Global Financial Analytics and Business Forecasting 9798886979145, 9798886979428, 9781536193893, 9781536194302, 9798886974140, 9798886974652, 9781685070632, 9781685071417, 9798891132238, 9798891132788

Citation preview

Business, Technology and Finance Financial Institutions and Services

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

Business, Technology and Finance An SEO Guide for Start-Ups Monica Rani, PhD (Editor) Shweta Paradkar, PhD (Editor) 2023. ISBN: 979-8-88697-914-5 (Softcover) 2023. ISBN: 979-8-88697-942-8 (eBook) Service Leadership Theory: Origin and Nature Daniel TL Shek (Editor) Diya Dou, PhD (Editor) Xiaoqin Zhu, PhD (Editor) 2021. ISBN: 978-1-53619-389-3 (Hardcover) 2021. ISBN: 978-1-53619-430-2 (eBook)

More information about this series can be found at https://novapublishers.com/product-category/series/business-technology-andfinance/

Financial Institutions and Services Credit Reporting: Reforms, Scoring and Policies Anthony J. Haley (Editor) 2022. ISBN: 979-8-88697-414-0 (Hardcover) 2022. ISBN: 979-8-88697-465-2 (eBook) SME Finance: Constraints and an Information Asymmetric Perspective Chokey Wangmo, PhD, Sardar Islam (Editors) 2021. ISBN: 978-1-68507-063-2 (Hardcover) 2021. ISBN: 978-1-68507-141-7 (eBook)

More information about this series can be found at https://novapublishers.com/product-category/series/financial-institutions-andservices/

Sanjay Taneja Ercan Özen Pawan Kumar and Sanjeet Kumar Editors

Global Financial Analytics and Business Forecasting

Copyright © 2024 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Please visit copyright.com and search by Title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact:

Phone: +1-(978) 750-8400

Copyright Clearance Center Fax: +1-(978) 750-4470

E-mail: [email protected]

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regards to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS.

Library of Congress Cataloging-in-Publication Data Names: Taneja, Sanjay, editor. | Özen, Ercan, editor. | Kumar, Pawan (Professor of finance), editor. | Kumar, Sanjeet, editor. Title: Global financial analytics and business forecasting / Sanjay Taneja, PhD (editor), Özen (editor), Pawan Kumar, PhD (editor), Sanjeet Kumar, PhD (editor) Description: New York: Nova Science Publishers, [2024] | Series: Financial institutions and services | Includes bibliographical references and index. | Identifiers: LCCN 2023045973 (print) | LCCN 2023045974 (ebook) | ISBN 9798891132238 (hardcover) | ISBN 9798891132788 (adobe pdf) Subjects: LCSH: Finance--Mathematical models--Data processing. | Finance--Databases. | Financial services industry--Data processing. | Financial services industry--Technological innovations. Classification: LCC HG106 .G56 2024 (print) | LCC HG106 (ebook) | DDC 332.015195--dc23/eng/20231002 LC record available at https://lccn.loc.gov/2023045973 LC ebook record available at https://lccn.loc.gov/2023045974

Published by Nova Science Publishers, Inc. † New York

Contents

Foreword by Simon Grima ....................................................................... vii Foreword by Amandeep Singh .................................................................. xi Editorial

......................................................................................... xiii

Preface

........................................................................................ xvii

Chapter 1

FinTech in India: Opportunities and Challenges ...........1 Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

Chapter 2

An Overview of FinTech in India ...................................21 Jitender Kumar, Anjali Ahuja, Vinki Rani and Nidhi Sindhwani

Chapter 3

An Introduction to FinTech ............................................39 Arti Gaur, Sanju Verma and Sweta Bhatti

Chapter 4

Artificial Intelligence and Its Role in Financial Markets ............................................................67 Kapil Kumar Aggarwal and Satakshi Agrawal

Chapter 5

Research on Consumer Preferences and Consumer Satisfaction Levels in the Banking Sector: A Case Study in the Republic of Moldova .......................................................83 Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Chapter 6

FinTech Tools Used in Finance ....................................117 Mohini, Seema Bamel and Shveta Singh

vi

Contents

Chapter 7

Unlocking the Predictive Power of ARMA Models on Algoquant Fintech’s Daily Returns ...........125 Monika Khanna, Mukul and Pawan Kumar

Chapter 8

Big Data: An Opportunity for Academic Organizations ...............................................147 Ritu Vashistha, Padam Bhushan and Kavita Dahiya

Chapter 9

Barriers and Challenges in the FinTech Industry ...........................................................157 Shlok Nitin Gupta, Gargi Pant Shukla and Priyanka Panday

Chapter 10

Applications of FinTech in Banking ............................165 Reepu and Atul Shiva

Chapter 11

Machine Learning Algorithms to Accelerate the Development of Business Analytics ..............................183 Renu Vij

Chapter 12

The Role of Business Intelligence in the Financial Sector .............................................................199 Sanjay Taneja, Neha Bansal and Ercan Ozen

Chapter 13

The Dynamic Linkages Between Stock Market Indices and Exchange Rates for BRICS ......................223 Sukhmani Kaur, Shalini Aggarwal and Vikas Arora

Chapter 14

Business Decision-Making with an Analytical Approach: A New Leadership Pattern ........................245 Kirti Khanna and Vikas Sharma

Chapter 15

A Granular Finance Tailored to the Needs of Less Advantageous Countries, Businesses and Individuals: Artificial Intelligence’s Role in the Financial Sector .............................................................261 Elhadj Ezzahid and Zakaria Elouaourti

About the Editors ......................................................................................277 Index

.........................................................................................281

