as financial technologies (bitcoins, electronic money, hedging instruments, intelligent robots, mobile

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FIGURE 7.9 Word Cloud.

payment, and industry 4.0), digitalization (digital economy, e-commerce), innovation (disruptive innovations, innovation performance, and entrepreneurship), and sustainability (economic sustainability, green manufacturing, environmental challenges) are the most frequently occurring keywords. The other themes which can be inferred from the WordCloud are Financial markets, financial institutions, business process, behavioral characteristics, and global value chain. Fintech, disruptive innovations, and digitalization are the most predominant themes according to the WordCloud (Figure 7.9). Thus, this research field is emerging at the intersection of Fintech, disruptive innovations, and digitalization of business.

7.5.2 CO-CITATION NETWORK The bibliometric method known as ACA has several applications. When a reference to the work of two authors, let’s say A and B, additionally mentions some of the work of B (which may have appeared in any journal), this is known as a co-citation. In other words, the proportion of references mentioned by A that are also cited by references cited by B indicates how frequently A and B are cited together. In addition to defining subfields within a discipline and explaining the ideational relationships between them, researchers have used it to examine a variety of phenomena (McCain, 1983; Culnan, 1986; Culnan, O’Reilly, & Chatman, 1990; Bayer, Smart, & McLaughlin, 1990; McCain, 1990; White & McCain, 1998; Sircar, Nerur, & Mahapatra, 2001; Ponzi, 2002). For instance, Cottrill, Rogers, & Mills (1989) used the methodology to analyze research traditions, while Sircar et al. (2001) examined conceptual differences between influential authors in software development to address the question of whether object orientation constitutes a revolutionary change. Because there are neither factual nor objective ways to recognize nor explain paradigms and paradigm shifts, some academics have recommended using citation patterns instead (Kuhn, 1970; Crane, 1972; Weber, 1987). ACA is used to understand how an academic topic develops since it can spot patterns of connectivity between authors based on how frequently they are referenced together (White & McCain, 1998) The same research stream’s authors typically cite one another and use the same data sources. Furthermore, other authors who write about

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intellectually related topics are likely to regularly co-cite (i.e., cite both of their works) them. This process results in a complex web of connections between authors that are made possible by the production and sharing of knowledge. Citations of influential authors thus serve as a foundation for analyzing the intricate patterns of relationships that exist between them and for tracking the evolution of intellectual currents over time. As a result, the frequency of co-citations can be used to determine how close writers are to one another. The author’s name should be understood to be merely a label for the main conceptual theme or idea that the author represents (Culnan, 1986). The frequency and general distribution of the cocitations that authors share determine the ideational interactions shown by the intellectual map (McCain, 1990; White & McCain, 1998). The co-citation network of authors in this study shows four clusters of authors networks based on their co-citations of each other’s articles (Figure 7.10). The four knowledge domains identified from ACA in this field are as follows. The network of scholars comprising Ashta A, et al., Wang, H, Lee M., Chen, J, Christensen, C.M., Moore, T, Gomber P et al., Wang C along with co-authors Y Sun, T Lu, Y Li, H Fu, J. Dong. Their study creates a brand-new transfer learning algorithm called TransBoost that improves financial inclusion. “Explores the wider implementation of financial technology (fintech) to minimize the trade finance gap for micro, small, and medium-sized firms in member countries of the Central Asia Regional Cooperation (CAREC),” according to Lee M. et al. brief.‘s for the Asian Development Bank in 2019. In their 2018 article titled “On the fintech revolution: Understanding the forces of innovation, disruption, and transformation in financial services,” Gomber P., et al., (2018) examine the effects of Fintech innovations, disruptions, and transformations for both existing players and new competitors. In their research paper titled “Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance” published in 2021, Ashta A. and

FIGURE 7.10 Co-Citation Network of Authors.

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Hermann H. provide an overview of how AI innovations analyze big data to enable lower costs, lower risks, and higher customization, all of which would help to increase demand and economic growth. The topics comprising the intellectual domain of this network of authors include evaluation of Fintech as disruptive technologies, adoption and regulation of financial technologies in financial services, blockchain-enabled data sharing, p2p lending, Fintech: ecosystem, business models, investment decisions, and challenges. Hence, the underlying intellectual pattern is the macro and strategic implication of AI and Fintech for incumbents, its adoption, regulation, and performance. These authors have explored various aspects of economic and investment, competitive, business model, and ecosystem impact of AI and Fintech in the subject area. The articles co-cited by Huang, Y., Kumar, S., Hasan, M., Li, Liu, Q, Lin, Philippon, T, and Berger, Brynjolfsson, E. are intellectually interesting because they focus on measuring, managing, and understanding the digital economy, managing next-generation AI and the Fintech opportunity, understanding the challenges of AI in banking, evaluating borrowers in peer-to-peer lending, and evaluating the impact of financial intermediaries in p2p. The working paper “Fintech credit risk assessment for SMEs: evidence from China” by Huang Y et al. compares the bank approach to credit risk assessment with the fintech approach, which uses big data and machine learning models to assess credit risk using traditional financial data and scorecard models. Investigating the “impacts of financial knowledge on financial access through banking, microfinance, and fintech access using the Bangladesh rural population data,” Hasan et al. (2021), art “How does financial literacy impact on inclusive finance?” The “On fintech and financial inclusion” working paper by Philippon (2019) analyses how Fintech may lower financial intermediation costs but also raise new regulatory concerns. These scholars have attempted to estimate how AI and Fintech will affect the financial ecosystem, intermediaries, and financial inclusion. The co-citation network of Kim, Ji, Belanche, Jung (Kim, J.-B., Zhang, L., intellectual focus is on topics comprising robo-advisors’ adoption among customers, fraud detection using machine learning, financial innovation, and regulating robo-advisors. The research focus is robo-advisory services adoption by customers, regulation, and applications of AI in financial services like fraud detection. The scholar network of Zhao, H, Yang, Y.E, Tan, Wu, D, Jiang, Z, Feng, G, Gao, Q, Xu, D, Guo, X, H, He, J. has published on research topics of deep learning, educational crowd funding, deep reinforcement learning, borrower learning effects in p2p lending, pathway analysis using random forests classification and regression, P2P networking lending platform for credit risk assessment. These scholars’ interest is in researching the complex structure of algorithms for evaluation of unstructured data and their applications for financial inclusion for example in credit risk assessment.

7.5.3 TOPIC DENDROGRAM The hierarchical clustering-produced keyword relationships are displayed in the topic dendrogram together with the hierarchical order. The dendrogram’s vertical lines and cut make it easier to examine and analyze the various groupings. To ease future debate, it seeks to estimate the approximate number of clusters (Andrews, 2003).

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FIGURE 7.11 Topic Dendrogram.

The topic dendrogram (Figure 7.11) shows two distinct clusters comprising of keywords of financial institutions, digital financial services, financial inclusion, service delivery, and service providers in cluster one and cloud computing, financial technologies, banking industry, financial services, social media, AI, fintech companies in cluster two. Cluster one is associated with financial institutions and digital service delivery by financial service providers. While cluster 2 is associated with financial technologies like AI, cloud computing, and social media, data analysis. Within cluster 1, financial inclusion and digital financial form a sub-cluster which is due to the significant role played by digital financial services through for example mobile payments in enhancing financial inclusion. Within cluster 2, author keywords of banking industry, social media, and data analysis form a sub-cluster while AI, Fintech companies, and financial services comprise another sub-cluster. AI is adopted predominantly by fintech companies while incumbent banking industry is more inclined towards social media and data analysis. ML and future research comprise a sub-cluster due to the research opportunities in applications of ML in financial services.

7.5.4 THEMATIC MAP The KeyWords Plus field is utilized by the thematic map to identify the dominant themes in this research field. These keywords are connected by editorial specialists at Thomson Reuters with the assistance of a semi-automated algorithm and the Keywords Plus field is normalized in contrast to the authors’ keywords. Figure 7.12 depicts the thematic mapping with four distinct thematic typologies extracted from the author’s keywords of selected articles. With few external linkages and high-density niche issues, the upper-left quadrant has little bearing on the subject (low centrality). There are no themes in this

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FIGURE 7.12 Thematic Map.

quadrant. The upper right-hand quadrant represents themes that have high centrality and high relevance within the typology of motor themes. The driving research themes in this field are “banking, digital technology,” “financial services, information technology,” and “digitalization, Covid.” Digital technology is transforming banking at a rapid pace while all aspects of financial services have been impacted by information technology, especially in the post-Covid era. Covid has further accelerated the digitalization of the financial services and is a predominant mainstream theme in this research field. The pandemic has accelerated the shift to digital payments (Auer et al., 2020a) which may benefit big tech firms and their activities in finance. Countries with more stringent COVID-19 policies and lower community mobility experienced a larger increase in financial app downloads in the wake of the pandemic (Didier et al., 2021). The theme of “Fintech, Artificial intelligence and machine learning” in lower right quadrant with high centrality and low development (basic quadrant) is reflective of the growing significance of AI technologies for financial inclusion. The second theme of “Digital finance, financial inclusion and Fintech” in the basic quadrant represents the growing impact of Fintech and digital finance on the development and delivery of financial services aimed at financial inclusion (Figure 7.12). The use of AI and Fintech for achieving financial inclusion is increasingly being adopted by Fintech companies and financial institutions in their service delivery. Declining or emerging topics with little relation to the topic might be found in the lower left quadrant. Alternative finance and microfinance is the prominent theme in this quadrant. Alternative finance terms like asset funding, cash flow funding, crowd funding, cryptocurrency (Bitcoin), slow money, pension fund investments, social impact bond, etc. are used frequently. Many people with low incomes rely on

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alternative financial services (AFS), which are financial services offered outside of traditional banking organizations (Bradley et al., 2009; Blank, 2008). These services frequently take the form of microfinance in poor nations (Arp et al., 2017). By offering analytically sound alternatives to determining the identity and creditworthiness of individuals and businesses based on alternative data collected from mobile phones, satellites, and other sources, AI can address the problem of lack of traditional identification, collateral, and credit history, required to access financial services, especially in emerging markets, and reduce cost of financial transactions by automating various processes—customer service and custodial services. Microfinance and alternative finance are anticipated to dominate study topics soon.

7.6 DISCUSSION The number of articles increased from 2018 to 2020, and the average number of citations each year peaked in 2019 before slightly declining in 2020 and 2021. Scholarly interest in this subject is at an emergent phase with few authors engaged in research in this field and hence most recent periods of 2020 and 2021 have fewer average citations per year than of 2018 and 2019 and the number of publications per year shows a fluctuating trend between 2018 and 2021. Most cited journals are from fields of e-commerce, production and operations management, finance, law, and information systems which shows the multi-disciplinary perspective being adopted by researchers. The most cited journal, that is, “Management Science” covers all aspects of management linked to strategy, entrepreneurship, organization, information technology, and all aspects of business functions like finance, production, and information technology. The fields of finance, operations, and information systems as well as multi-disciplinary magazines like the Journal of Business Research, which publishes on a wide range of business processes, choices, and activities, are those that receive the most citations. The subject of AI, Fintech, and Financial inclusion has been addressed by leading disciplinary, inter-disciplinary, and multi-disciplinary journals from both theoretical and practitioner viewpoints. The subject has been researched from cross-, multi-, and inter-disciplinary perspectives within the domains of finance, information systems, law, and production and operations management, technology, economics, business strategy, and property markets. The subject covers all types of businesses like corporates, start-ups, small enterprises which are domestic and multi-national. Both prescriptive and normative approaches define research in this field which encompasses different types of financial services organizations including large corporates, small enterprises, start-ups, and domestic and multi-national companies. The scope and nature of the core journals on the subject transcend various disciplines which have disciplinary, multi-disciplinary, inter-disciplinary, and trans-disciplinary focus as their scope of research publication. The publications are scattered across a wide range of subjects and disciplines with no journal dominating the publication landscape. The most impactful authors on this subject based on local (selected articles) h and g index are from the USA, France, Bahrain, and Spain which indicates the geographical concentration of research on this subject is specific to developed

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countries. The most impactful authors in this field are leading the discussions on experiences and perspectives on AI adoption by incumbents and challengers (Fintech start-ups), opportunities and risks of financial technologies for banking, investments, and microfinance, how AI is being used more and more in finance to analyse funding success and default predictions, the benefits and difficulties associated with blockchain technology, cryptocurrencies, and Sharia compliance, as well as legal and regulatory issues, How Islamic financial institutions can work together with Fintech firms to be more effective, the self-regulatory approach or stricter financial institution-based regulation of Fintech, the role of humans in using AI to assess borrower creditworthiness, various financial paradigms and technologies in Fintech, the adoption of robo-advisors by clients and FSP employees, and the strategic ramifications of Fintech for Indian and South African retail banks are just a few of the topics covered. Thus, the emergent field of applications of AI and Fintech on financial inclusion has a technology-dominant focus on how AI and Fintech are impacting financial inclusion. The various legal, regulatory, customer, technological, and organizational aspects of financial technologies are at the epicenter of research in this field. The most cited articles emphasize the challenges in the adoption of AI by customers and the impact of financial technologies like mobile digital payment systems and distributed ledger technologies or blockchains on Fintech. A conceptual article on the intersection of operations and information systems with a brief mention of how mobile digital payment systems and distributed ledger technologies via blockchains are affecting the Fintech industry is the second most cited article following the empirical study into the factors that influence how customers adopt robo-advisory services. The top ten cited articles on this subject have selective relevance to the subject of AI and Fintech in Financial inclusion as the content and the contexts of most of these articles are distributed across various sub-topics. The most influential subjects in this research field have been the adoption of AI technology, the impact of Fintech and relationship banking on Islamic finance, the digital transformation of financial ecosystems, and access to finance. The scope of research of these articles is varied and dispersed due to the emergent status of this subject. The impactful articles are mostly of exploratory or review type with qualitative research methodology and hence do not have adequate theoretical grounding except for few empirical articles, for example, the paper on robo-advisory adoption which has a consumer technology adoption theoretical approach. The most cited countries of corresponding authors are from developed economies of the USA and Europe and developing economies of South Asia and South Africa. Very few studies have been cited from South America, East and central Europe, and North and central Africa. This could be due to the technology-centric focus of research in this field which is being led by developed countries and emerging markets of, for example, China, India, and South Africa which are adopting financial technologies very fast while financial inclusion issues from business, social and cultural perspectives are yet relatively under researched even in countries like Indi with large sections of financially excluded populations. The scientometric analysis of the frequency of keywords (WordCloud) shows that research on application of AI and Fintech for Financial Inclusion prevails at the

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intersection of industry 4.0 financial technologies (bitcoins, electronic money, digitalization, data analytics, computer science, information technology, intelligent robots e-commerce), competition (disruptive innovations, incumbents, industrial revolution, global value chain), financial ecosystem (hedging instruments, implications for futures, financial services, mobile payment, investments, finance channels, financial markets, financial institution), sustainability (economic sustainability and environmental challenges, business process, green manufacturing) and innovation (innovation performance, entrepreneurship) (Figure 7.12). Thus, the multi-dimensional and multi-disciplinary perspectives of this research field make it wide and broad based in its scope and vision which also makes it difficult for the development of an integrated model of application of AI and Fintech in financial inclusion. Further, financial inclusion is not yet a mainstream theme of research in AI and Fintech which is a blind spot that needs to be addressed. The analysis of co-citation network of authors (Figure 7.10) supports the findings on themes that are impacting this field as discussed in earlier sections on most cited authors and most frequent author’s keywords. The intellectual structure of this field is defined by research on the strategic implication of financial technologies for financial services providers, the adoption, regulation, and performance of these technologies and how they impact investment decisions, business models, and the financial ecosystem; the structure and types of algorithms; the social, cultural, economic, and organizational implications and processes of adoption of these technologies by customers and employees. Topical dendrogram consists of two clusters with Cluster one representing the provision of financial inclusion and financial services through digital financial services and digital technologies by financial service providers and institutions (Figure 7.11). Cluster two represents the most applied financial technologies in this field that is, ML, AI, Cloud computing. Thus, research in this field is thus clustered around the provision and delivery of digital financial services and the predominant financial technologies.

7.7 CONCLUSIONS The review article on AI, Fintech, and Financial inclusion maps the trends in publications, authors, and countries and attempts to develop the intellectual and conceptual structure of this fast-developing subject in recent literature. The subject has a multi-disciplinary and inter-disciplinary perspective which is however geographically concentrated in select and developed countries like North America and Europe and in South Asian countries with impactful policy approaches towards digital financial services. The subject has been researched mostly from financial technology (ML, AI, Cloud computing) and competition (Incumbent, fintech companies) perspective though research on how AI is impacting financial inclusion and Fintech is yet to become part of mainstream research. The trends show that while various Industry 4.0 technologies like AI including deep learning, blockchain, social media, and cloud computing are transforming the financial services landscape, only social media has emerged into mainstream research while deep learning and AI are emerging or declining subjects which have not yet engaged mainstream researchers interest. In addition, research on AI has mainly focused on machine learning and deep learning, with little effort put into

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exploring how other AI technologies, such as expert systems, augmented reality (AR), and virtual reality (VR) may be used to achieve financial inclusion. The majority of research on financial inclusion has been conducted from the viewpoint of digital financial services, which may represent a significant gap in the literature because deep learning and other emerging AI technologies, such as augmented reality/virtual reality (AR/VR), natural language processing (NLP), and AR/VR, have the potential to revolutionize the fintech industry and increase financial inclusion, but they have not yet found a foothold in academic circles.