Foreword by Dr. Simon Grima

The processes of applying data analysis and statistical tools to get insights into financial markets, economic trends, and business performance on a worldwide scale are referred to as global financial analytics and business forecasting. It entails gathering, analysing, and interpreting financial data from diverse sources in order to draw conclusions and forecast financial events with accuracy. Analysing historical financial data, such as balance sheets, income statements, and cash flow statements, in order to spot patterns, trends, and linkages is known as financial analytics. It can aid in risk assessment, the identification of abnormalities or inconsistencies in financial data, and the identification of key drivers of financial performance. In order to help decision-making, it frequently uses tools like financial ratios, trend analysis, regression analysis, and data visualization approaches. On the other hand, business forecasting uses statistical models and historical data to project future business performance and market conditions. Based on variables like market trends, consumer behaviour, industry dynamics, and macroeconomic indicators, it seeks to anticipate future revenues, costs, profits, and cash flows. Time series analysis, regression analysis, econometric models, and machine learning algorithms are all examples of forecasting methodologies. The coverage goes beyond specific businesses or markets to provide a larger perspective in the context of global financial analytics and business forecasting. It entails taking into account elements that can affect financial performance and commercial outcomes across national borders, such as global economic indicators, geopolitical variables, cross-border trade, currency exchange rates, and international market dynamics. The advantages of corporate forecasting and global financial analytics include:

viii

Simon Grima

Enhanced decision-making: Organizations can use data-driven insights to make better strategic decisions about global investments, resource allocation, pricing, and risk management. Risk assessment and reduction: By examining financial data and market patterns on a global scale, it is possible to gain a thorough understanding of potential risks and take proactive steps to reduce them. Competitive advantage: By seeing new opportunities, market gaps, and potential development areas, accurate forecasting and analysis of global financial trends can give businesses a competitive edge. Resource allocation optimization: Organizations can increase operational effectiveness and resource allocation by anticipating company performance and global market circumstances. Planning for scenarios: Global financial analytics and forecasts help businesses create scenarios and evaluate the potential effects of various market circumstances, legislative changes, or economic occurrences on their operations. Organizations need to have access to high-quality data, powerful analytical tools, and qualified analysts who can interpret and translate data insights into practical strategies in order to effectively exploit global financial analytics and business forecasting. To improve their capabilities in this area, they might also partner with financial analytics companies or look for outside expertise. In order to do this, the book Global Financial Analytics and Business Forecasting explores topics like technology as a key factor in possibilities and problems. It focuses on artificial intelligence in finance, consumer preferences and satisfaction levels in the banking industry, Fintech tools used in finance, the predictive power of ARMA models on Fintech's daily returns, big data in academic organizations, and more. technological applications in the banking industry, machine learning algorithms for business analytics, the function of business intelligence in the financial sector, and the dynamic relationships between stock market indexes and exchange rates for the BRICS countries. This book stands out for its scientific brilliance and practical worth, the quality of argumentation, and the accuracy of analyses, adding notable value to the literature in this scientific field. I am convinced that readers will gain from an enlightened reading experience.

Foreword

ix

Regards, Simon Grima PhD (Melit.)., MSc(Lond.)., MSc(BCU)., B.Com (Hons), FIFA. FAIA, FIPA Associate Professor Head of The Department of Insurance and Risk Management Deputy Dean - Faculty of Economics, Management and Accountancy RM 218 FEMA Buildings University of Malta Professor of Finance, Faculty of Business, Management and Economics, University of Latvia

Foreword by Dr. Amandeep Singh

This book offers an in-depth analysis of the most salient features of contemporary financial systems and clarifies the major strategic issues facing the development of financial analytics and business forecasting. It provides insight into how the financial analytics system works in a socioeconomic context. It presents three key messages: that digital transformation will change the financial system entirely, that the State has a particularly important role to play in the whole process and that consumers will be offered more opportunities and freedom but simultaneously will be exposed to more risk and challenges. The book is very well designed and tries to cover the contemporary issues in the digital finance. The book covers the topics viz. FinTech, Bitcoins and Blockchain Trends, Future of Indian Financial System, ML and AI Scope in Financial Sector, Opportunities and Challenges in the FinTech, Descriptive Analysis, Predictive Analysis, Prescriptive Analysis, Decision Analysis, FinTech Tools, Intelligence in Financial Sector and Various Applications of FinTech. The book is a good blend of theory and the practical aspects of the Global Financial Analytics System. I wish the editors the best of luck. Dr. Amandeep Singh Professor and Associate Dean (MBA Marketing) Chitkara Business School, Chitkara University, Punjab, India

Editorial

Chief Editor Dr. Sanjay Taneja Post Doc (Green Finance, Turkey), PhD. (Finance and Banking), MBA (Gold Medalist), ADBM Associate Professor, University school of Business, Chandigarh University, India

In an era where the global economy undergoes rapid transformations, understanding the dynamics of financial markets and making accurate predictions becomes increasingly crucial. It is within this context that the book "Global Financial Analytics and Business Forecasting" emerges as a comprehensive guide to navigating the intricate landscape of finance. Authored by prominent experts in the field, this book offers a deep exploration of financial analytics and forecasting, shedding light on the intricate relationships between economic indicators, business performance, and market trends.

xiv

Sanjay Taneja

Unveiling the Power of Financial Analytics: The book's strength lies in its ability to demystify the complex world of financial analytics and its practical implications. By blending theoretical concepts with real-world case studies, it equips readers with the knowledge needed to make informed decisions in today's volatile markets. The authors adeptly break down intricate financial models, such as regression analysis, time series forecasting, and risk management techniques, providing readers with a clear understanding of their underlying principles and applications. Understanding the Global Financial Landscape: One of the book's standout features is its emphasis on the global nature of financial markets. In an interconnected world, where events in one country can have far-reaching consequences, it is essential to have a comprehensive understanding of the global financial landscape. The authors delve into the intricacies of international markets, exploring the impact of geopolitical factors, macroeconomic indicators, and cross-border transactions on financial analytics and forecasting. This holistic perspective enables readers to grasp the intricate web of global finance and make informed predictions amidst the ever-changing economic environment. Insights into Business Forecasting: "Global Financial Analytics and Business Forecasting" takes business forecasting to a new level, providing invaluable insights for corporate strategists, financial analysts, and entrepreneurs alike. By utilizing sophisticated analytical tools and methodologies, the book equips readers with the skills to identify market opportunities, optimize resource allocation, and mitigate risks. The authors emphasize the importance of accurate forecasting in decision-making, highlighting the potential pitfalls and common biases that can undermine the forecasting process. Through the lens of real-world examples, readers are presented with a roadmap to anticipate and respond to market fluctuations effectively. Navigating the Future: The book's relevance extends beyond its immediate audience, as it provides a broader perspective on the future of the global economy. With disruptive technologies, shifting consumer behavior, and evolving regulatory frameworks, the ability to predict financial trends and adapt to changing circumstances becomes paramount. "Global Financial Analytics and Business Forecasting" equips readers with the tools to navigate these uncertainties successfully, fostering resilience and adaptability in an increasingly dynamic business landscape. Conclusion: In a world where the financial landscape is continually evolving, "Global Financial Analytics and Business Forecasting" emerges as

Editorial

xv

a beacon of knowledge and guidance. Its comprehensive exploration of financial analytics, global market dynamics, and business forecasting provides readers with the essential tools to make informed decisions in an uncertain world. By bridging the gap between theory and practice, the book empowers professionals and decision-makers across industries, setting a new standard for understanding and leveraging the power of financial analytics.

Preface

The field of finance has undergone significant changes in recent years, and the use of advanced analytical tools and techniques has become increasingly critical in making informed business decisions. In today's fast-paced global business environment, it is essential for organizations to have access to accurate financial data and effective forecasting methods to stay competitive and achieve their goals. This book, Global Financial Analytics and Business Forecasting, provides a comprehensive overview of the latest trends and techniques in financial analytics and forecasting. The book covers a wide range of topics, including financial data analysis, risk management, forecasting methods, and the use of advanced technologies such as artificial intelligence and machine learning in finance. The book is intended for finance professionals, analysts, researchers, and students who want to enhance their knowledge and skills in financial analytics and forecasting. The book is also an excellent resource for businesses looking to improve their financial performance and stay ahead of the competition. The book's contributors are leading experts in the field of finance and analytics, and they bring a wealth of knowledge and experience to this comprehensive guide. Their insights and practical advice offer valuable perspectives on the latest trends and best practices in financial analytics and forecasting. The book is divided into 15 chapters, each focusing on a different aspect of financial analytics and forecasting. First Chapter provides a Fintech in India: opportunities and challenges, followed by an overview of Fintech in India, Introduction of Fintech, Artificial intelligence and its role in financial markets, Research of consumer preferences and consumer satisfaction level in banking sector: study case in republic of moldova, Fintech tools used in Finance, Unlocking the predictive power of ARMA models on Algoquant Fintech's daily returns, Big data: An opportunity for academic organizations, Barriers and challenges in Fintech Industry, Applications of Fintech in

xviii

Sanjay Taneja, Ercan Özen, Pawan Kumar et al.

banking, Machine learning algorithm to accelerate development of business analytics, Role of business intelligence in financial sector, Dynamic linkages between stock market indices and exchange rate for brics, Business decision making with an analytical approach: A new leadership pattern, A granular finance tailored to the needs of less advantageous countries, businesses and individuals: Artificial intelligence's role in the financial sector We hope that this book will serve as a valuable resource for finance professionals and students alike and help them stay informed and up-to-date on the latest trends and best practices in financial analytics and forecasting.

Chapter 1

FinTech in India: Opportunities and Challenges Chhinder Kaur1, Anand Sharma2 and Tejinder Pal Singh1 1Department

of Computer Applications, Chandigarh Group of Colleges, Mohali (Punjab), India 2University School of Business, Chandigarh University, Mohali (Punjab), India

Abstract FinTech refers to integrating technology in the financial industry, providing innovative solutions to financial services, thereby offering alternatives to conventional banking methods and other financing options. “FinTech” describes a relatively new notion in the financial sector. The primary goal of this study is to analyze the FinTech industry’s prospects and difficulties. Financial technology (FinTech) is introduced, along with an explanation of its history and a presentation of its application in India’s financial sector. Financial technology companies provide safer online financial transactions. FinTech services have many benefits, including reduced overhead and an intuitive interface. There is no country in the world where the FinTech business is expanding at a faster rate than India. The use of FinTech services will cause a sea change in the way financial institutions in India operate.

Keywords: block chain, FinTech disruptions, digital currency, artificial intelligence, financial services 

Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

2

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

Introduction The term “FinTech” was first used by a banker based in New York in the year 1972. Although the term “FinTech” does not have a commonly agreed-upon definition, it is generally understood to refer to businesses that offer a range of financial services using technology. The financial services offered by FinTech companies can include a wide variety of products and services, such as online lending platforms, financing solutions, forex and remittance services, investment opportunities, blockchain technology, virtual currencies, mobile payment solutions, AI in finance, crowd-sourced funding, and insurance coverage, among others (Digital Finance Institute, 2016). Thus, growing trends allowed by technological advancements that promote innovation have considerably impacted and influenced the financial services industry. Capital Markets: Innovation and the FinTech Landscape, a recent report by Ernst & Young, identifies nine technologies or technology-enabled trends that enable present and future FinTech advances, including, to begin with, the use of cloud computing. It’s a practice that involves sending off work or service to be done.

FinTech The term “FinTech” describes the innovative methods and tools that can be applied to the financial sector as a direct result of the development of digital technologies. Technically speaking, FinTech is defined by the Financial Stability Board 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.” However, there are other components that make up the FinTech sector. Financial technology can be broadly classified into four main categories: financing, asset management, payments, and “other FinTechs.” The different segments and their components are depicted in Figure 1.

FinTech in India

3

Figure 1. Components and Aspects of FinTech and compilation by authors (Dorfleitner et al., 2017).

FinTech Sector’s History FinTech is a term that refers to the use of technology to provide financial services. The FinTech sector has a relatively short history but has grown significantly in recent years. Here is a brief overview of the history of the FinTech sector: • •

•

1980s: The first online stock brokerage firms were founded, allowing investors to trade stocks electronically. 1990s: The widespread utilisation of the internet has resulted in the growth of digital banking and other technology-driven financial services. Late 1990s to early 2000s: The growth of e-commerce drives the development of payment systems such as PayPal, which allows for easy and secure online transactions.

4

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

•

•

•

Late 2000s: The global financial crisis led to a decline in trust in traditional financial institutions, paving the way for the rise of alternative financial service providers. 2010s: The increasing popularity of mobile devices and the abundance of smartphone applications have facilitated the creation of numerous FinTech offerings, including mobile payment systems, peer-to-peer lending platforms, and automated investment advisors. 2020s: The FinTech sector continues to grow and evolve, focusing on using technology to improve financial inclusion and accessibility for underserved communities.