7.7.1 FUTURE RESEARCH DIRECTIONS The following research directions are recommended for future investigation based on the review and gaps found in this study. Further studies on the role of specific AI technologies (e.g., NLP, ML, AR, and VR) and financial services and functions (e.g., credit scoring, creditworthiness appraisal, credit disbursal, financial portfolio management, access to microfinance, and micro credit delivery) in financial inclusion is warranted. Factors determining adoption of specific AI technologies by various stakeholders for example banks, financial institutions, capital markets, consumers, intermediaries can be empirically researched within varied contexts (for example institutional structure, culture, social relations, economic environment). For example, how Fintech and AI can enable financial inclusion of poor consumers through Islamic finance could extend the reach of formal financial services to regions where Islamic finance is predominant. While AI has been adopted by the mainstream financial service providers for the financially included sections of society, the processes, applications, and business models which can enable financial access to deprived sections of society needs further research? Some of the research questions which can be pursued are as follows: • What part does AI play in generating value for poor groups in society? The perspective of the beneficiaries has not been considered in AI research, which has been conducted from the perspective of financial service providers. Research into the barriers and opportunities for Fintech adoption among underserved and vulnerable populations, including low-income individuals and those lacking digital literacy, can contribute to inclusive financial systems that bridge the digital divide. Investigating AI enhanced customer experiences through personalized financial services can provide insights for personalized products, services, and recommendations for marginalized customer segments. Researchers can also address the balance between personalization and privacy concerns. Research into how AI can be employed for analyzing customer data to understand preferences, behaviors, and needs and offering relevant product recommendations, promotions, and services is a potential research opportunity. AI applications for predicting customer life events and financial needs through AI-driven predictive analytics which allow proactive engagement and offers, such as mortgage refinancing or investment opportunities can be researched by

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scholars. How AI ensures a seamless transition between different touchpoints, enabling uninterrupted customer interactions between different channels to enable an omnichannel experience for enhanced financial inclusion is a future research direction. • How can policy and organizational tools and processes support the adoption and deployment of AI and Fintech more quickly and effectively to promote financial inclusion? Researchers can investigate the regulatory challenges and opportunities associated with AI-driven Fintech thus addressing concerns related to data protection, algorithmic transparency, and compliance and provide guidance to policy makers and regulators in creating effective frameworks. Research into AI’s role in credit risk assessment can be undertaken to address questions related to fairness, accuracy, and predictive power. Understanding the impact of AI on credit access can shape lending practices and financial inclusion efforts. Investigating the potential of AI in automating regulatory compliance processes, including anti-money laundering (AML) and know-yourcustomer (KYC) procedures, can improve efficiency and reduce operational costs for financial institutions. Research into how AI-powered algorithms can effectively detect and prevent fraud in real-time can further strengthen financial systems leading to optimizing accuracy and reducing false positives. Researchers can explore the ethical dimensions of AI applications, such as customer data privacy, algorithm bias, and transparency for further understanding of the ethical challenges and designing of potential solutions which can guide policymakers and practitioners in building responsible AI-driven Fintech systems. • How does AI impact the financial ecosystem and industry structure? How will the structure of for example payment services change or be affected? What business models are being adopted by entrepreneurial Fintech companies, incumbent FSP’s, non-banking financial institutions, and other financial services providers?Investigating how AI disrupts and reshapes traditional business models, including branch networks, customer engagement, and workforce dynamics, can provide insights into the evolving landscape of financial services. The integration of AI into organizational systems and how it impacts the roles and responsibilities of employees is a potential area for research. With the automation of routine tasks by AI, financial services professionals would need to upskill on value-added services that require human judgment, empathy, and complex decisionmaking. Researchers can explore how AI tools can enhance the efficiency and effectiveness of financial professionals in data analysis, risk assessment, and compliance monitoring tasks. Researchers can investigate the emergence of new job roles like AI specialists, data scientists, and machine learning engineers within financial institutions. Research into how AI enhances cross-border payments, remittances, and currency exchange can lead to cost reduction, faster transactions, and increased financial access for global populations.

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The Role of Islamic Fintech in Indonesia to Improve Financial Inclusion for Resolving SDGs Atina Shofawati Universitas Airlangga, Surabaya, Indonesia

8.1 INTRODUCTION Each firm goes through four stages, including introduction, growth, maturity, and decline, in accordance with the principle of the product life cycle. Innovation is one method for boosting the maturity stage. Digital finance is innovation in the financial services industry. Particularly for Indonesia’s small and medium enterprises (SME), digital financing makes doing business easier. Small- and medium-sized businesses (SME) are essential to Indonesia’s economy; hence, their role in fostering economic growth is crucial (Shofawati, 2019). In the world, 700 million people survive on less than $1.90 every day. Theoretically, the most vulnerable low-income group in society also has the largest financial demands, yet they are typically shut out of the formal financial services markets. Because banks typically prefer to serve the wealthier, more lucrative parts, leaving the bottom of the pyramid unserved, there are currently two billion individuals worldwide without access to banking services. The significance of an inclusive financial system is widely acknowledged in policy circles and has been linked to improving access to education and healthcare facilities, reducing poverty, and social empowerment (Diniz, Birochi, and Pozzebon, 2011) in Nada (2020). By making investments in their businesses, children’s education, or healthcare, those who are excluded in society can escape poverty thanks to access to inexpensive financial services. Also, it makes it easier for those who are economically challenged to handle spikes in expenses or unforeseen financial setbacks. The goal of the World Bank Group’s Universal Financial Access 2020 (UFA 2020) effort is to get the approximately 2 billion people who are not already banked into established financial institutions (Nada, 2020). It is not surprising that there are issues with how finance and development interact on a global scale, particularly in areas where persistent economic inequality 112

DOI: 10.1201/9781003125204-8

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and financial exclusion exist. For instance, the UN 2030 Framework for Sustainable Development acknowledges the critical role that financial inclusion plays in achieving the Sustainable Development Goals (SDGs) and decreasing inequality (SDG) 10 in (Klapper, El-Zoghbi, and Hess, 2016) in Demir et al. (2020). According to the Global Findex database, 1.7 billion adults globally still do not have access to formal financial services, and 760,000 of those who have still do not use them. This is despite the substantial progress made in financial inclusion in recent years. High costs, distance, and documentation requirements are frequently cited as reasons for not having or using a financial institution account (Demirgüç-Kunt et al., 2018) in Demir (2020). However, it appears that there is a great deal of optimism and aspiration that recent advancements in financial technology (FinTech) will present unheard-of opportunities to overcome obstacles to financial inclusion and close the remaining gaps in ownership and use of bank accounts (or accounts at a financial institution), by taking advantage of the growing adoption of mobile technology (AFI 2018) in Demir et al. (2020). Mobile financial services are the FinTech type that has the most potential to integrate the remaining unbanked into the formal financial system and, ultimately, to promote more equitable growth (GPFI, 2016; DemirgüçKunt et al., 2018), according to Demir et al. (2020). The Islamic finance industry is worth USD 2.5 trillion. Takaful, Islamic capital markets, Islamic banking, and other IFIs are all included in what is known as shari’a-compliant finance or Islamic finance. According According to ICD-Refinitiv, 1,447 Shari’a-compliant financial institutions were doing operations in 72 countries in 2018. The value of Islamic finance assets was predicted to rise from USD 2.5 trillion in 2018 to USD 3.5 trillion in 2024. With a share of 70% or USD 1.72 trillion in 2017, Islamic banking remained the largest area of Islamic finance, followed by Sukuk ($470 billion, 18%), other financial institutions ($140 billion, 6%), Islamic funds ($108 billion, 4%), and Takaful ($46 billion, 2%) (ICD-Refinitiv, 2019) in (World Bank Group, 2020). According to IFSB, most Islamic finance assets are located in Asia, the GCC, and MENA areas, accounting for 95.5% of all Islamic finance assets globally in 2017. Islamic banking accounts for the greatest portion of the total global asset base. Islamic banking, however, only makes up a small portion of all banking assets globally and in most of the region’s countries. The two biggest Muslim nations in Asia, Pakistan, and Indonesia, have Islamic banking assets that make up 15% and 5.8%, respectively, of all banking assets. This is true even though Muslims make up 96% and 87% of the populations in Pakistan and Indonesia, respectively. Consequently, with the appropriate plan, there are substantial chances to increase market share (World Bank Group, 2020). Islamic financial assets can be further divided into six main financial service categories, including funding, trade finance, treasury financing, wealth management, and Takaful. Gaining market share in these service areas, which are now dominated by non-IFIs with broad customer bases, big customer datasets, and stringent security compliance, requires leveraging disruption and innovation (World Bank Group, 2020). DinarStandard reports that there are already 93 Islamic fintech businesses, the majority of which are engaged in financial services, followed by wealth management and finance. Distributed ledger technology is offered by 14 companies,

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followed by 65 companies that supply peer-to-peer (P2P) finance. Given that P2P platforms directly link the capital providers and the capital users via technology, they reflect the essence of Islamic finance and are the most popular Islamic fintech platforms. With 31 businesses, Indonesia leads all other countries offering Islamic fintech, with the US, UAE (led by the DIFC), UK, and Malaysia far behind with 12 companies or fewer each. Two of the top five jurisdictions for Islamic fintech do not have a significant Islamic finance industry, indicating fintech development expertise. Islamic financial technology is categorized according to financial services, technology, and geography (World Bank Group, 2020). Governments are actively encouraging the development of Islamic fintech since they recognize the potential benefits of it. As part of a welcoming regulatory and policy environment, nations are creating hubs for startups to engage and collaborate, offer early-stage funding, and connect with investors (World Bank Group, 2020). According to the Fintech Report for 2019, there are currently over 240 Islamic Fintechs operating internationally, serving a diverse variety of clients and meeting their financial demands through a number of cutting-edge technologies. For OIC nations, the Islamic Fintech sector was expected to be worth $49 billion in 2020. Based on transaction volumes, this accounts for 0.72% of the current size of the worldwide Fintech market. According to projections, the Islamic Fintech sector would reach $128 billion across OIC nations by 2025, growing at a 21% CAGR. This is a more favorable comparison to the 15% typical Fintech CAGR. Saudi Arabia, UAE, Malaysia, Turkey, and Kuwait are the top five OIC Fintech markets by transaction volume for Islamic Fintech, suggesting a strong domination by MENAT area countries. The OIC Islamic Fintech market is 75% larger overall, with the top five markets accounting for most of that growth. This high level of market concentration among the top jurisdictions (Global Islamic Fintech Report, 2021). The fourth industrial revolution has brought about an unheard-of upheaval in the global financial sector, along with three key technical forces: automation, disintermediation, and decentralization. In the first half of 2018, the total amount invested in financial technology (fintech) was USD 57.9 billion (Islamic Fintech Report, 2018). Top-tier financial institutions have acknowledged the need for change and are increasingly spending money on big data, AI, and data analytics. Furthermore, 77% of them intend to integrate blockchain technology into the system by 2020. Yet, according to Thomson Reuters, the Islamic banking sector hopes to increase its asset base by USD 3.9 trillion by 2023. Fintech adoption in this sector is still in its early stages. This is mostly because it is domestic and OIC-based. Yet, the development of Islamic fintech is being driven by the talented young people involved in this sector (i-fintech). About 90 Islamic fintech businesses worldwide are reportedly providing customers with financial solutions, according to the 2018 Islamic Fintech Report. A total of 65 of them offer P2P technology solutions to support consumer and commercial solutions and 14 more employ blockchain technology for deposits and transfers. Indonesia, the US, the UAR, and the UK are the countries with the most Islamic fintech startups in terms of population (Tarique and Ahmed, 2019). After the global financial crisis of 2008, financial services in Indonesia have developed quickly, utilizing innovation and new financial technologies (fintech) to broaden their reach and offer new client services. Due to the increase in internet

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access, the popularity of mobile devices, particularly smartphones, big data analytics, and a variety of new financial service providers, including e-money and mobile payments as well as alternative finance platforms like P2P and other digital lending models, fintech has developed quickly. Fintech is seen as evolutionary rather than disruptive because it supports established financial institutions. In addition to assisting banks, the fintech industry in Indonesia is also giving rise to new financial companies that are quickly establishing connections with a greater number of unbanked and underbanked customers than in the past. Responsible fintech can narrow the financial inclusion gap more quickly than traditional financial services if it is implemented appropriately. Due to Indonesia’s low banking penetration rate, which was 48.9% in 2017, this is significant (World Bank Findex Report, 2017) in (OJK, 2020). The finance gap for MSME is another significant issue in Indonesia. According to estimates from the International Finance Corporation and the World Bank, Indonesia’s MSMEs need financing worth $165 billion (or 19% of GDP), yet there is now just $57 billion available (ADBI Working Paper Series (Oct 2019) in (OJK, 2020). The fintech sector in Indonesia views its purpose as improving access to and encouraging more and simpler use of practical and inexpensive financial services. To create an atmosphere with an appropriate regulatory framework, OJK has adopted an interactive and collaborative strategy. OJK encourages the safe use of new technologies that quicken exponential growth in order to provide a balanced regulatory approach. OJK aims to closely follow technological advancements without putting up obstacles that can hinder responsible innovation in order to better assist the sector. By implementing regulations that prevent any potential monopolistic acts, it also aims to ensure a competitive digital finance sector. The elements work together to establish a well-balanced regulatory strategy that encourages safe, responsible financial innovation and guarantees adequate consumer protection. A suitable fintech regulatory framework has been established by OJK under regulation OJK No.13/POJK.02/ 2018 on digital financial innovation in the finance industry. OJK identified 83 providers of financial digital innovation in April 2020. The 18 clusters and business models that make up this group of providers include aggregator, claim service handling, credit scoring, property investment management, financial planners, financing agents, funding agents, online distress solution, online gold depository, project financing, social network and robo-advisor, blockchain-based, tax and accounting, electronic know your customer (e-KYC), customer due diligence verification, and more. Insurance broker marketplace, insurtech, and regtech will all analyze the regulatory sandpit at OJK in the same manner (Otoritas Jasa Keuangan (OJK)) (2020). Through the National Islamic Finance Committee, the Indonesian government or regulators support Islamic Fintech as part of the OIC (KNKS). KNKS is a part of the federal government. The National Shari’a Finance Committee (KNKS) is an organization that serves as a national and international accelerator for the growth of Islamic finance and the broader Islamic Economy. It was established to oversee the implementation of the Indonesian Shari’a Economic Master Plan, which was released in May 2019 (KNKS, 2019) in (World Bank Group, 2020). It seemed interesting to continue the research in order to learn the state of the academic research on Islamic finance in relation to sustainable development goals that are, for example, related to the “social aspects” of finance (Olanrewaju,

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Shahbudin, & Zakariyah, 2020; The World Bank, 2017; Cattelan, 2018). These studies explained the existing relationship between Islamic finance and social finance. In actuality, the Sustainable Development Goals (SDGs) established by the UN (OECD, 2020) in Lanzara might be attained by institutional and economic players through the use of Islamic finance (Lanzara (2021). Islamic Research and Training Institute, IsDB & Al Maali Group (2021) present the report “Bridging Islamic Finance and Sustainability through Fintech—Focus on Maghre focuses on how to leverage fintech in Islamic banking for linking finance to sustainable development.” These are lofty goals when there exist vast variations among countries in their preparedness for the use of fintech and financial innovations towards sustainable development in general. The report tries to strike a balance between the reality and the ideal. It advocates the use of technology in Islamic finance for its efficiency and greater impact on sustainable economic development. To address this problem, the present report points out the integration of social, environmental, and economic development into the business models of IFIs through fintech as a key leverage point (Islamic Research and Training Institute, IsDB & Al Maali Group, 2021). Based on the study’s preliminary evidence and if supported by further research, Islamic finance and sustainable development goals have a proven relation that can be enhanced by all levels of stakeholders, from governments to banking institutions to social enterprises (Iannaci & Mekonnen, 2020) in Lanzara (2021)., PPPs and SMEs that have to coordinate their actions in order to generate a positive impact and effective sustainable development (Lanzara, 2021). Indonesia was one of the first countries in the region to regulate P2P lending and crowdfunding, and it continues to adjust these regulations to meet the new challenges and risks that develop. In the area of digital banks, OJK issued a digital banking regulation that distinguishes between electronic banking services and digital banking services (OJK, 2020). While fintech players are seen as central to helping attain Indonesia’s financial inclusion goals, numerous financial literacy and consumer protection issues need to be addressed in the digital age (OJK, 2020). Islamic Fintech is part of the Islamic Financial Service Institution. Indonesia is an archipelago country. The availability of fintech can fulfill the financial inclusion gap in the whole region in Indonesia. Therefore research inquiry for this chapter what is the role of Islamic fintech to improve financial inclusion for resolving SDGs in Indonesia? The purpose of this chapter is to have an objective to explain the role of Islamic fintech to improve financial inclusion for resolving SDGs in Indonesia.

8.2 THEORETICAL BACKGROUND 8.2.1 FINANCIAL TECHNOLOGY DinarStandard has defined Islamic fintech as, “fintech technologies exponentially enhancing and disrupting 20th-century Islamic financial services, operations, business models, and customer engagement.” The innovation or disruption can be categorized within the six financial services (World Bank Group, 2020).

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The evolution of FinTech has begun for so long and continues to grow. Arner, Barberis, and Buckley (2015) states that FinTech has three phases up to the current phase of today (David Varga, 2017) in Saad and Fisol (2019). According to David Varga (2017) in Saad and Fisol (2019) evolution of fintech consists of three phases: (a) Fintech 1.0 (1866–1987); (b) Fintech 2.0 (1987–2008); and (c) Fintech 3.0 (2008–). Fintech is used to describe new technology that seeks to deliver improved and new uses of financial services (Popescu, 2019) in Innousa (2021). Sahay et al. (2020) in Innousa (2021) argue that the emergence of fintech is changing the financial services landscape. They contend that fintech disrupts traditional financial services delivered by financial institutions predominately built on cash transactions and face-to-face interactions. With the development of digital platforms which can offer a range of financial products (new and or existing) accessible from mobile phones or computers, the need for traditional services is reduced. However, OECD (2020) in Innousa (2021) raises questions about whether fintech companies and services are replacing banks and other financial institutions. Whether they induce healthy competition, enhance efficiency in markets or rather cause disruption and financial instabilities. Navaretti, Calzolari, Mansilla-Fernandez and Pozzolo (2018) in Innousa (2021) argue that fintech companies will not replace banks in their most essential functions. Yet, they enhance competition in financial markets, provide services that traditional institutions do less efficiently or not, and widen the pool of users of financial products and services. Gomber, Koch, and Siering (2017) in Innousa (2021) discuss fintech companies as organizations with new business models built on internet-related technologies (e.g., cloud computing, mobile internet) providing financial services (e.g., money lending, transaction banking) that tend to promise more flexibility, security, efficiencies, and opportunities than established financial services. Arner et al. (2020) in Innousa (2021) argue that fintech comprises five major areas, finance and investment, operations and risk management, payments and infrastructure, data security and monetization, and customer interfaces. Three fundamental changes have influenced the development of fintech: massive data generation, advances in computer algorithms, and increases in processing power. These have been facilitated by high-speed broadband internet, cloud computing, and artificial intelligence, which have enabled big data analytics, blockchain technology, and biometric identification. Fintech is changing the way financial services are delivered to small businesses and low-income households. Traditionally, financial services have been delivered by banks and their agents, microfinance institutions, and informal systems (for instance relying on relatives, microlending clubs, or money lenders), with often limited competition. They are predominantly built on cash transactions and face-to-face interactions with the financial service provider. Those interactions are the basis for monitoring creditworthiness; they are also often the way customers become financially educated. The emergence of fintech is changing this landscape: with the development of digital finance tools that are accessible from mobile phones or computers, the need for face-to-face interactions is greatly reduced. The mobility restrictions to contain the current COVID-19 pandemic have amplified these benefits of expanding digital financial services. The development of digital platforms, which can offer a variety of financial products and serve as aggregators for

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existing financial products or fintech companies’ own products, helps maximize the value for customers by facilitating a comparison of the price and suitability of products and services offered by different companies. Fintech’s potential to boost financial inclusion has been on the radar of global leaders and policymakers, since long before the COVID-19 crisis. The Alliance for Financial Inclusion (AFI), a global network of policymakers, started in 2008 and set out its main objectives in the Maya Declaration in 2011. The G20 leaders also focused on financial inclusion in the Seoul Summit in 2010, endorsing a Financial Inclusion Action Plan (FIAP) and creating the Global Partnership for Financial Inclusion (GPFI). In 2015, the United Nations adopted SDGs for 2030, wherein financial inclusion features prominently. In 2016, the AFI and GPFI identified technology as a core aspect of financial inclusion, creating a new workstream, Fintech for Financial Inclusion. In 2018, at their Annual Meetings in Indonesia, the IMF and World Bank launched the Bali Fintech Agenda, which lays out the broad principles for the safe development of fintech, including to support financial inclusion. The COVID-19 pandemic has put a bright spotlight on how digital financial inclusion can be harnessed to respond to the crisis and how the crisis in turn would accelerate digital financial inclusion (Sahay et al., 2020). OJK’s vision of a digital finance sector in Indonesia rests on four main factors: fintech that is stable, contributive, inclusive, and sustainable. Stable means the platform, technology, business process, and standard operating procedures used in the digital financial products are safe for consumers to use and the regulations overseeing them ensure all risk management measures are in place and implemented. It also means that the technology and platform are reliable and resistant to hacking, security breaches, and systemic disruption during natural disasters or other operational risks. Contributive refers to the contribution that the digital financial service has on increasing access to financing for SMEs, as well as empowering consumers to improve their financial health by offering more suitable and beneficial financial products and services. The sector should also be competitive by offering services that are affordable and take advantage of new technologies to reduce costs and expand outreach. Inclusive ensures the products and services developed by the industry reach out to as many underserved communities as possible. Sustainability focuses on ensuring that the technology and business processes are responsible, environmentally friendly, and support the achievement of the Sustainable Development Goals. OJK wishes to foster the development of a responsible digital finance sector that builds in consumer protection practices, including the responsible use of client data, while ensuring the highest data privacy standards, appropriate levels of good governance, and compliance with for anti-money-laundering (AML) and combatting the financing of terrorism (CFT) requirements. OJK also sees the key role that collaboration plays not only with new fintech players, but also between traditional financial institutions and new fintech providers that focus on outreach to new sectors, expanding access to agricultural finance and value chain payments, as well as facilitating access to insurance, health, education, and other sectors. The building blocks for supporting the development of this sector are ensuring sufficient digital infrastructure, and ensuring there is an adequate pool of talent to build the sector (OJK, 2020).

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Through the regulatory sandbox, considering the risks generated by certain use cases, or any other specific issues that need special attention, OJK may also formulate specific regulations on specific subject matter. As the purpose of this regulation is very specific, it should be more detailed and in-depth. To avoid regulatory overload, the issuance of such regulations should be very selective and based on a strong market case. The main purpose of this regulation is to maintain the integrity and stability of the market, while accommodating the potential for market growth. The four major clusters of digital finance innovation in Indonesia are digital payments, digital banking, P2P lending, and crowdfunding. Digital Payments The Bank of Indonesia oversees Indonesia’s Payment Systems Blueprint, 2025. The payment system infrastructure is critical to promoting a digital economy and ensures the smooth processing of cash (currency-based payment) and noncash (deposit accounts-based payment and non-deposit account-based payment) circulation to the public inclusively and equitably. Bank Indonesia is also building BI-FAST, a fast payment infrastructure that serves all types of payment transactions, including card-based transactions. BI-FAST will clear with the Bank Indonesia National Clearing System and the National Payment Gateway as the retail infrastructure on the back-end. BI-FAST is expected to encourage industry competitiveness, provide more extensive payment options for the public, improve transaction efficiency, and strengthen the reliability of retail payment systems in Indonesia. To further enhance Bi-FAST, the Bank Indonesia National Clearing System will focus on clearing and checks settlement, and demand deposits. In addition, Bank Indonesia, together with industry, plans to develop an integrated payment interface to ensure interoperability and interconnectivity between various payment service providers and traditional financial institutions. The goal is to facilitate innovation by the service providers while reducing the barriers to entry (OJK, 2020). Bank Indonesia is also working to enhance standardization in the payments sector and has launched a quick response (QR) code standard for Indonesia to encourage interoperability and economic efficiency. Additional activities include standardization under the National Payment Gateway, the National Standard Indonesian Chip Card Specification, and the Garuda Card will also continue to be expanded under Indonesia’s Payment System Blueprint Strategy. In addition, domestic credit card standards are to be further developed to reduce the high interchange fees imposed by global credit card schemes. Digital Banking OJK issued Regulation No.12/POJK.03/2018 on the Implementation of Digital Banking Services by Commercial Banks. This regulation aims to encourage efficiency in banking operations through the use of responsible-IT innovation and to improve the quality of service so that it is faster, easier, and better suits customers’ needs. All banks offering digital banking services must be approved by OJK. Several banks in Indonesia have already provided digital onboarding through bank applications, collaborating with financial service and/or nonfinancial service suppliers that enable customers to transact faster by using an open API connection and providing payment authorization alternatives; such as the use of QR code payments. OJK notes a trend within the banking sector to expand financial services through the development of IT capacities so as to become full-fledged, digital banks. OJK also has observed that IT companies have an interest to participate in the Indonesian digital

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banking landscape by expanding their services to include digital banking. While the existing digital banking regulation has not yet covered the institution and licensing aspects for a full digital bank, OJK is conducting research to see how full digital banking maybe adopted in the future. OJK will carefully monitor and support this trend over the next few years. Nonbank Fintech or Peer-to-Peer Lending Indonesia’s largest fintech sector is the digital nonbank lenders. These include balance sheet lenders that operate platforms using their own funds for businesses and consumers, P2P business and consumer online lending platforms where people and other institutions provide loans to businesses or consumers and factoring or invoice online lending platforms that provide businesses with purchase invoices, or accounts payable from other businesses or clients to receive credit (OJK, 2020). Although there continues to be a rapid growth in fintech lending, the number of borrowers from outside Java, where most Indonesians live is considerably smaller than those within Java. The uneven growth rates have been attributed to Java’s larger economy and higher financial literacy levels. (OJK, 2020). Pursuant to this regulation, OJK acknowledges the need to support start-ups through the provision of alternative fund sources via IT so that these businesses can contribute to the national economy. The regulation is intended to provide legal certainty and protection to all stakeholders involved in the crowdfunding from the investor to the platform provider, to the investee. It also lays the framework for equity crowdfunding licensing. The promotion of equity crowdfunding in Indonesia has been slowed by low levels of digital financial literacy, and lack of awareness among SMEs, who have not understood what equity crowdfunding entails. Therefore, SMEs still rely on conventional sources of finance, such as loans or credit facilities from banks, multifinance companies, or P2P providers. It should be noted, however, that other factors, such as lack of capital and collateral mean that not everyone is bankable. This is especially true of SMEs, which are obliged to pay the interest and the principal from their uncertain cash flow. In equity crowdfunding, the investor could receive returns from profit sharing. If an investor wishes to exit from his investment, he can sell a portion or the whole on the equity crowdfunding secondary market, and potentially make a profit. While the COVID19 lockdown temporarily affected the issuance of new licenses for equity crowdfunding, licensing is expected to resume in the second half of 2020 (OJK, 2020). OJK appreciates the importance of its balancing role of providing an enabling regulatory framework to support innovation in the fintech sector while also ensuring the safety and soundness of the finance sector and the protection of consumers. OJK has therefore adopted three regulatory and supervisory strategies to support digital financial innovations in Indonesia: 1. Enabling but applying a balanced framework. OJK will ensure the safety and soundness of fintech development while prompting innovation and competition. 2. Agile regulations. OJK will follow a principles-based approach to regulating digital financial innovation, including harnessing the use of new regulatory technologies (regtech) to enhance compliance.

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3. Market conduct supervision. OJK is accountable for fintech regulation and supervision. The fintech industry is responsible for managing their business by applying sound corporate governance; risk management; compliance; and, an industry code of conduct to complement OJK’s marketconduct regulations. OJK regulates all fintech providers in areas such as digital banking; P2P, marketplace, and balance sheet lending; crowdfunding platforms; insurtech; investment and personal financial management providers; and market aggregators. It also shares oversight of the fintech sector with Bank Indonesia, which regulates payments and e-money (OJK, 2020).

8.2.2 FINANCIAL INCLUSION According to Rangarajan (2008) on page 1 of Nada, “Financial inclusion is the process of ensuring that vulnerable groups, such as weaker portions and low-income groups, have access to financial services and timely and enough credit where needed” (2020). Research from many markets, including Bangladesh, India, Kenya, Mexico, and Mexico, have shown a strong positive association between financial inclusion (FI) and a nation’s economic development (Iqbal & Sami, 2017; Kamboj, 2014). Egypt now considers financial inclusion to be a national objective for achieving sustainable economic and social development. With 28% of Egyptian adults living below the national poverty line and only 33% of adults having access to formal financial services, there is a big opportunity for banks to enter that market and significantly alter economic progress and social empowerment (Nada, 2020). Financial Inclusion as a Driver of Economic Growth: in their studies of the connections between financial inclusion and economic growth in India and Kenya, respectively, Iqbal and Sami (2017) and Julie (2013) (Nada, 2020). Based on earlier research, Kim, Yu, and Hassan (2018) in Nada (2020) studied the connection between financial inclusion and economic growth in 55 Organization of Islamic Cooperation (OIC) nations. The use of dynamic panel estimates, panel VAR, IRFs, and panel Granger causality tests were among the various analytical techniques used. As in earlier studies, the proxy variable for economic growth was GDP. Here, five factors were used to gauge financial inclusion: the ratio of life insurance premiums to GDP, the number of ATMs per 100,000 adults, the number of bank branches per 100,000 adults, the number of commercial bank deposit accounts per 1,000 adults, and the number of commercial bank borrowers per 1,000 adults. Despite the stark differences between nations in terms of level of religiosity, illiteracy rates, gender inequality, income levels, monetary policies, and political environments, the results of study number seven demonstrated that financial inclusion has a positive impact on economic growth in OIC countries (Kim et al., 2018) in (World Bank Group, 2020). Financial inclusion can be evaluated using several frameworks and terms. Two categories of financial inclusion are supported by the Innousa (2021) study: traditional financial inclusion and fintech-driven financial inclusion. The term “digital access” will be added to Sarma’s (2008, p. 3) definition of financial inclusion, which states that it is “a process that ensures the ease of access, availability, and utilization of the formal financial system for all members of an economy.” The

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revised explanation of what financial inclusion is will say: “A process that ensures the availability, accessibility, and usage of the formal financial system for all participants in an economy, whether through traditional or digital means.” Conventional financial inclusion includes a variety of financial services for both individuals and businesses, including savings, credit, transactions, and insurance. While internet connection via computers and mobile devices (internet access) captures the digital access and use of formal financial services, Innousa’s financial inclusion is driven by digital or fintech (Sahay et al., 2020) in Innousa (2021). This concept emphasizes several aspects of financial inclusion, including availability, accessibility (both traditional and digital), and utilization of the financial system (Innousa, 2021). 8.2.2.1 SDGs Meeting “the requirements of the present without sacrificing the potential of future generations to satisfy their own needs” is a key component of the notion of sustainable development. Three facets—economic growth, social inclusion, and environmental protection—are essential to realizing this vision. Islamic finance analysis is required since it is essential to the first two elements. Understanding how this system might help achieve sustainable development can be important (Sadiq & Mushtaq, 2015). Egypt’s Sustainable Development According to the United Nations (UN, 2019), sustainable development is “development that meets the demands of the present without compromising the ability of future generations to meet their own needs” (2020). Economic growth, social inclusion, and environmental conservation must all be balanced in order to realize an inclusive, resilient, and sustainable future. The 2030 Agenda for Sustainable Development’s 17 Sustainable Development Goals (SDGs) were formally accepted by world leaders in January 2016. The 17 SDGs’ overarching goal is to end poverty worldwide by fostering inclusive, sustainable, and fair economic growth, lowering social disparities, boosting living standards, and ensuring the sustainable management of the environment (UN, 2019) in Nada (2020).

8.3 METHODOLOGY This chapter employs the narrative review methodology. The “conventional” method of reviewing the available literature is the narrative review, which tends to interpret existing information qualitatively (Sylvester et al., 2013). Simply put, a narrative review doesn’t aim for generalization or abstraction; rather, it aims to synthesize what has been written about a specific issue. Cumulative understanding from the material reviewed (Davies, 2000; Green et al., 2006). Instead, the review team frequently takes on the assignment of compiling and synthesizing the literature to prove the merit of a given viewpoint (Baumeister & Leary, 1997). As a result, reviewers may choose to limit or omit findings in order to make a point. This strategy is relatively ad hoc, without specific inclusion criteria, and might result in biased interpretations or assumptions when choosing material from primary sources (Green et al., 2006). Many narrative reviews in the specific eHealth sector, as in many fields, adopt such an unstructured methodology (Silva et al., 2015; Paul et al., 2015).