International FinTech Investment Activity Worldwide Fin Tech Investment activity (CBInsights, 2022) represents the FinTech sector of the financial services industry. The growth of the FinTech industry has disrupted the traditional financial services sector, but for FinTech startups to compete with established financial services providers, they often require a significant amount of investment. This investment is necessary for FinTech startups to develop and scale their services, as well as to compete with established providers in terms of technology, marketing, and other areas. On the other hand, established financial services providers need to take the FinTech industry’s growth seriously and adapt to the changing landscape of financial services. This can include embracing new technologies, improving the customer experience, and finding ways to integrate FinTech services into their existing offerings. Failing to do so could result in lost market share and revenue to FinTech companies. Both FinTech startups and established financial services providers have an important role in the growth and development of the FinTech industry. FinTech startups need investment to compete with established providers, while established providers need to adapt to the changing landscape of financial services and embrace new technologies to remain competitive.

FinTech in India In (KPMG, 2016) N. Punater et al., claim that India is becoming a hub where FinTech startups can get traction and develop into multibillion-dollar

FinTech in India

5

enterprises. FinTech start-ups in India have many goals, including expanding into new markets and targeting untapped niches. As per NASSCOM, the FinTech software industry in India is projected to experience growth, increasing from its current USD 1.2 billion to a significant USD 2.4 billion by the year 2020. The progress of e-commerce and the widespread adoption of smartphones in India have provided a welcome catalyst for the country’s historically cash-driven economy to embrace the FinTech opportunity. The FinTech industry in India has been proliferating in the last few years, and the projections for its future growth are quite promising. According to research (Ray, 2021), the FinTech sector in India is anticipated to rise at a compound annual growth rate of 22% over the next five years, which would take its value from an estimated $33 billion in 2016 to $73 billion by 2020. This growth can be attributed to several factors, including the increasing adoption of technology, the government’s push towards digitalisation and financial inclusion, and a large pool of skilled tech professionals in India. Additionally, the increasing use of mobile devices and the growing number of consumers who are looking for more convenient and cost-effective financial services has also contributed to the progression of the FinTech industry in India(Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al. 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d). In conclusion, the FinTech industry in India has a bright future, and its growth is expected to bring significant benefits to both consumers and the financial services sector in the country. The sector’s growth will not only provide more convenient and cost-effective financial services, but it will also create new job opportunities and contribute to the country’s overall economic growth. The FinTech sector in India is projected to witness significant growth, with an estimated five-year compound annual growth rate of 22%. This growth is expected to bring the sector’s value from an estimated $33 billion in 2016 to $ 73 billion by 2020. In 2015, Bengaluru was the leader in venture capitalsupported investment deals, with a total of 11 agreements worth $57 million, Mumbai with nine deals and Gurgaon with 6. As a result, Bengaluru, India’s startup capital, is now regarded as one of the world’s top start-up hubs, ranking 15th globally. India has a large pool of highly skilled technology professionals, many of whom have received education and training in computer science, software engineering, and data analysis. This provides a significant advantage for the

6

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

country’s FinTech industry, allowing companies to access a wide range of talented workers at relatively low costs. In addition to the talent advantage, India’s large and growing market for financial services is another key factor driving growth in the country’s FinTech sector. India offers a vast potential market for financial services, including banking, insurance, and investment products, owing to its population size of 1.3 billion. This presents a significant opportunity for FinTech companies to provide innovative and affordable financial services to a large and growing customer base. The Indian FinTech industry has been growing rapidly in recent years, with the government actively promoting digitisation and financial inclusion. This has led to several innovative FinTech startups that provide various financial services through digital platforms, helping people access financial services and manage their finances. India’s growing interest in and adoption of these trends makes it an extremely profitable market.

India’s Acceptance of FinTech FinTech adoption has been on the rise globally, with digital consumers embracing various financial technology services and solutions. These services can range from mobile payment apps and peer-to-peer lending platforms to robo-advisors and blockchain-based solutions. Adoption rates can vary significantly across different markets due to technological infrastructure, regulatory environment, consumer behaviour, and cultural preferences. As of 2021, some of the leading markets in FinTech adoption included:

1. China: China has been a leader in FinTech adoption, driven by the widespread use of mobile payment platforms like Alipay and WeChat Pay. These platforms have become integral to daily transactions for millions of Chinese consumers. 2. India: The Indian FinTech landscape has rapidly evolved, with digital payment solutions like UPI (Unified Payments Interface) gaining widespread popularity. Additionally, digital lending and investment platforms have seen increased adoption. 3. United States: In the U.S., mobile banking apps, peer-to-peer payment apps like Venmo, and investment platforms like Robinhood have contributed to the growing FinTech adoption rate.

FinTech in India

7

4. United Kingdom: The UK has a mature FinTech ecosystem, with digital consumers using a variety of services, including digital wallets, challenger banks, and investment platforms. 5. Brazil: FinTech adoption in Brazil has been driven by the need for financial inclusion, with digital payment solutions and lending platforms being trendy. 6. Australia: Australian consumers have embraced FinTech services for payments, investments, and personal finance management. 7. Singapore: Singapore’s government has actively promoted FinTech innovation, leading to a high adoption rate for digital payment services and robo-advisors. 8. South Korea: Mobile payment platforms and digital banking services have gained significant traction in South Korea. 9. Mexico: FinTech adoption in Mexico has been increasing, with mobile payments and remittance services widely used. 10. Germany: Digital consumers in Germany have shown interest in digital wallets, payment apps, and online investment platforms. These are just a few examples, and FinTech adoption trends can also vary within each market. The adoption rate depends on factors like regulatory frameworks, consumer awareness, trust in technology, and the availability of user-friendly platforms. For the most current information on FinTech adoption across different markets, I recommend consulting industry reports, market research, and news sources specialising in FinTech and financial technology trends.

India’s FinTech Ecosystem Coverage Government Naturally, in a highly regulated financial business, the government is the primary trigger for the success or failure of FinTech. The government of India and its regulatory bodies, such as the SEBI and RBI, have been actively supporting the growth of the FinTech industry in India. They have recognised the potential of FinTech to transform the country’s financial services sector and promote financial inclusion and digitalisation. The government has implemented various initiatives to encourage and the transition to a cashless digital economy and develop a robust FinTech ecosystem as mentioned in

8

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

Figure 2. This includes providing subsidies and tax benefits to FinTech companies, simplifying the regulatory environment, and promoting innovation in the financial sector (2016 Report on Financial Technology in India from KPMG) To help spread the word about the benefits of digitally enabled financial platforms among institutions and the general public, the following multi-pronged strategy has been implemented:

Funding Support The Government of India introduced the Start-Up India initiative in January 2016, and a part of it is a USD 1.5 billion fund for new businesses.

Promoting Equal Financial Opportunities Over 200 million formerly unbanked Indians have already opened bank accounts due to the Jan DhanYojana, and Aadhar has been expanded to cover pensions, provident funds, and the program.

Tax and Surcharge Relief Some noteworthy efforts include: Businesses that process more than half of their sales online should receive tax breaks—80 per cent discounts on patent fees for new businesses tax-free first three years for new companies. Longterm investments in private, non-public enterprises (held for more than 24 months) are exempt from capital gains tax (from the 36 months needed earlier). The Ministry of Finance has proposed eliminating the surcharge for electronic payment methods to access government services.

Infrastructure Support The Government of India has created the Digital India and Smart Cities projects to encourage the growth of the nation’s digital infrastructure and entice foreign investment. For new businesses, the government just released a platform just for registration.

FinTech in India

9

IP Facilitation Support The government will pay for the costs of patent lawyers, trademark lawyers, and other people who help with the design.

Figure 2. Components of the FinTechLandscape (Ray, 2021).

Financial Oversight Authorities The Reserve Bank of India (RBI) has been actively promoting innovation in the financial sector, while also ensuring that the industry operates safely and securely. The RBI has established a regulatory framework for the FinTech sector that balances innovation and consumer protection. For instance, the RBI has implemented regulations for digital wallets, peer-to-peer lending platforms, and other FinTech services to ensure that consumer data is protected and that there is proper due diligence in the lending process. The RBI has also established a regulatory sandbox to permit FinTech startups to test their novelties in a controlled environment while allowing the regulator to monitor the effect of these innovations on the financial sector. In addition, the RBI has also been advocating for increased financial literacy and consumer protection, especially in the digital space. This is important as the growth of FinTech has made financial services more

10

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

accessible to consumers, who may not be familiar with the risks and challenges associated with digital transactions. The regulator’s primary focus has been on fostering an atmosphere conducive to the unrestricted development of FinTech, increasing access to banking services for the unbanked, ensuring the security of electronic payments, and giving customers more choices in their financial dealings. In the payments space, digital wallets have become extremely popular in India, with companies like Paytm, Google Pay, and PhonePe leading the way. These wallets allow consumers to make payments, transfer money, and pay bills easily and quickly using their mobile phones. The growth of these wallets has also led to a significant increase in the number of digital transactions in the country. In the lending space, FinTech startups have been able to disrupt the traditional lending model by providing loans to consumers and small businesses that traditional banks previously underserved. These startups use technology to assess credit risk, allowing them to provide loans quickly and at a lower cost. These are the RBI’s primary areas of interest, and there have been numerous documented approaches to encouraging FinTech engagement. •

•

•

•

•

The advent of the NPCI’s Unified Payment Interface, for instance, has the potential to completely revamp digital payment systems in India, bringing the country one step closer to its “Less-Cash” society goal. The cause of financial inclusion can benefit from the approval of 11 organizations to establish a payments bank and 10 organizations to establish a small finance bank. India’s P2P lending market is being regulated with the release of a consultation document, and the country’s financial institutions are being urged to study blockchain technology. Among the many promising fields is that of P2P remittance management in India. In India, the transaction cost percentage is higher for smaller remittances, making it prohibitively expensive for the beneficiaries. Following the lead of established markets, FinTech companies that effectively solve this large issue stand to reap enormous rewards. FinTech companies like Transfer Wisein UK, which has developed a remittance platform, are now valued at over $1 billion because of the growing popularity of their product. In conclusion, Indian authorities deserve praise for their efforts to foster the development of India’s FinTech industry; continuing to

FinTech in India

•

•

11

build on this momentum is essential for implementing even more innovative and timely legislative initiatives. Examples: The UK financial regulator, the Financial Conduct Authority (FCA), has developed a program called “Project Innovate” to facilitate collaboration between startups and the FCA to speed up the introduction of novel financial products. The New York State Department of Financial Services’ implementation of the “BitLicense” rule in 2015 has facilitated innovation in the United States.

FinTech Innovations, Products, and Technology A report from the World Economic Forum in 2015 claims that FinTech innovations lack a universally agreed-upon taxonomy. Through its scoping exercise, the WG was able to classify some of the most significant FinTech breakthroughs into five primary categories, providing a better understanding of the breadth of current advancements in this sector. (Skan et al., 2016) While intended to be a partial survey of the field of FinTech, this chapter focuses on the developments that are anticipated to have the most substantial effect on the financial markets. Some of the most significant recent developments in financial technology can be conveniently classified based on the subsets of financial markets where they are most likely to be used.

Categorisation of Major FinTech Innovations FinTech innovations can be broadly categorised into the following categories: 1. Payment and Transactions: This category includes innovations facilitating electronic payments and transactions, such as mobile payments, online banking, and peer-to-peer payments. 2. Lending and Credit: The financing category encompasses innovations that allow for the development of innovative credit and lending solutions. 3. Wealth Management: This category includes innovations that provide investment and wealth management services, such as robo-advisors and automated investment platforms.

12

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

4. Personal Finance: This category includes innovations that help individuals manage their personal finances, such as budgeting and financial planning tools. 5. Insurance: This category includes innovations that facilitate the purchase and management of insurance policies, such as insurance comparison websites and digital insurance platforms. 6. Regtech: This category includes innovations that help financial institutions comply with regulations, such as risk management and compliance software. 7. Blockchain and Cryptocurrency: This category includes innovations that use blockchain technology and cryptocurrency to facilitate financial transactions and create new financial products. 8. Other: This category includes FinTech innovations that do not fit the above categories, such as financial education platforms and financial inclusion initiatives.