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In this study, secondary data are used. A few articles were chosen based on their titles. To assure the relevance of this research, a rigorous assessment of the abstracts of the articles that were chosen was done in the next step. These articles were deemed appropriate and relevant for this research. The literature search has a strict deadline of November 2021. The list includes articles from every field of study that are pertinent to this inquiry. Documents and papers that were published between 2000 and 2021 have been chosen to respond to the research question. The years that the Millennium Development Goals and Sustainable Development Goals have been in effect are included in this time frame (MDGs 2000–2015 and SDGs 2015-still on operations). Documents from worldwide academic journals, book reviews, book chapters, editorial material, conference papers, and reports from some institutions were all included in this study.

8.4 RESULTS The Role Of Fintech In Increasing Financial Inclusion To Address SDGS. After the global financial crisis of 2008, financial services in Indonesia have developed quickly, utilizing innovation and new financial technologies (fintech) to increase reach and offer new client services (OJK, 2020). Due to the increase in internet access, the popularity of mobile devices, particularly smartphones, big data analytics, and a variety of new financial service providers, including e-money and mobile payments as well as alternative finance platforms like P2P and other digital lending models, fintech has developed quickly. Fintech is seen as evolutionary rather than disruptive because it supports established financial institutions (Otoritas Jasa Keuangan (OJK)) (2020). In addition to assisting banks, the fintech industry in Indonesia is also giving rise to new financial companies that are quickly establishing connections with a greater number of unbanked and underbanked customers than in the past. Responsible fintech has the ability to narrow the financial inclusion gap more quickly than traditional financial services if it is implemented appropriately. Due to Indonesia’s low banking penetration rate, which was 48.9% in 2017 (The World Bank, 2017), in OJK, this is significant (2020). The finance gap for MSME is another significant issue in Indonesia. According to estimates from the International Finance Corporation and the World Bank, Indonesian MSMEs need loans worth $165 billion (or 19% of GDP), but there is now only $57 billion accessible (ADBI, 2019) in OJK (2020). Small and medium-sized enterprise (SME) finance, social empowerment, and promoting sector competition are given priority in OJK’s focus on the positive features of the fintech sector (OJK, 2020). The fintech sector in Indonesia views its purpose as improving access to and encouraging more and simpler use of practical and inexpensive financial services. To create an atmosphere with an appropriate regulatory framework, OJK has adopted an interactive and collaborative strategy. OJK encourages the safe use of new technologies that quicken exponential growth to provide a balanced regulatory approach. OJK aims to closely follow technological advancements without putting up obstacles that can hinder responsible innovation in order to better assist the sector. By implementing laws that prohibit any potential monopolistic behavior, it also aims to ensure that the digital finance sector remains competitive (OJK, 2020). In Indonesia, P2P lending, crowdfunding,

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digital payments, and digital banking constitute the four main innovation clusters in the field of digital finance (OJK, 2020). Digital nonbank lenders make up the majority of the fintech market in Indonesia. These include balance sheet lenders who run platforms using their own funds for consumers and businesses, P2P business, and consumer online lending platforms where individuals and other institutions lend to businesses or consumers, and factoring or invoice online lending platforms that give businesses purchase invoices, accounts payable from other businesses, or clients to receive credit (OJK, 2020). One of the first nations in the area to regulate P2P lending and crowdfunding was Indonesia, which is still updating these rules to address emerging dangers and issues. OJK published a digital banking law in the domain of digital banks that makes a distinction between electronic banking services and digital banking services (OJK, 2020). Fintech companies are crucial to achieving Indonesia’s aims for financial inclusion, but there are several difficulties with consumer safety and financial literacy that need to be addressed in the digital age (OJK, 2020). Fintech and traditional financial services must work together in the financial services sector to attract new clients. New business models (innovation) including business-to-business, business-to-consumer, and peer-to-peer lending, as well as crowdfunding, are supported by technological development and new partnerships between the fintech and financial services sectors. The use of Islamic finance to further sustainable development objectives is a reality, and while the intellectual and economic debate in that area has only recently started, it has the potential for unending growth. It suggests that it is time to increase the contribution of Islamic finance to the 2030 Agenda’s goals through moral investments that will promote sustainable development. A special effort must be made to incorporate this kind of ethical finance in relation to the other SDGs, especially those related to SDGs 4—Quality education, SDG 5—Gender equality, and SDG 16—Peace, justice, and strong institutions. This is in addition to the 10 SDGs mentioned above that had been found to be related to Islamic finance. These Goals need to be seriously considered and may draw social investments to provide better living conditions for the engaged inhabitants, as those countries’ current standards of living are not acceptable (OECD 2020) in Muslim countries, which have a closer relationship with Islamic financing (OECD 2020) in Lanzara (2021). There were 364 fintech businesses in Indonesia as of April 2020. The two fastestgrowing fintech categories in Indonesia are P2P (both marketplace and balance sheet lenders); and payment service providers. They represent business models such as P2P, payment provision, Digital Financial Innovation platforms and tools for personal and wealth management, and insurtech. Data from OJK from March 2020 illustrates the providers’ explosive expansion and reach. There were 161 P2P lending companies in Indonesia as of the end of March 2020, 136 of which were registered and 25 of which held an OJK licence. Of these, 149 are traditional fintech lenders and 12 are Sharia lenders. The entire outstanding P2P loan portfolio was Rp12.62 trillion in Java and Rp2.17 trillion outside Java as of the end of 2016. Disbursed P2P loans reached Rp87.72 trillion in Java and Rp14.81 trillion beyond Java. From March 2019, there has been an increase of over 86% (OJK, 2020).

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Toatl 70% of P2P borrowers are between the ages of 19 and 34, and 28% are between the ages of 25 and 54. Men make up 50.59% of fintech consumers, while women make up 49.29%. Only 0.13% of borrowers are businesses; the majority are people or tiny MSMEs (OJK, 2020). There are 107,966 lenders outside of Java and 528,441 lenders in Java. In comparison to the prior year, the number of lenders climbed by 157% in Java and 66% outside of Java. The number of lenders in this category climbed by 72.5% between 2019 and 2020, indicating that larger corporate investors are also becoming more interested in offering funding through P2P lending platforms. Financial consumer protection needs to receive more attention as a result of the huge growth in loans and the addition of new individual and corporate lenders (OJK, 2020). Fintech enhances financial inclusion by giving people greater access to financial products and services like payments, savings, credit, and insurance in a responsible and sustainable manner, especially those in underserved groups like those living in rural areas, the unbanked and underbanked, women, and MSMEs. Services made possible by fintech can also boost productivity, reach, and competition. Yet, emerging technologies and unregulated fintech companies (including third-party operators) might raise operational risks and dangers for customers who might not grasp the risks involved with the products being sold, particularly in populations with low financial literacy (OJK, 2020). According to study results published in 2019 by OJK, 38% of Indonesians were financially literate. However, the rate of financial literacy is significantly lower among young people, maybe as a result of their limited exposure to financial services. Similar to how mental acuity starts to deteriorate with age, it is often lower in older populations. Emerging technology might potentially result in unethical business practises and data privacy problems. The overall objectives of financial inclusion and sustainability of the fintech ecosystem are supported by OJK’s Responsible Digital Finance Framework, which is built on a balanced regulatory framework, responsible industry development, and enhancing consumers’ overall financial capability to use digital financial services through increased financial access and literacy (OJK, 2020). The fintech industry draws significant investment. With transactions mostly signed by US fintech companies worth up to USD 52.5 billion, fintech investment has skyrocketed across regions, with the Americas bringing in USD 54.5 billion in 2018 (compared to USD 29 billion in 2017). Following Asia with USD 22.7 billion in fintech investment, Europe drew USD 34.2 billion, nearly doubling from USD 12.2 billion in 2017 (KPMG, 2019). The top 5 fintech companies in each region accounted for more than 50% of the deal value, indicating a fairly consolidated market. Investments in established fintech firms and a strong level of investor confidence (World Bank Group, 2020). According to Islamic teachings, every Muslim is obligated to donate to many charitable causes, with the most important ones being Zakat (obligatory charity), Sadaqah (voluntary charity), and Waqf (Islamic endowments). Every person and organization is obligated to donate to the less fortunate through Zakat, and they are also urged to make voluntary contributions through Sadaqah. Another definition of Shari’a Waqf is an Islamic religious endowment, which is the voluntary and

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irrevocable dedication of all or part of one’s wealth, whether in cash or in kind (such as a house or a garden), and its disbursement for Shari’a-compliant initiatives (such as mosques, religious schools, and other social projects) 2020 (World Bank Group). As a result, firms must be concerned with both profitability and providing beneficial societal contributions through Zakat, Sadaqah, and Waqf since Islamic finance adheres to Shari’a. According to UNDP in 2018, Zakat could possibly provide $200 billion to $1 trillion in total to reducing poverty, and Waqf assets in only Indonesia and India are expected to have an economic value of USD 84 billion. If effectively realized, this untapped potential of Islamic social financing can contribute to closing the USD 2.5 trillion annual deficit in the UN’s SDGs (World Bank Group, 2020). Access to inexpensive financial services by all societal segments is a key indicator of financial inclusion. Bank accounts, accessible credit, insurance, payments, and trade activities all fall under the category of financial services. Financial inclusion and economic growth are directly related, according to research by Kim, Yu, and Hassan, and more financial inclusion will increase economic growth in OIC countries (Kim et al., 2018) in (World Bank Group, 2020). By delivering financial services efficiently to all parts of society, financial inclusion enables people to manage their financial needs, reduces poverty, and supports wider growth of the economy (Kim et al., 2018) in (World Bank Group, 2020). As an inclusive financial system, financial inclusion is a crucial pillar of development strategy in nations all over the world. It is essential for eliminating extreme poverty, increasing shared prosperity, and fostering sustainable inclusive economic growth. Financial inclusion is measured by the access to financial services by all sectors of society at an affordable cost (Buckley et al., 2019) in (World Bank Group, 2020). The SDGs that were a component of the Agenda for Sustainable Development that UN member states endorsed in 2015 put financial inclusion at the center. The SDGs provide a framework to eliminate poverty, achieve zero hunger, provide universal quality education, and other targets within social, economic, and environmental areas (https://www.un.org) in (World Bank Group, 2020). Access for the unbanked population is anticipated to be facilitated by fintech and innovation in governmental policy. Each fintech subcategory should make a distinct contribution to growing financial inclusion, effecting mobile payments to rural areas, applying digital verification to simplify consumer identification, and lowering transaction costs. Local governments can implement cybersecurity rules, create credit protection programs, and fund online learning initiatives simultaneously. Governments can also provide financial institutions with incentives to target the unbanked and encourage the development of a financial ecosystem with a variety of entry points. Finally, to serve the unbanked, creative credit evaluation procedures must be developed in the absence of credit histories (World Bank Group, 2020). Closing the USD 2.5 trillion yearly financing gap will be necessary to achieve the SDGs. UNDP estimates that until 2030, cumulative investments of USD 5–7 trillion per year would be needed to achieve the SDGs. For developing nations to achieve their Goals, there is an approximate USD 2.5 trillion financial shortfall every year. The IMF estimates that the yearly financing shortfall constitutes 15% of the GDP of developing countries, highlighting the need for more investment in basic infrastructure, health, and education (roads, electricity, water pipes, and

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sanitation services). In 2016, the OECD Development Assistance Committee (DAC) revealed that the greatest sum ever received through official development aid was USD 142.6 billion, or less than 10% of the yearly expenditure needed to close the funding gap. Development Agenda stated that in order to successfully accomplish the SDGs, governments must provide at least 50% of the total funding needed. The global GDP of poor countries may increase by 3.7 trillion USD thanks to the fintech industry, which would reduce the financial gap. According to the McKinsey Global Institute, fintech can benefit billions of people by spurring growth and raising the GDP of developing nations by an estimated USD 3.7 trillion by 2025. Certain Goals can be made profitable while also having a positive social impact, which will encourage fintech companies to take the initiative in promoting financial inclusion. The following fintech use case studies demonstrate that financially inclusive activities can also aid in achieving various Goals linked to financial independence and reducing inequality (World Bank Group, 2020). Given the importance of ethics in the Islamic Finance paradigm, the potential integration of Islamic Finance operations into the real economy, and the potential for synergies with Awqaf (Islamic endowment funds) and Zakat institutions (compulsory charity), IFIs have the legitimacy to play a leading role in resolving finance and sustainability under a distinctive value proposition. Customers’ good views towards sustainability, the availability of finances, and the requirement to provide a value proposition that extends beyond “Shari’ah compliance” (Islamic Research and Training Institute, IsDB, and Al Maali Group) are all contributing factors (2021). Fintechs are fascinating tools that help IFIs go from “business as usual” to later stages of their sustainability journeys. Fintechs do, in fact, promote product innovation, operational excellence, and the opportunity to fundamentally alter the consumer experience (Islamic Research and Training Institute, IsDB, and Al Maali Group) (2021). There are numerous instances of fintech businesses founded by public, commercial, and nonprofit entities with the goal of solving sustainability-related problems. These examples demonstrate how Fintech may cut transaction costs, increase efficiency, and stimulate innovation to provide a value offer that balances the need for financial gain and the need for social impact (Islamic Research and Training Institute, IsDB, and Al Maali Group) (2021). Fintech must be used to bridge Islamic finance and sustainability. Several IFIs engage in a number of social programs, ranging from Qard Hassan and energy conservation to Zakat payment and charity contributions, from the sustainability perspective. But, overall, these businesses have fallen short of what customers expected in terms of their social and environmental initiatives. Moreover, Islamic Financial Institutions score worse than their conventional counterparts due to their poor degree of ethical and sustainable disclosures (Islamic Research and Training Institute, IsDB, and Al Maali Group) (2021). The market has not yet recognized the potential of the Islamic Finance industry in promoting sustainable development with favorable environmental, social, and governance outcomes, despite the wealth of literature on the fit between sustainable development and Islamic Finance as well as some successful impact finance

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initiatives, particularly in South East Asia (Islamic Research and Training Institute, IsDB & Al Maali Group, 2021). Investment in financial technology (Fintech) is a crucial factor to consider if the Islamic finance sector is to integrate sustainability into its fundamental business models. Today’s fintech offers enormous prospects that weren’t available in the past. Financial services can now be made more accessible, scalable, adaptable, and effective thanks to technology (smartphones, peer-to-peer platforms, blockchain, artificial intelligence, etc.) (Islamic Research and Training Institute, IsDB, & Al Maali Group, 2021). Some Islamic Fintech business models address social and environmental challenges in terms of sustainability. For instance, the Global Sadaqah website (https:// globalsadaqah.com)/in (Islamic Research and Training Institute, IsDB & Al Maali Group) unites contributors of Sadaqah, Zakat, and Waqf with reputable charity partners abroad (2021). Ethis Crowd’s platform for investment-based crowdfunding enables direct investment in real estate development and construction projects, with a particular focus on social housing in Indonesia (https://ethis.co/id/) in (Islamic Research and Training Institute, IsDB, and Al Maali Group) (2021). Finterra offers a blockchain ecosystem to enhance the effectiveness and performance of Waqf management in (Islamic Research and Training Institute, IsDB, and Al Maali Group) (https://finterra.org/) (2021). Despite these successes, Islamic Fintech has only begun to address sustainability challenges in recent years. There is yet a great deal of room for advancement by expanding and broadening current initiatives focused on peer-to-peer finance and utilizing additional Fintech sectors for sustainable development (Islamic Research and Training Institute, IsDB, and Al Maali Group) (https://ethis.co/id/) (2021). Financial inclusion through fintech resolved SDG challenges for resolving SDGs in Indonesia, especially SDGs 8 (Decent Work and Economic Growth). The majority of payments made by SMEs are in cash, leading to higher costs. Go-Jek distributed digital payments to its drivers and partner SMEs, with amounts reaching USD 700 million annually, providing decent incomes to its drivers and SME partners, while also contributing to economic growth (World Bank Group, 2020). SDGs 11 (Sustainable Cities and Communities). Kitabisa.com started as a crowd-endowment platform that involves mass funding for social or humanitarian causes. Kitabisa.com also distributes Islamic endowment funds like Zakat to enable the poor to improve their living conditions (http://www.kitabisa.com, 2019) in (World Bank Group, 2020). IFIs (Islamic Financial Institutions) are crucial to the accomplishment of sustainable development objectives. Literature suggests that financial institutions should concentrate on fostering organizational diversity and transition to portfolios with higher shares of equity-based financing in order to improve financial stability and resilience. Also, new equity-based businesses must be founded. By enhancing the availability of equity-based instruments in the capital markets and expanding their use, equity markets can be improved. Chances for small and medium-sized businesses to be listed publicly. Additionally, the issuing of public and private risk-sharing sukuk would open new prospects for lenders and investors. In order to reach more people and promote