The Future of FinTech in India According to our findings, the financial services industry is already experiencing multiple forms of disruption from FinTech entrepreneurs. According to the researcher (Khosla, 2023), the next wave of advancements will come from the financial technology (FinTech) industry; therefore, let us learn more about it. Until the advent of blockchain technology, all business deals had to be verified by an impartial third party. In their place, blockchains offer cryptographic security and eliminate the need for third-party reconciliation. •

•

Blockchain-based virtual currencies like Bitcoin have quickly gained popularity. However, blockchains are anticipated to expand into many new areas, not limited to cryptocurrencies, financial transactions, or the banking industry. Traditional banks determined that providing loans to new, small businesses was not profitable, so they began looking for alternatives. Entrepreneurs in the financial technology sector jumped on the P2P lending bandwagon, creating online platforms to match lenders and borrowers in exchange for cheaper interest rates. Crowdfunding and other forms of unconventional lending will only increase in

FinTech in India

•

•

•

13

popularity, so expect this pattern to continue. Traditional intermediaries were crucial before the advent of Robo-advisors to bridge the gap between the stock market and investors. This frequently resulted in sloppy and untraceable financial dealings. The use of Robo-advisory software will increase the stock market’s accessibility, transparency, and traceability, while also providing superior value to astute investors. Payments may now be made instantly and with no effort, thanks to the innovations of FinTech startups. More and more places are switching to mobile wallets instead of regular wallets because of the convenience and security they offer. Even automated teller machines will be obsolete. In the insurance industry, customers can now shop around on various e-commerce sites to find the best possible coverage at the most reasonable price. The latest technologies, such as AI, ML, and big data analytics, can help insurance companies automate many of their processes, from underwriting and claims processing to customer service and policy management.

Types of FinTech Companies Accenture distinguishes between “competitive FinTech ventures” and “collaborative FinTech ventures” as the two primary types of financial technology businesses (Kavuri& Milne, 2019). According to Accenture’s 2016 research, competitive FinTech companies are those that would pose direct challenges and barriers to the financial services industry. These businesses have thrived for decades because they prioritise customer satisfaction over profit maximisation by constantly innovating the technology they sell. In this regard, eToro’s retail investor help, advice, and optimal solution offerings reflect the company’s expert business methods at the recommended price. Square has improved and expanded its card service in order to provide microbusinesses with the most value possible. Accenture (Skan et al., 2016), on the other hand, highlights the role that collaborative FinTech companies play in propelling the development of financial institutions (Anikina et al., 2016). In reality, the incumbent financial institutions are seen as prospective consumers by collaborative FinTech ventures. It is in their best interest to help these financial institutions succeed,

14

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

so they are always working together, lending their assistance, and suggesting new ways forward so that they can advance in the industry. Collaborative FinTech companies, for instance, aid financial institutions in disrupting their outdated business models and introducing novel, more long-term ways of doing things. In addition, the creation and use of these high-tech solutions assist financial institutions in optimising their present business, reducing costs, and simplifying procedures and day-to-day financial services (Skan et al., 2016).

Top FinTech Companies in India Here is a list of some of the top FinTech companies in India, along with their locations, business categories, and total funding: 1. Paytm (Delhi, India) - Business category: Digital payments, financial products - Total funding: $4 billion 2. PhonePe (Bangalore, India) - Business category: Digital payments, financial products - Total funding: $750 million 3. PolicyBazaar (Gurgaon, India) - Business category: Insurance comparison website - Total funding: $200 million 4. BankBazaar (Chennai, India) - Business category: Financial comparison website - Total funding: $70 million 5. Flipkart (Bangalore, India) - Business category: E-commerce, financial products - Total funding: $7.5 billion 6. ICICI Bank (Mumbai, India) - Business category: Bank, financial products - Total funding: N/A (ICICI Bank is a publicly traded company) 7. HDFC Bank (Mumbai, India) - Business category: Bank, financial products - Total funding: N/A (HDFC Bank is a publicly traded company)

Challenges and Future Perspectives After the demonetisation of the Indian currency, the country’s population embraced other payment methods. The government actively promoted cashless technology such as digital wallets, Internet banking, and mobile-

FinTech in India

15

driven point-of-sale systems (POS). KYC, UPI, and BHIM, all linked to the Aadhaar card, have reorganised India’s banking system (Bhasin& Rajesh, 2021). As much as 22% more business was done digitally in India after the demonetisation. PayTM and other FinTech startups reported a 435 per cent increase in visits to their digital properties. As a result, many FinTech startups have emerged in India, capitalising on the country’s abundant growth potential. Many governments’ startup policies have been helpful to digital finance companies. The Reserve Bank of India also made establishing a financial technology firm simple. For new businesses, the government will invest up to 1 crore. The digital currency quickly gained widespread consumer and business acceptance. The Indian government’s mandate for using digital wallets as a payment method resulted in adjustments made by banks and financial institutions to adapt to the changing economic landscape. The value of virtual currencies like Bitcoin rose owing to the convergence of information technology and finance. The introduction of cryptocurrencies and blockchain technology aided in the quick processing of electronic payments (Lee & Jae, 2017). Smaller FinTech companies in India saw development once large financial institutions integrated their formal digital transactions with startups like Startup Village. Traditional banking and financial institutions have seen a rise in consumers and a decrease in service times due to technological advancements. The FinTech sector faces similar difficulties to those faced by FinTech businesses generally. There is still a long way to go until collaboration and adoption rates approach digital payments, but the ratio is on the rise (up 59% since 2015). Cryptocurrency is still in its infancy in India because of the widespread adoption of related technologies such as blockchain-based administration. One of the biggest problems in India’s FinTech industry is the need for more clarity surrounding the relevant regulations. For the most part, lowincome groups have benefited less from innovation. Internet access and widespread education are also major barriers. Like two sides of a coin, the FinTech industry in India faces some difficulties. However, these obstacles can be overcome with adequate government backing and even turned into possibilities. According to (Vijai, 2019), the problematic for India’s FinTech industry is the lack of clarity surrounding relevant regulations. For the most part, low-income groups have not benefited as much from innovation. Internet access and widespread education are also significant barriers. Like two sides of a coin, the FinTech industry in India faces some difficulties. However, these

16

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

obstacles can be overcome with adequate government backing and even turned into possibilities (Deloitte, 2019).

Conclusion The rapid growth of the FinTech industry in India highlights its importance and significance in transforming the financial services sector in the country. With an increasing number of people using digital financial services, the FinTech industry has the potential to drive financial inclusion and bring financial services to millions of people who were previously excluded from the formal financial system. According to the article, NASSCOM predicts that the Indian financial software market will grow from USD 1.2 billion to USD 2.4 billion by 2020. The growth of e-commerce and widespread smartphone usage in India have created a favorable environment for the country’s traditionally cash-reliant economy to embrace the potential of FinTech. The Indian government actively supports the FinTech industry and is dedicated to promoting its growth, showcasing innovative ideas and technologies. FinTech companies leverage technology to deliver a variety of financial services, including online lending, digital payments, investment management, and insurance. The use of technology has enabled FinTech companies to offer these services in a more convenient and user-friendly way, making them accessible to a wider section of the population, including those who were previously omitted from the formal financial system. FinTech can potentially interrupt the traditional financial services sector and bring about significant changes in how financial services are provided and consumed. The rise of FinTech has led to increased competition, improved customer experience, and lower costs for financial services. The advent of FinTech in India has brought about several positive changes for the country’s economy, including improved safety and ease of use for financial services and lower fees for providing these services.

References Anikina, I. D., Gukova, A. V, Golodova, А. А.,&Chekalkina, А. А. (2016). Methodological Aspects of Prioritization of Financial Tools for Stimulation of Innovative Activities. XIX(2), 100–112. https://www.um.edu.mt/library/oar/handle/123456789/28941.

FinTech in India

17

Bhasin, N. K., & Rajesh, A. (2021). Impact of E-Collaboration Between Indian Banks and FinTech Companies for Digital Banking and New Emerging Technologies. International Journal of E-Collaboration (IJeC) 17(1), 21. https://doi.org/ 10.4018/IJeC.2021010102. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the FinTech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/ 10.3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. CB Insights. (2022). FinTech trends to watch in 2022. Retrieved from https://www. cbinsights.com/research/report/FinTech-trends-2022/. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962 Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802. Deloitte. (2019). FinTechs in India – Key trends (Issue December). Retrieved from https://www2.deloitte.com/in/en/pages/financial-services/articles/FinTech-indiaready-for-breakout.html. DFI. (2016). FinTech Ecosystem. Finance Institute Digital. Retrieved from https://www.digitalfinanceinstitute.org/?post_ministries=FinTech. Dorfleitner, G., Hornuf, L., Schmitt, M., & Weber, M. (2017). Definition of FinTech and Description of the FinTech Industry. In FinTech in Germany (pp. 5–10). Springer International Publishing. https://doi.org/10.1007/978-3-319-54666-7_2. E&Y. (2017). EY FinTech Adoption Index 2017- The rapid emergence of FinTech. Retrieved from https://assets.ey.com/content/dam/ey-sites/ey-com/en_gl/topics /banking-and-capital-markets/ey-FinTech-adoption-index-2017.pdf. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021.

18

Chhinder Kaur, Anand Sharma and Tejinder Pal Singh

Kavuri, A. S., & Milne, A. (2019). FinTech and the future of financial services : What are the research gaps ? (No. 18). Retrieved from https://ssrn.com/abstract=3215849. KPMG. (2016). FinTech in India (Issue June). Retrieved from https://assets.kpmg/ content/dam/kpmg/pdf/2016/06/FinTech-new.pdf?cv=1. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. doi:10.18280/ijsdp.180204 Lee, I., & Jae, Y. (2017). FinTech : Ecosystem, business models, investment decisions, and challenges. Business Horizons. Retrieved from https://doi.org/10.1016/j.bushor. 2017.09.003. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic.org /index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/en/ pub/yalovasosbil/issue/72655/1143214. Ray, T. (2021). FinTech Ecosystem in India – Trends, Top Startups, Jobs, Challenges and Opportunities. Stoodnt. Retrieved from https://www.stoodnt.com/blog/FinTechecosystem-in-india-trends-top-startups-jobs-challenges-and-opportunities/. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Skan, J., Dickerson, J., &Gagliardi, L. (2016). FinTech and the evolving landscape : landing points for the industry Executive Summary. Retrieved from https://FinTechin novationlab.com/wp-content/uploads/2021/03/FinTech_Evolving_Landscape_ 2016.pdf. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. doi:10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301.

FinTech in India

19

Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307. Vijai, C. and Vijai, C., FinTech in India – Opportunities and Challenges (March 17, 2019). SAARJ Journal on Banking & Insurance Research (SJBIR) Vol 8, Issue 1, January 2019, Available at SSRN: https://ssrn.com/abstract=3354094 or http://dx.doi.org/ 10.2139/ssrn.3354094.

Chapter 2

An Overview of FinTech in India Jitender Kumar1 Anjali Ahuja2,* Vinki Rani3 and Nidhi Sindhwani4 1Department

of Management Studies, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India 2School of Commerce and Finance, Geeta University, Panipat, Haryana, India 3Department of Management Studies, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, Haryana, India 4AIIT, Amity University, Noida, Utterpardesh, India

Abstract Information technology advancements have impacted many different industries. The Indian economy has transformed in recent years, becoming more digital and involving FinTech due to economic growth. FinTech is rapidly spreading worldwide, being driven by innovators, closely followed by academics, and now attracting the interest of regulators. FinTech, in its broadest sense, refers to both the innovative financial services enabled by technology and the business models that support them. It is an innovation that involves how businesses aim to improve the production, distribution, and utilization of financial services. The term “financial technology” is included in “FinTech.” The technology employed in the back end was initially referred to as FinTech in the twenty-first century. The present chapter attempts to discuss the overview of FinTech in India. 

Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

22

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

Keywords: FinTech, technology, financial services, India, application, blockchain

Introduction The term “FinTech” combines the words “finance” with “technology.” A New York banker first used “FinTech” in 1972 (Vijai, 2019). Capital Markets: Innovation and the FinTech Landscape identified the nine technologies: Cloud Technology, Robotic Process Automation (RPA), Advanced Analytics, Artificial Intelligence (AI), Smart Contracts, Process and Service Externalization, Internet of Things, Digital Transformation and Blockchain (Panova, 2021). An essential newscast at present is financial technology. FinTech refers to applying cutting-edge technologies to financial products, services, and processes. Due to its potential to digitise complicated corporate processes, FinTech has gained much respect from researchers and economic actors over the past five years (Wamba et al., 2020). However, the FinTech industry has attracted the complete devotion of policymakers, applicants, users, and researchers due to its rapid alteration of traditional banking (Cai, 2018). FinTech companies’ investment increased to US$4,256,202 million in 2018 and is projected to expand by 17% annually to US$7,971,957 million by 2022 (Darmansyah et al., 2020). FinTech’s popularity and ability to generate business value have been emphasised (Belanche et al., 2019); still, there areneeds to be more clarity about how to recognise the success of technological innovation in the last century, especially the adoption of FinTech. Financial technology set up the information system to offer more developed risk management, industry processing, assets management, and tools for data mining of financial services in the banking sector (Gomber et al., 2018). Therefore, advanced technologies (e.g., blockchain, data analysis.) have been used by the FinTech industry to transform existing business models and developed new products (e.g., cashless payment, robo-advisor, etc.) in the financial service sector (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al., 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d). FinTech provides faith, integrity, and clarity in areas where these attributes are most needed for system and payment (Gozman et al., 2018). The present chapter attempts to discuss the overview of FinTech in India. Following are the remaining chapter structures: The literature review is presented in Section 2. The objective of the research describes in Section 3.