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organizational diversity, financial institutions can set up specialized units to provide microfinance in Islamic banks. In the capital markets, social and retail sukuks can be used, and microfinance institutions based on zakat and waqf can be employed to support the social sector. Financial institutions give the poor a chance to save money and can provide a foundation for the growth of micro-takaful from the standpoint of lowering vulnerability and managing risk. The utilization of zakat and waqf as safety nets can benefit the social sector. Financial institutions can employ the macro-maqasid approach in their daily operations when addressing the role of IFIs in resolving social and environmental problems and can allocate funding to the growth of the social sector. Positive screening and social sukuk can also be used by capital markets to create waqf. The social sector can then broaden the zakat and waqf base and improve the effectiveness and efficiency of how these revenues are used. Finally, financial institutions should focus on syndicated finance while capital markets use sukuks to ensure an expanding infrastructure (Sadiq and Mushtaq 2015). The SDGs are 17 worldwide goals that 193 governments have agreed on. Those 17 suggested SDGs are more ambitious and comprehensive than their predecessor (MDGs), as they seek to provide an inclusive vision and framework for all countries’ evolution (Hashem, 2019). Indonesia’s Contribution to SDG Achievement through Islamic Financing With the formation of the first Islamic bank in Indonesia in 1992, Islamic finance began somewhat late. Since then, the industry has grown and diversified into a number of other industries. The country has four fully-fledged Islamic banks, 22 Islamic banking windows, and 160 Islamic rural banks (COMEC, 2019: 72) in Hashem (2019). Since the current investment is only about 1.4 trillion dollars, Indonesia needs between 3 and 4.5 trillion dollars per year to accomplish the SDGs by 2030, leaving it with a funding shortfall of roughly 2.5 trillion dollars. Zakat and other forms of Islamic finance (waqf, microfinance, skuk) offer an important opportunity to achieve the SDGs, by embodying socially responsible development and by bridging opportunities for economic growth and social welfare, especially for the poor and most vulnerable groups (UNDP, 2018: 2) in Hashem (2019). Indonesia is exploring the connections between Islamic finance and SDGs. A good initiative has been made: Baznas in mid-2017 extended its first contribution (US$ 350,000) to support the SDGs in Indonesia, focused mainly on renewable energy (UNDP, 2018:3) in Hashem (2019). A new law was passed in 2011 designating Baznas as the sole national organization in charge of gathering, allocating, and organizing the management of zakat. 6.8 million poor people, or 22.6% of Indonesia’s impoverished population, have benefited from Baznas (Hashem, 2019). For the SDG that calls for assisting in the eradication of hunger through DIGITAL FINANCIAL INCLUSION, the Indonesian government switched to card-based vouchers for the 1.4 million people who received subsidized rice in 2017. As a result, nine out of ten recipients reported receiving more and better food (Sulastri and Kumar, 2018) in (UNSGSA, 2018). Go-Jek implemented digital payments to enhance logistics and payments in line with the SDGs, which call for “More Economic Development and Quality Employment through Financial Inclusion,” and created a full range of services on its

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mobile app. The company distributes over $700 million in earnings to its drivers and partner MSMEs annually (The Jakarta Post, 2018) in (UNSGSA, 2018).

8.5 CONCLUSIONS Because most people in Indonesia are Muslims, Islamic fintech is particularly helpful in enhancing financial inclusion because it may satisfy societal demands for halal financial instruments. With Islamic financial institutions and Islamic social finance, Islamic fintech may increase financial inclusion and collaborate to address SDGs in Indonesia. Fintech enhances financial inclusion by giving people greater access to financial products and services like payments, savings, credit, and insurance in a responsible and sustainable manner, especially those in underserved groups like those living in rural areas, the unbanked and underbanked, women, and MSMEs. Services made possible by fintech can also boost productivity, reach, and competition. Fintech and traditional financial services must work together in the financial services sector to attract new clients. Business-to-business, business-to-business, and P2P lending, as well as crowdfunding, are examples of new business models (innovation) that have emerged as a result of technological development and new collaborations between the fintech and financial services sectors. The use of Islamic finance to further sustainable development objectives is a reality, and while the intellectual and economic debate in that area has only recently started, it has the potential for unending growth. It suggests that it is time to increase the contribution of Islamic finance to the 2030 Agenda’s goals through moral investments that will promote sustainable development. Given the importance of ethics in the Islamic Finance paradigm, the potential integration of Islamic Finance operations into the real economy, potential synergies with Awqaf (Islamic endowment funds) and Zakat institutions (compulsory charity funds), and customers’ positive attitudes towards sustainability, Islamic Financial Institutions (IFIs) have the legitimacy to play a leading role in reconciling finance and sustainability under a unique value proposition. Fintech must be used to bridge Islamic finance and sustainability. Several IFIs engage in a number of social programs, ranging from Qard Hassan and energy conservation to Zakat payment and charity contributions, from the sustainability perspective. The market has not yet recognized the potential of the Islamic Finance industry in promoting sustainable development with favorable environmental, social, and governance outcomes, despite the wealth of literature on the fit between sustainable development and Islamic Finance as well as some successful impact finance initiatives, particularly in South East Asia. Investment in financial technology (Fintech) is a crucial factor to take into account if the Islamic finance sector wants to integrate sustainability into its fundamental business models. Islamic Financial Institutions (IFIs) are crucial to the accomplishment of sustainable development objectives, and some Islamic Fintech business models address social and environmental concerns. Literature suggests that financial institutions should concentrate on fostering organizational diversity and transition to portfolios with higher shares of equity-based financing in order to improve financial stability and

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resilience. In the capital markets, social and retail sukuks can be used, and microfinance institutions based on zakat and waqf can be employed to support the social sector. Financial institutions give the poor a chance to save money and can provide a foundation for the growth of micro-takaful from the standpoint of lowering vulnerability and managing risk. The utilization of zakat and waqf as safety nets can benefit the social sector. Financial institutions can employ the macromaqasid approach in their daily operations when addressing the role of IFIs in resolving social and environmental problems and can allocate funding to the growth of the social sector.

8.6 LIMITATION OF THE RESEARCH The research’s scope is limited by how Islamic fintech is used in Indonesia to increase financial inclusion and address SDGs.

8.7 DIRECTION FOR FURTHER RESEARCH AND IMPLICATION Direction for further research can elaborate the role of Islamic fintech, Islamic finance, and Islamic social finance in Indonesia to improve financial inclusion for resolving SDGs with comprehensive explanation.

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Challenges of Artificial Intelligence Adoption for Financial Inclusion Priyanka Chadha Amity University, India

Rajat Gera CMR University, India

G.S. Khera Manav Rachna University, India

Mona Sharma Manav Rachna University, India

9.1 INTRODUCTION Financial inclusion achieves economic growth and reduces poverty (Izquierdo & Tuesta, 2015, Mhlanga et al., 2020, Mhlanga, 2020a). Artificial intelligence (AI) contributes to financial inclusion because it exploits alternative sources such as public statistics, satellite images, registries, and social media logs to reduce the cost of identity profiling, accessing creditworthiness of specifically low-income and rural customers, evaluating asset collaterals, analyzing repayment ability, and tracking behaviors (Peric, 2015; Meunier, 2018; Alameda, 2020; Biallas & O’Neill, 2020; Mhlanga & Dunga, 2020; Mhlanga, 2020a; Mhlanga, 2020c; Mhlanga & Denhere, 2021). By 2023, banks are expected to save US$ 7.3 billion in operating costs thanks to the intelligent chatbots. The World Bank through the International Committee on Credit Reporting (ICCR) and For the World Bank Group’s objectives of eradicating extreme poverty, fostering prosperity, and achieving financial inclusion, the International Finance Organization (IFO) provides financial institutions with advice on the development of algorithm-based credit scoring models (International Committee on Credit Reporting, 2019; Oliver & Marsh, 2019). For example, IBM Watson Chatbots assist Bank BCP’s customers in Peru for converting currencies and repaying credit cards 24 hours a day and answering 283.000 questions about 62 parameters with an accuracy of 95% of Banco Bradesco in Brazil. In the private sector, an algorithm of Yoma Bank in Myanmar reduced the non-performing loan ratio below 1% as of 2020 by scoring automatically micro, DOI: 10.1201/9781003125204-9

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small, and medium enterprises. The exploitation of the big data by Ant Financial, a subsidiary of Alibaba Group in China, assesses the creditworthiness of over 560 million connected individuals including those without collateral security to increase its granted loans from 0.5 to US$ 4 billion over four years at the rapidest pace and the lowest cost. The digital wallet of MTN in Ivory Coast reduced by almost 95% of conversations with customers about financial products and obligations (Tinsley & Agapitova, 2018; Oliver & Marsh, 2019; Mhlanga, 2020b; Biallas & O’Neill, 2020). The issues financial institutions have in providing financial services to the excluded are underappreciated in the new research, which focuses on the influence of AI on reducing the costs of low-value transactions for financial inclusion. In this chapter, we analyze the technological and social issues that frequently accompany the deployment of AI solutions in the financial inclusion of the excluded using covert research methods including document analysis. The remaining chapters are structured as follows: we start by reading up on the difficulties in implementing AI in the supply of financial products and services. We next move on to the document analysis and grounded theory study of the difficulties in implementing AI in the delivery of financial services to the economically disadvantaged. The conclusions and suggestions are followed by a discussion of the difficulties in implementing AI solutions for the financial inclusion of the excluded.

9.2 LITERATURE REVIEW: THE CHALLENGES OF AI IN PROVISION OF FINANCIAL PRODUCTS AND SERVICES There is unanimity about the impact of AI on cost reduction of financial transactions and corollary the financial inclusion of the low-income earners, small businesses, women, and youth (Ozili, 2018; Ozili, 2021; Mhlanga 2020a; Kandpal & Khalaf, 2020). AI describes how machines imitate human intellect by acting somewhat autonomously in response to their environment (Access Partnership, 2020; Meunier, 2018). Appearing as a discipline around 70 years ago, AI is now evolving towards study of efficient applications to reduce costs in transactions such as financial services (Radcliffe et al., 2012), application of blockchain for financial inclusion (Saon et al., 2019), deployment of digital finance to relieve farmers vulnerability (Wang & He, 2020), and the enhancement of financial good of people who are not versed in computer science (How et al., 2020). While AI arguably improves financial inclusion, its application encounters challenges that might fail the process. In this section, we review these challenges which basically happen to be those of consumer data exploitation, technological failure, agency hazards, algorithmic computation, cyber-attacks, and competition. The challenge of customer data refers to the use of data with the informed consent of the would-be (included) clients (Biallas & O’neill, 2020; Emeana et al., 2020). The challenge of technology occurs when the AI-based network fails to function properly and leads to data loss and privacy breach. Clouds that host data and applications might also fail to function properly. The leaked customer (biometric) data may be abused and misused (Xie, 2019; Kaspersky, 2019; Cuevas, 2020). If the AI applications are not deployed and managed by experienced people, the risks of technology failure are more important. The challenge of agency might strike when

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financial institutions outsource technological infrastructure and security to third-party tech firms which ignore the specific needs and behaviors of clients of financial services (Newman 2017; International Committee on Credit Reporting, 2019). The challenge of computational occurs when algorithmic bias produces discrimination about credit scoring models (Biallas & O’Neill, 2020; International Committee on Credit Reporting, ICCR, 2019). The algorithms might disadvantage some segments of the unbanked because of ethnicity, gender illiteracy, and socioeconomic conditions (Newman 2017; Oliver & Marsh, 2019; Cuevas, 2020). Financial service providers should attract staff with proper skills for AI-based credit scoring for example to bias and discrimination to the poor customers who might lose generally trust to the whole financial system (Oliver & Marsh, 2019). The challenge of cyber-attacks denotes the protection of the chain against the penetration of malefactors. Even distributed ledger technology reputed of its high security is attacked in the cyberspace causing approximately US$1.45 billion between 2013 and 2020 (Bouveret, 2018; International Committee on Credit Reporting, 2019; Biallas & O’Neill, 2020). While the technological failure is unintentional, the cyber-attacks are premeditated. The challenge of competition results from the action of the first movers of AI adoption in financial services who prevent newcomers to enter to the market and deprive consumers from the choice and the benefits of quality and price competition (International Committee on Credit Reporting, 2019; Biallas & O’Neill, 2020). The challenges of AI applications for financial inclusion might be classified into two categories. The category of human-based challenges contains those which result from ethical or unethical actions of actors: consumer data exploitation, agency hazards, cyber-attacks, and competition. The category of technological challenges includes uncontrollable factors like technological failure and algorithmic computation. To address these challenges, not only the public authorities should regulate and monitor to weed out customer data abuse and anti-competitive practices (Oliver & Marsh, 2019), but financial service providers should also govern operations, data privacy, cybersecurity, and biases (Newman, 2017). Theories of technology adoption: Over the years, scholars have evaluated factors influencing consumer and organizational technology usage intentions and behavior by using a variety of theories and models of technology adoption (TA). The Technology Acceptance Model (TAM) (Davis, 1986), the Technology-Organization-Environment (TOE) framework, the Theory of Planned Behavior (TPB) (Ajzen, 1985), the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), the Diffusion of Innovation (DOI) (Rogers, 1995), the Institutional (Scott and Christensen 1995, Scott, 2001 (Tornatzky and Fleischer, 1990). Several researchers have sought to examine the impact of technology adoption since the 1970s, both in a hedonistic environment and at the corporate level (Tarhini et al., 2015). The most popular theory for illuminating technological adoption at the enterprise level is the TOE framework (Oliveira and Martins, 2011). In comparison to other models of TA, the TOE framework is more comprehensive because it explains organizational usage of technological innovations, incorporates the context of the environment to better understand the adoption of intra-firm innovations, and has the potential to be applied to the adoption of IS (Oliveira and Martins, 2011). The

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framework takes into account the three elements of the technological context (which encompasses both internal and external technologies), the organizational context (such as firm size and scope, complexity of the managerial structure, quality, characteristics, and availability of firms’ technology and financial resources), and the environmental (or institutional) context (which refers to the firm’s industry, business partners, competitors, and government) (Tornatzky and Fleischer, 1990). Several technological, industrial, and national contexts have shown the explanatory value of the TOE model (e.g., Hsu et al., 2006; Oliveira & Martins, 2010; Wang et al., 2010). The initial conceptions of the contextual factors are somewhat generic and out-of-date, for example, notwithstanding the model’s generally accepted reliability (Baker, 2011; Wang et al., 2010). In order to produce more robust research, researchers typically modify the model’s elements to fit certain study contexts or combine the model with additional theories (e.g., Hsu et al., 2006; Oliveira & Martins, 2010; Wang et al., 2010; Zhu, Dong, et al., 2006; Zhu, Kraemer, et al., 2006). Different research environments have produced conflicting results for several constructs, suggesting the presence of other influences. The researchers agreed with Tornatzky and Fleischer (1990) that the three TOE contexts affect adoption, but they went on to presume that there are different elements or measurements that apply to each technology or setting that is being researched. Various factors affect the acceptance of different kinds of inventions. Thus various national and cultural contexts and industries will have various factors. As a result, different criteria for the technological, organizational, and environmental contexts are used in different research projects. The qualities and accessibility of current and emerging technologies that are significant to a corporation are included in the technological settings (Tornatzky & Fleischer, 1990). The organizational context describes the traits of an organization, such as its internal spare resources, organizational size, organizational structure, and communication procedures (Tornatzky & Fleischer, 1990). The environment in which an organization operates is described by the environmental context, which also includes industry features, governmental regulations, and external innovation infrastructure (Tornatzky & Fleischer, 1990). The characteristics of the company and its resources, such as the level of employees‘ technological proficiency, the managerial structure, the availability of resources, and the firm’s size, have all been considered as organizational context constructs in literature (Zhu & Kraemer, 2005; Lippert & Govindarajulu 2006; Baker, 2011; Matikiti et al., 2018; Eze et al., 2019); data security or the security of data collection and sharing, dependability, and the complexity of technology are all linked notions that have been studied in the past (Lippert & Govindarajulu, 2006; Lu et al., 2015; Chau et al., 2020) (Gangwar et al., 2015; Lu et al., 2015; Chau et al., 2020). In this study, the constructs have been inductively derived and categorized into technological factors related to complexity, safety, and resilience of AI systems and algorithms, environmental factors related to infrastructural constraints of access to technology and the competitive dynamics of the industry and the organizational factors related to communication, coordination, competency, ethical and financial challenges being faced by organizations in the adoption of AI for financial inclusion. Research Problem: the topic of challenges of adoption of AI by financial service providers (FSP) and users in literature is mostly addressed by conceptual and

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descriptive articles and there is a scarcity of empirical experiments and studies that can validate the propositions made. The gap of a theoretical approach or model for studying the challenges of AI adoption in literature is still to be addressed. A qualitatively developed model from the documentary evidence published in credible reports and articles would provide a framework for scholars and practitioners to tackle the challenges of AI adoption for financial inclusion. The challenges in adoption of AI have been published in various conceptual, conference and working papers, research articles, institutional reports and news articles from technological, human, regulatory, and social perspectives of Financial service providers (FSPs), users and regulatory and government agencies. An integrated and theoretically grounded framework would contribute to the development of an empirically validated and generalizable model of the adoption of AI for financial inclusion.

9.3 THE METHODOLOGY OF RESEARCH STUDY Unobtrusive research techniques like conceptual and documentary analysis are used in this study to identify and categorize the varied challenges associated with the adoption of AI technological solutions for financial inclusion. The main method of data gathering and analysis used in this study is document analysis. Documentary analysis was introduced by Glaser and Strauss (1967) as a type of qualitative methodology in which documents are examined to explore different study issues. Dissecting documents entails categorizing their content into different categories, which helps researchers comprehend social constructions and realities and draw insightful conclusions (Atkinson & Coffey, 2004; Denzin & Lincoln, 1994). Document analysis is a systematic process for assessing or evaluating documents, including printed and electronic (computer-based and Internet-transmitted) material, according to Bowen (2009, p. 27). Understanding and separating what was stated or written from what was implemented are made possible through document analysis. In order to generate meaning, obtain insight, and create empirical knowledge, necessitates the analysis and interpretation of data (Corbin & Strauss, 2008; see also Rapley, 2007). As a method of triangulation, document analysis is typically combined with other qualitative exploratory techniques (Denzin, 1970). It is used as a standalone technique of exploration as well, though. There are several specific types of qualitative research that exclusively rely on document analysis. For example, Wild, McMahon, Darlington, Liu, & Culley (2009) did a “diary study” that examined engineers’ information needs and document usage. They created new “document use” scenarios using the data and then tested a related software system’s “proof of concept” using those scenarios. Multiple (at least two) sources of data were accessed for methodical substantiation and corroboration of information (Yin, 1994). Documents used for this study comprised reports of reputed organizations or institutions, newspaper articles, reports of governmental regulatory bodies and think tanks, conceptual and systematic literature review studies published in high-quality journals and conceptual articles by scholars. Finding, choosing, setting (making meaning of), and synthesizing the data present in the documents was the logical process that was followed. Extracts, citations, and complete sections were used to create open codes, selected

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codes, and significant themes using content analysis to arrange the data obtained through document analysis (Labuschagne, 2003). The steps involved in the conceptual and document analysis of this study are as follows: setting inclusion criteria for documents; collecting documents; articulating key areas of analysis; document coding; verification; and analysis.

9.3.1 SETTING INCLUSION CRITERIA The authors considered the categories of documents to be included, the types of documents to be examined, and the dates of publication and distribution of those papers while establishing inclusion criteria for document selection for the study. The initial set of documents chosen included studies and reports from globally renowned consultancies, think tanks, and multinational organizations like the United Nations Development Program (UNDP), the Organization for Economic Cooperation and Development (OECD), and government regulatory bodies like the Reserve Bank of India (Central bank of India). Working papers by the International Monetary Fund, World Bank, and United Nations Development Program; journal articles published in top-tier indexed journals; Deloitte, Price Waterhouse Coopers, McKinsey, and Brookings Institute (Brookings, 2020). To be able to utilize the exercise as a “baseline” and follow the difficulties in the adoption of AI in more recent years, publication dates from 2000 onwards were taken into consideration. Two academic experts chose the practice and policy documents based on the standing and repute of the companies conducting the study.

9.3.2 COLLECTING DOCUMENTS Google keyword searches were used to gather public domain documents for the investigation. Secondary data search of documents was undertaken from google’s online search engine by using the keywords “Artificial Intelligence,” “adoption,” “challenges,” and “financial inclusion” and their synonyms with the Boolean operators “and/or.” The search was continued till point of saturation of content was reached, that is, when no new documents were meeting the selection criteria and the documents being extracted were repetitive of the content of the existing documents selected. Throughout the course of a thorough search that lasted many months, over 126 documents were examined, contextualized, and categorized for analysis (Bowen, 2003). Table 9.1 provides examples of the documents chosen and the data examined. Defining the main research areas. The original documents chosen were examined and assessed for relevance to the major areas of analysis that is, risks and challenges associated with the adoption of AI for financial inclusion. The full content of the TABLE 9.1 Types of Documents Selected for Review Journal Articles 36

Institutional Reports

Thesis/Working Papers

Newspaper Articles

Total

24

14

16

86

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extracted articles was reviewed by two academic experts to justify the selection of relevant content for analysis. The documentary evidence put an end to the investigations when it came to the difficulties and dangers of adopting AI and the related phenomena that were being investigated. Researchers and analysts extracted and analyzed data from files as part of theoretical sampling, which is “sampling on the premise of principles that have established theoretical significance to the evolving notion,” in addition to adding context richness to the studies (Glaser & Strauss, 2012).

9.3.3 DOCUMENT CODING

AND

ANALYSIS

Two academic experts reviewed each selected document’s content to ascertain how much the extracted content connected to or addressed each of the noted “themes” and “sub-themes” on problems of AI adoption in financial inclusion (i.e., areas of analysis). A qualitative data analysis method was used to highlight and code the text that related to each theme (grounded theory method). The study’s value is centered on this kind of qualitative analysis of content, meaning, and context relevance, which notably sets the research apart from a simple keyword search. Interview studies, ethnographic field notes, written personal experiences, and records are connected to grounded theory methodologies (Clarke, 1998). The content is coded using a constructivist grounded theory (Charmaz, 1995, 2000) approach. A focus on action and process, line-by-line initial coding, simultaneous coding and additional data collection, and an emphasis on analytic development rather than description were all used to code the collected data (Charmaz, 1995, 2000, 2001). Each code was inductively created line by line by segmenting bits of information into actions. The remainder of the data gathered during the second step of focused coding was coded using the original codes that showed up the most frequently (Strauss & Corbin, 1990, 1998). Using an inductive-deductive-inductive procedure, the 25 initially created open codes were then carefully coded by systematically finding those categories that have a close relationship to the core category (Table 9.2). Events, activities, and documents that were theoretically important were sampled until theoretical saturation was attained (Strauss, 1987).

9.3.4 THEORY DEVELOPED

FROM

LITERATURE

As additional categories developed inductively during the investigation, the growing model or theory was updated (Miles & Huberman, 1994). The initial theoretical model (Figure 9.1) was created by grouping the selection codes into the three thematic variables based on the framework previously proposed by Tornatzky and Fleischer, which used technology, organizations, and environments (1990). Verification: the analysis of each document was checked by two separate specialists to make sure the coding and assessment procedure was reliable and consistent. When the two experts couldn’t agree, a third expert offered ad hoc verification and acted as a judge for any discrepancies between the two main coders. According to Morrise et al. (2002), this was regarded satisfactory for coding reliability and ensured reliable interpretative analysis and conclusions.

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TABLE 9.2 Grounded Theory Method of Challenges of AI Adoption in Financial Inclusion Themes

Selective Codes

Environmental

Potential Digital Divide Risks due to Competition

Organizational

Coordination and Communication Challenges

Open Codes Digital Exclusion Competitive Advantage to Big Companies Risks due to Market Concentration. Lack Of Enterprise Data Management Operationalization and Maintenance ML models:

Talent Shortage

Skill sets and talent Explain ability of AI models Opaqueness of AI models: Technology Rabbit Hole Lack Of Domain Expertise

Ethics and Morality

Consumer Data Exploitation Data Privacy Noncompliance Lack of Accountability

Technological

Economic constraints

Budget Constraints Return on Investment

Data Quality

Data Quality Data Accuracy Data Relevance

Algorithmic biases

Biased or discriminatory outcomes of AI Models Structural Deficiencies Risk of biased and discriminatory models Inaccurate ML-based scoring due to faking indicators by Consumers

Cybersecurity

Biases due to dataset Tempering Attacks on Confidentiality of AI System Attacks on Integrity of AI System Attacks on availability of AI System

Source: developed for the study

Analysis: when this data were collected, it underwent analysis to identify the drivers and obstacles affecting the adoption of AI for financial inclusion. The three categories created for this study were used to group the excerpts from these documents. The continual comparative method (Glaser & Strauss, 1967), an inductive strategy for spotting patterns and spotting theoretical features in the data, served as the basis for the data analysis strategy. To organize ideas and identify concepts that seem to cluster together, the data is carefully examined and compared with other data as well as with codes. Codes are grouped into groups, then with information

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Technological Data Quality Algorithmic Bias Cyber Attacks

Organizational Co-ordination and Communication

AI adoption for Financial Inclusion

Talent Shortage Ethics and Morality Economic constraints

Environmental Potential digital divide Competition

FIGURE 9.1 Framework of Challenges for Adoption of AI for Financial Inclusion (Developed for the Study).

from documents. To clarify concepts, define conceptual boundaries, and validate the fit and relevance of categories, similarities, differences, and general patterns were explored in the data (Bowen, 2008, p. 144) (Charmaz, 2003). An initial list of coding categories was produced from a survey of the literature using an inductive and deductive technique. The model or theory was changed during the analysis when new categories and themes arose inductively (Miles & Huberman, 1994). The three initial themes which were selected for this study were deductively adopted from the TOE framework, that is, “technology,” “organizational,” and “environmental” challenges of AI adoption with Fintech for financial inclusion. The theme of organization-based challenges is a result of ethical or unethical actions of actors within the organization which are, for example, consumer data exploitation, agency hazards, coordination and communication challenges and talent shortage. The theme of technological challenges could be due to technological failure, algorithmic biases, data quality failures, and risks of cyber-attacks. The environmental factors could be systemic risks emerging from tacit collusion, market consolidation leading to unfair competitive advantage to large companies, and the digital divide.

9.3.5 VALIDITY

OF

FINDINGS

Integrity and dependability: a study is deemed “dependable” if, given the chance to examine the same group of documents under comparable circumstances, another reader would have “reached the same basic conclusion” (Altheide,1996). Each open code’s justification was described in full and with references to the publications.

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The documents were the only information source used in the scoring, which aided objectivity. Consistent interpretation of themes was made possible by the verification of coding by a second researcher and, in the event of a disagreement, a thirdparty adjudicator.

9.4 RESULTS AND DISCUSSION 9.4.1 SURFACING

THE

NARRATIVES: THE OPEN CODES

Open coding was the initial step in the grounded theory method (GTM). Each piece of chosen text was line-by-line coded by professionals. As the data collection and analysis progressed, the open codes changed numerous times. The two academic experts finalized open codes are listed below:

9.4.2 DIGITAL FINANCIAL INCLUSION The term “digital inclusion” describes the degree to which disparities in online activity, account ownership, and payment adoption exist across gender, class, and region. Digital financial inclusion is the use of computers and mobile devices to access and use formal financial services. They include mobile money, mobile banking, marketplace lending, and digital payments and lending/credit (Sahay et al. 2020). , Digital financial inclusion entails access to digital transactional platforms for formal financial service access, access to digital devices (mobile phones, etc.), and access to retail agents to enable financial services to be accessed via the digital transactional platform offered by banks and non-banks to the financially excluded and underserved (World Bank, 2014). The assumption behind digital financial inclusion is that a sizable portion of the excluded population will possess a digital device and that the delivery of financial services via computers and mobile devices will increase the excluded people’s access to finance (World Bank, 2014).

9.4.3 COMPETITIVE ADVANTAGE

TO

BIG COMPANIES

Big companies can gain competitive advantage through access to customer data and big data not normally available to smaller companies. Their competitive advantage is further reinforced with their use of AI to offer novel, customized, and more efficient services and products with the customer data. Big companies can leverage their higher resources and number of customers to access better quantity and quality of data and use it to deploy better AI-based models than smaller companies thus blocking access of potential consumers to range of financial services which would have been possible with larger number of players participation in the financial services market.

9.4.4 RISKS DUE

TO

MARKET CONCENTRATION

Depending on their size and breadth, a small number of dominant BigTech companies may have an impact on the systemic stability of the market (DAF/CMF (2019)29/REV1) (FSB, 2020).

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Due to possible market concentration and anti-competitive behavior by a small number of key players in markets for AI solutions and/or services incorporating AI technologies (such as cloud computing service providers that also offer AI services), it may be difficult to access and audit the financial activities provided by such firms (ACPR, 2018). The use of customer data by BigTech players to establish monopolistic positions would also pose challenges for the competitive environment, both in terms of client acquisition (e.g., through effective price discrimination) and through the introduction of high entry barriers for smaller players. Due to the limited engagement of service providers, this may have detrimental effects on financial inclusion. Enterprise data management is lacking in the financial industry, which processes enormous amounts of data generated both internally and externally. Because data must be shared among a network of peers and data sovereignty controls are required to deal with sensitive personally identifiable information (PII) data of consumers, the absence of a proper data management framework can result in siloed and undisciplined processes throughout the company, potentially resulting in massive data breaches and noncompliance. Most businesses using AI-based models struggle to create efficient enterprise data management solutions. Companies frequently lack the personnel and skill sets needed to put AI ambitions into action. According to Gartner (2018), through 2022, 85% of AI projects would produce inaccurate results, mostly because of skill gaps. Project management, business analysis, data science, data engineering, domain expertise, user interface (UI) designing, software development, and change management are the core competencies needed for AI deployment. Organizational skill gaps could prevent AI-based models for financial inclusion from being implemented effectively, result in improper AIdriven financial technology implementation, and potentially prevent vulnerable populations from accessing financial services due to biased model results or raise organizational risk due to incorrect client credit risk predictions.

9.4.5 ML MODEL OPERATIONALIZATION

AND

UPKEEP

The production deployment pipeline for an organization only includes a small portion of the ML models created by data scientists. Data scientists must work with teams from other disciplinary fields, such as engineering, operations, and business, to get an AI model to perform. In order to ensure proper collaboration, coordination, and communication across all stakeholders, this creates organizational obstacles. Data scientists should be spending their time creating and testing new algorithms rather than performing labor-intensive manual tasks like data retrieval and cleaning. Extensive testing, model packaging, production deployment, and continuing maintenance are required for AI models. Nevertheless, most businesses aren’t yet prepared to make this happen (PWC, 2019). As a result of poor testing and validation of the new AI models, this could have negative effects on the outcomes related to financial inclusion.

9.4.6 LACK

OF

EXPLAIN ABILITY

OF

AI MODELS

Because the underlying AI algorithms are complex and the input-output relationships are unclear, it may be unsettling for both customers and staff to adopt

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AI-driven decision-making. For instance, banks’ adoption of straightforward algorithms based on linear models, such as linear and logistic regression, is understandable given that the algorithm’s operation can be deduced. Nevertheless, the “lack of explain ability” of AI-based models makes it impossible to understand, follow, or replicate the decision-making process, which poses problems for lending and causes the model to use data improperly or inappropriately because it is hard to identify the reasons why. Additionally, since the approach makes it difficult to uncover any potential bias in credit allocation, lenders are unable to explain how a credit decision has been reached, and consumers are powerless to address apparent prejudice or take efforts to improve their credit rating. (Brookings, 2020).

9.4.7 AI MODELS‘ OPACITY ML (a type of AI) based credit rating models consider several risk variables, making it challenging to determine which element predominates in the final credit rating decision. This might result in immoral judgments and a lack of oversight. The opaqueness of ML-based credit ratings may also result in unintentional bias in results and potential financial exclusion of creditworthy customers, thus posing a risk to consumer protection (Fuster et al., 2018).

9.4.8 TECHNOLOGY RABBIT HOLE AI teams frequently make the mistake of employing machine language (ML) experts who develop proof of concept AI models without comprehending the business issues that the technology can address or without verifying whether the model is applicable in many usage scenarios. This could result in skewed credit appraisal results and flawed applicant selection for goods and services (Forbes, 2022).

9.4.9 LACK

OF

DOMAIN EXPERTISE

The core AI team is made up of professionals in technical fields including data engineers, ML engineers, and data scientists who can create technically complex solutions but may not always solve the business issue. The AI team needs to be continuously guided by domain experts that are familiar with the business context and understand business needs, which is typically not the case, leading to ineffective and sub-optimal suggestions connected to decisions that could negatively influence financial inclusion.

9.4.10 EXPLOITATION

OF

CONSUMER DATA

Organizations and their workers exchange and utilize customers’ financial and nonfinancial data with their knowledge and informed consent (US Treasury, 2018). Even if informed consent is obtained, which is the legal basis for any use of data, consumers are not adequately informed or competent regarding how their data is

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being managed or used, and as a result consent may not be properly given. This is according to a report to President Donald J. Trump and Executive Order 13772 (Core Principles for Regulating the United States Financial System). Consumer internet activity tracking and data sharing by third-party service providers raise the possibility of privacy and data protection legislation infractions, which could have detrimental effects on the financially excluded.

9.4.11 PROTECTING CONSUMER INFORMATION The data utilized for AI modeling is gathered from sizable data sources (such as social networks and the internet of things), which could lead to the exclusion of specific consumer groups due to the usage of their personal data and the technology’s illogical judgments (see Fuster et al., 2018). Historical data may no longer be valid for risk assessment of new clients due to significant structural changes because of environmental changes (for instance, changes in financial development, macroeconomic policies, and industrial changes) (see Rajan, Seru, and Vig, 2010). To prevent these big changes from having a negative impact on creditors, the AI modeler would need to recognize them and retrain the taught models of risk assessment.

9.4.12 NONCOMPLIANCE The AI team is largely unaware of compliance mandates and does not assess whether the data they are using is following those mandates, especially when autonomous decision-making systems are involved, as is the case for example the Equal Credit Opportunity Act (ECOA), the California Consumer Privacy Act (CCPA), and the General Data Protection Regulation (GDPR) in the USA. Without a compliance evaluation, the monetization of data and insights from AI projects might result in lawsuits, harm the organization’s reputation, and have a negative influence on its financial inclusion programs (Forbes, 2022).

9.4.13 BUDGET CONSTRAINTS Very small budgetary allocations are made by organizations for AI projects irrespective of whether AI projects are categorized as IT project, a change management project, or an innovation project. According to a study by the Economist Intelligence Unit 21, 86% of FSP (Financial service providers) aim to increase their investment in AI over the next five years, especially from the Asia-Pacific region (90%) and North America (89%). However, budgetary constraints hamper the effective deployment of AI-based systems for accessibility of financial services to the constrained segments of population.

9.4.14 LACK

OF

ACCOUNTABILITY

There is a tangible lack of accountability as regards decision-making by AI models. Organizations tend to blame the complex and sophisticated algorithms making the

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decisions and regulators find it increasingly complex to monitor. Hence, accountability of adverse outcomes by autonomous decision-making systems is complicated and difficult to establish and hence decisions adversely affecting the financially excluded may not be corrected or addressed.

9.4.15 RETURN

ON INVESTMENT

AI-based systems have a low likelihood of instant returns which hampers the utilization and growth of AI financial services thus affecting the financial inclusion goals set by organizations and governments. A McKinsey State of AI 2020 report found that just 22% of organizations reported quantified value from AI as the benefits are not quantifiable or attributable to AI. For example, time savings, higher employee morale, or brand value are unquantifiable benefits of AI due to which FSPs may not adequately invest in AI affecting their ability to achieve their financial inclusion goals (Forbes, 2021).

9.4.16 DATA QUALITY The performance of AI models is influenced by the quality of the input data because poor data will result in poor output and predictions. A survey found that, compared to model selection, training, and deployment, AI teams spend roughly 39% more time cleaning and preparing the data (Bias in AI isn’t a business priority, but it should be, survey cautions | VentureBeat). Data preparation involves standardization, validation, enrichment, de-duplication, and consolidation of data in the format appropriate for the model. Semi-structured or unstructured datasets frequently contain FSP data. 80% of banking data, including emails, log files, market data, regulatory reports and filings, and earnings call records, is unstructured, according to a research study (Unlocking the potential of unstructured data in banking FinTech Futures). Off-the-shelf data quality products can only do type checks and null checks, resolve data drift, and provide a small data profile (Forbes, 2022). So, low-quality data is likely to have a negative effect on organizations’ financial inclusion activities and programs.

9.4.17 DATA ACCURACY Big data is employed by AI systems for financial inclusion modeling. Big data’s veracity is the degree to which it is believed to be accurate (IBM, 2020). This belief may be based on the dubious source reliability, insufficient quality, or unsuitable nature of the data employed. Big data’s veracity may be impacted by particular behaviors (such as those found on social networks) or by noisy or biased data collection technologies (e.g., sensors, IoT). Data representativeness refers to whether the population and pertinent subpopulations under study are represented by the data utilized, which could prevent over- or under-representation of operator groups in the financial markets and result in more accurate model training. It could help promote minority financial inclusion in credit scoring.

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9.4.18 DATA RELEVANCE Data relevance involves the use of relevant signals and correct labeling and structuring of big data so that ML models can distinguish signal from noise and recognize patterns in data used to describe the phenomenon being studied (S&P, 2019). The utilization of data that accurately describes the current event without introducing exogenous (erroneous) information is referred to as data relevance. For instance, in credit scoring, the appropriateness of data on the conduct and/or reputation of natural individuals (for legal persons) should be thoroughly evaluated before the model’s inclusion and application. Given the enormous volume of data involved, evaluating the dataset used on a case-by-case basis to enhance the accuracy and appropriateness of the data used may be laborious, and it may also limit the savings provided by the deployment of AI (OECD, 2021)

9.4.19 BIASED

OR

DISCRIMINATORY OUTCOMES

OF

AI MODELS

Well-intentioned but poorly constructed and controlled ML models have the potential to unintentionally produce biased results, discriminating against groups of people who are legally protected (based, e.g., on race, sex, or religion), or perpetuate pre-existing biases. For instance, algorithms may aggregate facially neutral data elements and use them as stand-ins for fixed traits like race or gender, flouting current anti-discrimination legislation (Hurley, 2017]). The algorithm can infer gender based on transaction activity and apply this information in the assessment of creditworthiness, which would result in gender-based bias, even when a credit officer may deliberately eliminate gender-based variants as input. The usefulness of the AI models to increase financial inclusivity may be hampered by biases that are inherent in the data and variables used for training the model, which may have historical biases integrated in the data used to train it (OECD, 2021).

9.4.20 STRUCTURE-RELATED ISSUES To reflect implicit information like business knowledge, intuition, and predictions of future events not realized in the data, the basic algorithms of ML models cannot be simply adjusted (see Rajan, Seru, and Vig, 2010). For instance, the fundamental algorithms of ensemble tree-based models like Random Forest are too stiff to include implicit knowledge because they build multiple trees and choose characteristics at random. Like this, deep neural networks (NNs) have few modeling options since they have several parameters that have no obvious relevance to any specific piece of information (Bazzardash, 2019). Models could be unable to stray from the goal for which they were developed since machine learning algorithms are made to solve problems. For instance, algorithms created to spot fraudulent payment behavior won’t find suspicious conduct involving business transactions. Algorithms may need to be improved on a recurring basis to accommodate changing requirements in the setting of financial institutions and the legislation designed to oversee them (Rajan, Seru, and Vig) (2010). A broader AI model that can address various use cases using a single, adaptable algorithm is also required as

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a result of this (PWC, 2022). As a result, even well-designed ML models may lose the ability to accurately determine the creditworthiness of potential consumers and their usefulness in accomplishing the organization’s financial inclusion goals.

9.4.21 MODELS THAT ARE BIASED

AND

PREJUDICED

The notion of generalizability is broken when ML models are trained with data that may not precisely reflect all classes of borrowers that the creditor deems deserving of lending to. Absence of sufficient and pertinent data for some classes would limit the ML analysis’s conclusions and might result in the exclusion of applicants who belong to a particular population group. For instance, if a raw sample is used to make predictions about the default behavior of underserved borrowers, a class of borrowers who have historically been denied credit for non-business reasons (e.g., gender, race, religion, ethnicity, residence in certain areas, etc.) may be unfairly subject to financial exclusion and poor credit rating prediction. Variables like gender, color, and religion should be left out of the model because they can lead to bias. Yet, using such characteristics to assess bias using a scoring model can help prevent prejudice, (Bazzardash, 2019).

9.4.22 INACCURATE ML-BASED SCORING DUE CONSUMERS

TO

FAKING INDICATORS

BY

A borrower may purposefully alter the value of an indicator that would otherwise have a negative impact on their credit score. For instance, if social media is included as a high character score in the AI model, potential customers can strengthen their social media connections even though it is not a reliable indicator of the creditworthiness being evaluated. A model trained with outdated data may produce inaccurate credit scoring results for new customers since the value of the variable used as an indicator of the borrower’s creditworthiness may vary over time due to existing customers’ willful or inadvertent conduct. Biases resulting from biased datasets: self-learning AI models constantly adjust their algorithms based on the data that is supplied into the system. As a result, the model may disregard the veracity of the data and produce biased results. The risk of creating bubbles related to systemic risk in the financial system and ineffective results for financial inclusivity is demonstrated by Malik (2020) who demonstrates how the feedback loop between data and an updating ML algorithm creates a “selffulfilling prophecy”: the ML system overestimates its prediction accuracy, and its (human) users over-rely on the system predictions. Attacks on secrecy: when AI is implemented, systems are vulnerable to misuse and hostile attacks that are aimed at compromising the confidentiality, integrity, and availability of the AI system (PWC, 2019). Membership and attributes Inference attacks aim to breach an AI system’s secrecy to gain access to data for more specialized assaults. Attacks that use inference attempt to deduce the algorithm as well as training attributes and training data. Attackers can utilize the dataset to train the model, create their own “copy model” of the model, then create their own “attack” using this “copy model.” As part of the open-source movement, datasets

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can be accessible by hacking the system storing them, or attackers can create their own comparable dataset that can be used to create a “copy model” (Harvard Kennedy school wp, 2019). Attackers may then misuse the information they have gained, thereby impacting organizations’ financial inclusion initiatives. For instance, if an attacker is interested in learning how a Financial Service Provider (FSP) determines that you are a member of a target audience, let’s say a group of women in a particular area. We can alter our online behavior to show you a specific commercial, for example, by trying to find information about dippers and determining whether we are seeing advertisements geared to the specific demographic of women (Polyakov, 2019). Attacks on integrity: attackers may aim for an AI system’s dependability (using adversarial or modified inputs) during testing or production, which could have a negative impact on the AI system’s results. Attacks known as “data poisoning” (Barreno et al., 2006) entail altering or erasing training data or introducing hostile samples to logically shift the learning process. These can take the form of input feature manipulation (manipulating the labels as well as the input features of training points analyzed by the learning algorithm if the adversaries have knowledge of the learning algorithm), label manipulation (modifying the training labels with some or all knowledge of the target model), and Qui et al. (2019). The outcomes generated by the system may be biased towards particular population subsets or creditworthy clients if the integrity of the AI systems is compromised, increasing the risk to the company and its aspirations for financial inclusion.

9.4.23 ATTACK

ON

AVAILABILITY

Adversaries can seize control of the model through adversarial reprogramming and manipulate its operation, or through malicious attacks to deploy the model even if an organization is not using it, or with ML models having higher speed, precision, and accuracy thus manipulating the outcomes of the AI model which may not benefit underserved or unserved population segments while increasing the regulatory and financial risk for the organization (PWC, 2019).

9.4.24 INVESTIGATING

THE

NARRATIVES CHOOSING CODES

To attain a greater level of abstraction, the open codes outlined in the preceding section were aggregated into selective codes. Under the framework of technology, organization, and environment, the selective codes were established by both deductive and inductive methods. In order to make them more abstract, two of the open codes were changed into selective codes. Eight selective codes were created by combining twenty-five open codes. Seven selected codes were produced from the conversion of the open codes, and they are explained below.

9.4.25 DIGITAL DIVIDE POSSIBILITY The gap between population groups and geographic areas with access to contemporary information and communication technology (ICT) and those without it or

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with restricted access is known as the “digital divide.” Telephone, television, personal computers, and internet connectivity are examples of this technology. Consumers might not be able to use the banking systems if they don’t have access to current personal devices (such PCs, cellphones, and tablets), internet connectivity, and ICT skills. It’s possible that those from lower socioeconomic levels can no longer access financial services. The epidemic, according to the World Economic Forum, made the digital divide worse for the estimated 55% of the global population that is still not online. Due to high levels of illiteracy and low income brought on by the concentration of economic activity in urban areas, 31% of adults in Southeast Asia, or about 150 million people, are not online. Those who are excluded from the formal digital economy frequently lack access to technology, are more likely to pay greater fees and be exposed to more intermediaries and riskier systems. Also, they struggle to completely prepare their kids for the digital world. Risks of competition: high market concentrations may limit consumers’ ability to make informed decisions because smaller financial services providers may be excluded from the market because they lack the financial and human resources needed to adopt internal AI/ML techniques or have unequal access to big data sources (US Treasury, 2018). The risk of concentration and dependence on a small number of dominant players is increased by the possibility of network effects, which could lead to the emergence of new systemic risks as a result of market concentration (OECD, 2021) . Without a formal agreement or any human contact, the widespread deployment of AI-based models may also lead to a covert agreement to maintain higher profits (Barclay Hedge, 2018). Higher market concentration by larger players and less ideal outcomes for financial inclusivity may result from high transparency and frequent interaction caused by the dynamic adaptive capacity of self-learning and deep learning AI models that recognize the mutual interdependencies and adapt to their behavior and actions of other market participants or other AI models without explicit human intervention (OECD, 2017).

9.4.26 CHALLENGES

IN

COORDINATION

AND

COMMUNICATION

Even though significant investments and resources may be needed for data infrastructure, AI software tools, data expertise, and model development, AI systems are incorrectly thought of as a plug-and-play technology with quick results. While leaders attempt to match a company’s culture, structure, and ways of working to facilitate widespread AI deployment, FSPs struggle to transition from the pilots to corporate-wide programs. To lessen the fear of failure and hasten development and scale up, one needs a test-and-learn culture where failures are reframed as sources of discoveries (HBR, 2019). The question of how analytical procedures scale is more influenced by a company’s talents, capabilities, and data than by the approaches themselves. Businesses sometimes overlook the “final mile,” which involves integrating the output of AI models into frontline operations, as well as the “first mile,” which involves challenges of how to gather and organize. As a result, AI initiatives frequently fall short of achieving scale and value for the company, making it challenging for FSPs to use AI effectively for financial inclusion.

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Lack of talent: human involvement in AI-based decision-making processes is essential for detecting and correcting biases in data or model design as well as for explaining the model’s output (US Treasury, 2018). Due to the difficulty of observing prejudice in the model’s output and decision-making processes and the talent scarcity that most FSPs experience, human intervention is essential for reducing the hazards of biased models (McKendrick J, 2020). According to Gartner’s forecast, talent gaps would cause 85% of AI initiatives to provide inaccurate results by the year 2022 (Gartner, 2018). Rich data and effective algorithms require talented personnel to combine them, and traditional financial institutions have trouble implementing AI approaches that integrate or replace them with existing solutions because the new process calls for different talents and extensive reorganizations. Traditional financial organizations’ resistance to change is another factor in their failure to adopt AI or to adopt it with acquisitions from other sources. Because AI-based models are inherently complicated, it can be challenging for FSP personnel to explain their workings to end-user clients. Deep neural networks and reinforcement learning algorithms are two examples of sophisticated AI tools. Coders themselves might not comprehend how the algorithms work. Also, there is a mismatch between the complexity of AI models and human reasoning and interpreting abilities (Burrell, 2016). Financial consumers, regulators, and supervisors have little faith in AI-powered approaches since they are highly opaque and lack “clear declarative knowledge” (Holzinger, 2018), especially in the case of crucial financial services (Financial Stability Board, 2017). The GDPR in the EU19, which is used in insurance pricing or credit decision-making, may not be applicable to FSPs, as it gives citizens a “right to explanation” for judgments made by algorithms and information on the logic involved (2021). The deployment of AI technologies in the financial industry and organizational programs for financial inclusivity may be hampered by a lack of explain ability. Butaru et al. (2016) provide an example of how the use of AI algorithms for credit distribution can make the entire process opaque and possibly unsustainable, which undermines public confidence in AI and prevents widespread implementation.

9.4.27 MORALITY

AND

ETHICS

The field of AI ethics is very broad and constantly changing (Kazim, 2021). With the growing use of AI systems, ethical and moral concepts including nondiscrimination, autonomy, and distributive fairness are vulnerable to infringement. For instance, information gathered for insurance purposes in one setting could possibly be utilized improperly in other settings. Together with vast datasets and related AI skills, analytics capabilities could infer information about protected traits like gender, ethnicity, or religion. This could help FSPs detect and distinguish between groups, possibly excluding some groups. The ability of people to make their own decisions may be restricted by intensive surveillance mixed with proactive interventions on the part of the insurance company. While AI algorithms and information may not be visible and comprehensible, the distribution of premium costs throughout a community of customers may not be equitable or that fairness may not be proven. According to research done for the UK Financial Consumer

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Panel, most customers rarely read the terms and conditions or privacy warnings before giving their assent, or if they did, they didn’t comprehend them (fscp, 2018). More than 75% of customers say they do not feel informed when they read terms and conditions, according to research done by the Financial Services Consumer Panel in the UK in 2018 (https://www.fs-cp.org.uk/sites/default/files/final position paper - consenting adults - 20180419 0.pdf). The likelihood of consumer rights being violated is raised by market-driven risks, abuse of unfamiliar products, new types of fraud, a lack of security, and faster transaction times (e.g., right to clear information, right to be protected from misleading or false advertising). Financial exclusion might result from a lack of privacy and secrecy, inappropriate use of digital profiling, and other factors. The possibility of violating moral and ethical standards when delivering AI-based financial services for financial inclusion is increased by regulatory and supervisory risks like unequal protection levels within or across jurisdictions, noncompliance with financial services providers‘ obligations for negotiation, and pre-formulated contracts (OECD, 2021).

9.4.28 FINANCIAL LIMITATIONS More than any other aspect, cost considerations are holding back the adoption of AI. The second and third reasons for not using AI more widely inside a business are insufficient infrastructure and poor data quality, respectively (Forbes, 2020). According to a research by the Economist Intelligence Unit 21, FS executives from the Asia-Pacific region (90%) and North America (89%) are most likely to boost their investment in AI over the next five years. Budgetary restrictions and the difficulty of calculating and reaching return on Investment (ROI) targets are slowing down the adoption of AI, particularly by smaller businesses. According to the European Commission (2020), there is a correlation between AI use and firm size (European Commission, 2020).

9.4.29 DATA RELIABILITY According to the World Economic Forum’s survey from 2020, the two primary obstacles to the use of AI are the absence of sufficient quality and quantity of data and the shortage of AI-related skills. Several AI applications in the banking industry rely on the mixing of conventional data with alternative data sources, which are still hard to come by and are not properly integrated with traditional financial data. Also, the variety of financial applications and duties necessitate a careful evaluation of the top choices (Onay and Zürk, 2018). The use of incomplete or inaccurate data may exacerbate biases identified through insider knowledge; erroneous models may be constructed using such biased data or through the discovery of spurious correlations (US Treasury, 2018), which may also lead to biased conclusions and discrimination (White & Case, 2017). With internal information utilized as a variable and outside information that can have favorable biases or perpetuate ingrained biases, biases can be inherent. Biases that currently exist in society and are taken into account in such databases may be perpetuated by AI-based systems (OECD, 2021). The possibility for discrimination exists even in well-intentioned AI systems that are biased due to

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inadequate or unrepresentative datasets, incomplete or racialized datasets, or AI training techniques that conceal how data is used in choices. Algorithmic prejudices in marketplaces that are prone to implicit and explicit biases, algorithmic decision-making can lessen face-to-face discrimination, but it can also result in unintentional discrimination (Barocas and Selbst, 2016). Poorly built ML models may unintentionally provide biased results, discriminate against groups of people who are legally protected (based, for example, on race, sex, or religion), or amplify biases already present in the data (PWC, 2021). By producing cycles that perpetuate biased credit allocation while making it harder to detect prejudice in lending, AI can amplify already existing bias. Unpredictable and changing market conditions could make it challenging to predict how models will react, with portfolio and macro implications post-training as continuous learning causes a drift towards discrimination as AI systems may pick up new behaviors through self-improvement and learning leading to unintended consequences. For instance, if an online lending platform started denying loan requests from women or members of racial or ethnic minorities more frequently than those from other groups based on how they had previously sought out credit (Deloitte, 2021). . For instance, race or gender information may be included in insurance premiums or credit choices when developing AI models since unconscious prejudice or a lack of diversity among development teams may have an impact on how AI is trained, carrying bias further into the model. Across the world, non-governmental organizations (NGOs), academic institutions, and multilateral organizations are attempting to better characterize AI bias and provide guiding principles and procedures to lessen it.

9.4.30 CYBERSECURITY Malicious hackers may launch aggressive cyber-attacks against ML models. The application of AI opens additional attack surfaces and exposes systems to new avenues for malevolent actors to exploit and abuse. These assaults aim to compromise the AI system’s availability, secrecy, and integrity. Hackers can conduct adversarial attacks to confuse or manipulate AI systems to their benefit in addition to assaults focused at stealing the data used by AI algorithms. As a result, consumers must voluntarily choose between more privacy and individualized service. When compared to clients who agree to the sharing of private data, those who place a high value on privacy would provide less data points to the AI models, resulting in a less tailored (and more general) service (PWC, 2019). Cyber-attacks may thereby impair AI’s ability to help organizations achieve their goals of financial inclusion.

9.4.31 STITCHING

THE

NARRATIVES

Theoretical Coding: the open and selective coding of the extracted text from the selected documents (Table 9.2) were then organized into a theoretical model based on the T-O-E framework (Figure 9.1). The technological factors of data quality, algorithmic bias, and cyber-attacks make the outcomes of AI systems-based decisions prone to intended and unintended bias in their outcomes due to malicious attacks by hackers, poor quality or inadequate data used to train the system, poorly

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developed AI systems by AI staff who may fail to mitigate the bias inadvertently designed into the system, macro environment and social changes which may make the well-designed AI system ineffective, and new behaviors which may be acquired by self-learning by AI models. This may lead to erosion of trust and discriminatory decisions by FSPs making them liable for regulatory and legal liabilities. Organizational factors of skill gaps, coordination and communication deficiencies, ethical and moral challenges in implementation of AI-based systems decisions, and budgetary and financial constraints may also inhibit the adoption of AI systems for financial inclusion by FSPs. Environmental factors related to the potential digital divide in the population may make digital financial inclusion difficult for some subpopulations due to digital divide, inadequate digital literacy, and discriminatory systems with high risk or costs of access to formal financial services. Systemic risks on account of market consolidation, tacit collusion, and unfair competitive advantage to large companies may further deprive the financially excluded from choice and competitive offerings by FSPs.

9.4.32 CONCLUSIONS

AND

RECOMMENDATIONS

The postulates of this contribution corroborate the research works on AI and FinTech to provide opportunities for financial inclusion. Its objective is to identify the challenges associated with the adoption of AI solutions in the financial inclusion of the excluded. We use unobtrusive techniques methods of documentary research and case study to identify the challenges and identify consumer protection challenges, data protection and cyber-attacks, risks related to the reduction of competition, irresponsible deployment of AI and the risk of fueling the digital divide, exclusion, and displacements. The study concludes that the adoption of AI does not significantly contribute to financial inclusion if the challenges are not properly managed. It is important that policymakers in partnership with the private sector prioritize the development of digital infrastructure to allow insertion of the AI for the financial inclusion and respective economic and social development plans. This study is the first attempt to construct a theoretical model of challenges of AI for financial inclusion. A framework of challenges of AI adoption for financial inclusion is developed using an inductive-deductive approach, document and content analysis method of data extraction, and a grounded theory method of data analysis to code the selected content into 25 open codes, 8 selective codes, and 3 themes of technology-organization-environment using the TOE framework. The study contributes to the structuring of the factors of AI adoption for financial inclusion which can contribute to better decision-making and risk mitigation by FSPs. Further empirical research by scholars can validate the theoretical model proposed in this study. The study thus contributes to the theoretical development of a model of organizational adoption of AI for financial inclusion by adapting the T-O-E framework. Scholars can further develop an empirically validated model of AI for financial inclusion. Practitioners can enhance the effectiveness of AI adoption in their

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organization by tackling the identified challenges of AI adoption towards achieving the financial inclusiveness goals of their organization. The model can be used by policymakers and regulators to check if the use of AI to financial inclusion is consistent with fostering financial stability, safeguarding financial consumers, and fostering competition and market integrity. Developing dangers resulting from the application of AI techniques can be recognized and reduced to allow for the implementation of responsible AI without impeding innovation. To address the perceived hazards and incompatibilities of current regulations governing AI applications, existing regulatory and supervisory standards may need to be adjusted and fine-tuned.

9.5 LIMITATIONS The study is limited by the sample of documents selected for the study which is predominantly online reports, articles, thesis, working papers, and news reports and may not be fully representative of the factors determining AI adoption for financial inclusion.

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Index Account, 2, 6, 7, 9, 11 Aggregator, 115 AI, 1, 2, 3, 4, 5, 6, 7, 8, 9, 82, 88, 94, 95, 96, 99, 102, 114 AI adoption, 135, 137, 139, 140, 141, 155 AI applications, 135, 155 AI-based systems, 145, 146, 152, 154 AI modelling, 145 AI professionals, 8 AI solutions, 134, 143 AI technologies, 86, 102, 106 Algorithmic, 99, 101, 107 Algorithmic Bias, 135, 141 Algorithmic computation, 134, 135 Anonymity, 50–51 Augmented Reality, 106 Bank, 2, 6, 7, 8 banking industry, 101 banked adults, 7, 11 Banking, 2, 4, 6, 8, 40, 41, 46, 47, 72, 73, 78, 142, 146, 150, 152 Banking/banks, 45–47, 49 Based capital market, 45, 48 Bias, 5, 9, 11 Bibliographic coupling, 88 Bibliometric, 83, 87, 88, 89, 98 Big data, 2–9, 11, 12, 134, 146, 147, 150, 114 Big Data Analytics, 115 Bitcoin, 44–45, 49 Blockchain, 3, 5, 9, 12, 82, 94, 100, 104 Blockchain market, 40, 47–48, 50 Blockchain technology, 9, 114 Bradford’s law, 90 Business models, 94, 100, 105, 106, 107 Capital market, 40–41, 48, 51 Citation analysis, 88 Client, 4, 5, 6, 8 Cloud computing, 101, 105 Clusters and Business Models, 115 Co-citation network, 85, 89, 98, 99, 105 Companies, 46 Consumer Data Exploitation, 134, 135, 140, 141 Co-word analysis, 88 Credit, 2, 3, 4, 6, 7, 8 credit score, 4 credit risk, 6, 12 Credit risk, 83, 86, 100, 107 Credit scores, 4 Credit scoring models, 115, 133, 135, 147, 148

Creditworthiness, 3 Crowdfunding, 46, 51, 120 Cryptocurrencies, 95, 96, 104 Cryptocurrency platforms, 44–46, 49 Cryptography-based business protocol, 44 Customer(s), 3, 5, 6, 8, 69, 71, 72, 73, 74, 75, 76, 77, 78 customers’ choice, 70, 73 customers’ intention, 72, 73 Customer data, 8 Cyber security, 135, 140 Cyber-attacks, 134, 135, 141, 153, 154 Data analytics, 3, 4, 5, 105, 114 Data privacy, 107 Data protection, 8 Decentralized network, 44, 47, 50 Deep neural networks, 14, 151 Digital divide, 140, 141, 149, 154 Digital finance, 2, 14, 134 Digital Financial, 115 Digital financial inclusion, 3 Digital Financial Innovations, 120 Digital Financial Literacy, 120 Digital financing, 2, 112 Digital Lending Models, 115 Digital market, 44 Digital technology, 4 Digitalization, 95, 98, 102, 105 Disintermediating centralized platforms, 41, 44–45, 50–51 Distributed ledger technology/DLT, 39, 41, 43, 44, 45, 46–47, 51 Document analysis, 134, 137, 138 e-Money, 115 Economic, 41–43, 45 Economic Growth, 112 Efficiency, 41–43 Electronic, 44, 48–49 Electronic Know Your Customer (e-KYC), 115 EMH, 42–43 Entrepreneurship, 98, 103 Equilibrium, 41–42 Ethereum, 30, 31 Exclusion/excluded, 41, 45, 50–51 Factors, 71, 72, 73, 74, 78 Finance, 41–43, 45–48, 51, 52 Financial architecture, 44

161

162

Index

data, 45 inclusion, 3, 5, 6, 7, 11, 40 innovation, 40, 43, 44, 45 institutions, 6, 8, 9, 12 market, 40, 43, 44 products, 7, 11 regulation, 40, 47 sector, 39, 40, 41, 47 services, 4, 5, 7, 10, 11, 40, 43, 44, 45, 46, 47 stability, 40, 43, 45, 48 system, 43 technology, 46, 47 Financial consumers, 151, 155 Financial data, 142, 152 Financial digital innovation, 115 financial ecosystem, 95, 100, 104, 105 Financial exclusion, 113 Financial inclusion, 83, 84, 85, 86, 87, 89, 95, 97, 99, 100, 101, 102, 104, 105, 106, 107, 112, 113, 115 Financial institutions, 112, 113, 115, 133, 134, 135, 147, 151 Financial market, 44, 50 Financial planners, 115 Financial service providers, 115, 135, 136, 145 Financial services, 1, 3, 4, 5, 8, 82, 83, 85, 87, 95, 96, 100, 101, 102, 103, 105, 106, 134, 135, 143, 146, 150, 152, 154 Financial system, 3, 4, 6, 7, 8, 41 Financial technologies, 87, 94, 95, 97, 100, 101, 104, 105 Financial technology (Fintech), 113 Financing agents, 115 Fintech, 2, 3, 39–43, 45, 46–49, 51, 71, 72, 73, 74, 75, 76, 77, 78, 79, 82, 83, 84, 85, 86, 87, 89, 92, 94, 95, 96, 98, 100, 101, 102, 103, 104, 105, 113, 115, 141, 146, 154 Fintech Companies, 125 Fintech Investment, 125 Fintech Sector, 115 Formal Financial Services Market, 112 Formal Financial System, 113 Fraud, 6 Funding agents, 115

Industry 4.0, 98, 105 Inexpensive Financial Services, 112 Information, 41–44 Innovation, 39, 40, 41, 42, 43, 44, 45–49, 112 hub, 41, 42, 43, 48 Insurance Broker Marketplace, 115 Insurtech, 115 Intellectual structure, 88, 105 Investments, 112 Islamic Finance Industry, 113, 114 Islamic Financial Technology, 114 Islamic Fintech, 112, 113, 114 Businesses, 113 Platforms, 114 Sector, 114 Startups, 114

Grounded Theory, 134, 139, 140, 142, 154

Objects, 44 OJK Otoritas Jasa Keuangan Financial Services Authority, 115, 120 Online Distress Solution, 115 Online Gold Depository, 115

Hazards, 8 Hub, 41, 42, 43, 48 Immutability, 41, 45, 50–51 Inclusion, 40–41, 45–51 Inclusive Financial System, 112 Indonesia, 112 Inductive-deductive-inductive, 135

Know your customer (KYC), 46 Literature, 41–42 Low-Income Group, 112, 150 Low-level-income people, 45 Machine learning, 83, 86, 94, 100, 102, 107 Market, 40, 41, 43, 44–45, 47, 50–51 Market hypothesis, 40, 42–43, 45, 47 Mechanism, 41–42 Microeconomic, 42 Microfinance, 94, 95, 100, 102, 103, 104, 106 ML models, 140, 143, 147, 148, 153 Mobile Devices, 115 Mobile digital payment systems, 95, 104 Mobile Financial Services, 113 Mobile money, 47 Mobile Payments, 115 Mobile Technology, 113 Morocco, 40, 51 Multivariate Regression, 73, 78 National Islamic Finance Committee Komite Nasional Keuangan Syariah (KNKS), 115 Natural language processing, 86, 106 Neoclassical, 41–43 NON COMPLIANCE, 140, 143, 152

P2P Lending Platforms, 125 P2P Platforms, 114 Peer-To-peer (P2P) finance, 114 Pricing, 42

Index Privacy, 2–3, 8, 11 Product life cycle, 112 Products, 7, 11 Property investment management, 115

163 Stability, 40, 43, 45, 48 Start-ups, 44–45, 120 Sustainable Development Goals (SDGs), 113 System, 41, 43–46, 50–51 Systemic risks, 141, 52

Qualitative research, 137 Rationality, 41, 43 Regulation, 40, 47 Regulatory Sandbox, 41, 42, 43, 46, 47, 48 Regulatory Technologies (Regtech), 120 Responsible Fintech, 115 Risk, 3–4, 6, 7–8, 40, 41, 45, 48 Risk management, 8 Robo-advisor, 82, 84, 94, 95, 96, 100, 104 Sadaqah (Voluntary Charity), 125 Scientometric, 86 SCIENTOMETRIC ANALYSIS, 88, 97 Scopus database, 83, 88, 90 SDGs, 112 Sector, 39, 40, 41, 47 Services, 40–41, 45–50 Shari’a-compliant finance Or Islamic finance, 113 Sharia compliance, 94, 104 Smart contract, 44, 46 Smartphones, 115 SMEs, 120 Social, 45, 51

technology, 39–54 Technology adoption, 135 Technology-Organization-Environment, 135 Thematic map, 101 Theory of Planned Behavior, 135 TOE Framework, 135, 136, 141, 154 Topic dendrogram, 83, 84, 89, 101 Traditional, 44–47 Transformation, 44–46 Transparency, 41, 45, 50–51 Trends, 70, 73, 78 Unbanked, 41, 46–47, 50–51 Virtual reality, 106 Waqf (Islamic Endowments), 125 Word Mapping, 83 WordCloud, 98 World Bank, 116 World Bank Group, 115 Zakat (Obligatory Charity), 125