An Overview of FinTech in India

23

The methodology is reflected in Section 4. The overview of FinTech provides in Section 5. The conclusion is presented in Section 6.

Review of Literature Arner et al., (2017) highlight the advantages of FinTech for the financial system, along with how it can increase consumer confidence in the financial services sector, speed up the time it takes for goods to reach consumers, employ commercial authorities, and foster the growth of financial startups. Later, Wamba et al., (2020) point out FinTech are creating economic value and removing transaction costs, especially in changing industries due to digital technology. FinTech is vital for boosting the variety and availability of services and advancing the financial sector (Swartz, 2017; Gabor & Brooks, 2017). FinTech is a novel concept that has recently changed the banking ecosystem. Various scholars Wonglimpiyarat, 2017; Wang et al., 2022, highlight technology innovation has fundamentally altered the market environment for commercial banks, and the rise of FinTech presents significant difficulties for traditional banking. Additionally, Lee and Shin (2018) emphasise the difficulties facing FinTech, such as legislative ambiguity, technical obstacles, and data security concerns. Similarly, Cai (2018) discovers that by providing an alternative platform for financial transactions, both blockchain and crowdfunding can substantially impact established financial institutions. FinTech is an innovation that benefits entrepreneurs, regulators, and society. Using principal component analysis, Kumar (2022) examines IoT’s role in digital financial inclusion. Swartz (2017) highlights that the new regulation positively impacts the FinTech sector in Hong Kong. Susilo et al., (2019) outline the comparison between the acceptance rate in the technology of the GO PAY system and OVO system by using the TAM model. The result showed that TAM model failed to identify the user perception between GO PAY system for Gojek and OVO pay system for Grab. Sung et al., (2019) explore the UK’s availability and opportunity for FinTech education. The author finds that the use of the FinTech keyword increased in online searches from (September 2012 to August 2018) in the education and employment category. Stern et al., (2017) analyze the emergence of P2P lending platforms in different provinces in China. The result reveals that P2P lending platform greatly facilitates strong communication between mobile user.

24

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

On the other hand, it has a negative correlation between the investment of fixed assets in the country and the size of traditional banks. Sohns and Wojcik (2020) observe how entrepreneurial actors respond to political uncertainty and change the institutional setting. The political instability and structure setting can generate reactions at the stakeholder level, which could alter the diversity of an entrepreneurial ecosystem (EE). On the other side, anchor firms play an essential role in self-paced networks and could shape their strategic responses. Singh et al., (2020) explore several significant findings about the use of FinTech services. The outcome shows that perceived utility and security were positively impacted, however, social influence hurt the intention to use FinTech services.

Objective of the Study This chapter aims to explain the overview of FinTech- an evolution of FinTech, the top ten FinTech companies in India, benefits of FinTech, types, applications, challenges and the future of FinTech.

Methodology A thorough search was conducted utilising relevant terms connected to FinTech or Financial Technology throughout the planning stage. The essential concepts, including benefits and innovation in FinTech, the impact on FinTech technology, applications, emerging trend types, FinTech challenges and the future of FinTech, were considered when outlining the following documents. To develop a fundamental understanding of the subject area, a non-structured process was utilised that involved testing different keyword combinations and accessing articles from reference lists. During this stage, we received the explanation of the filtering rules and search terms utilized to locate the final dataset.

Evolution of FinTech Financial technology (FinTech) has been observably developing since banks began to operate online. As our society becomes less dependent on cash,

An Overview of FinTech in India

25

wallets and platforms have been designed to assist us in understanding and managing our financial transactions. Technology has always played a vital role in the financial sector (Kamath et al., 2003; Machkour and Abriane, 2020). Table 1 highlights the evolution of FinTech in the world. Table 1. Evolution of FinTech FinTech Revolution Period Focus FinTech 1.0 (1886-1967) Infrastructure FinTech 2.0 (1967-2008) Banks FinTech 3.0 (2008-2014) Startups FinTech 3.5 (2014-2018) Globalisation FinTech 4.0 (2018-Till now) Disruptive technologies Source: https://thepaymentsassociation.org/article/FinTech-the-history-and-future-offinancial-technology/.

FinTech 1.0 (Infrastructure) – Period 1886-1967 In this phase, globalised financial services have been implemented. The first time swift financial information transfer across borders was made possible because of technology like the telegraph, railroads, and steamships (Arner et al., 2015). The first transatlantic cable was introduced in 1866. Fedwire was the first electronic fund transfer system used in the USA in 1918. In the 1950s, credit cards were introduced to minimise the inconvenience of carrying cash. The first credit card was introduced by Diner’s Club (Devkota et al., 2021)

FinTech 2.0 (Banks) - Period 1967-2008 The first portable calculator and the first ATM were installed by Barclay Bank in 1967. It began the modern FinTech age (Pant, 2020). The Society for Worldwide Interbank Financial Telecommunications (SWIFT) was established in 1973. As the first and most widely used protocol for communication between financial institutions, it makes cross-border payments possible. With the development of mainframe bank computers in the 1980s, customers could now conduct online banking. In the 1990s, the growth of the Internet and e-commerce business models led to the widespread use of online banking. Beginning of 21st century, all aspects of banks’ internal

26

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

operations, interactions with other parties, and relationships with retail consumers had been digitalised. In 2008, the Global Financial Crisis ended this time frame.

FinTech 3.0 (Startups)-Period 2008-2014 The causes of the Worldwide Financial Disaster, which quickly grew into a more severe global recession, were made clear to individuals. Users started to oppose traditional banking. As a result, both new and established players, particularly FinTech companies, have grown this time (such as banks). In 2009, Bitcoin v0.1 was launched. Smartphones have changed the face of finance, providing millions of individuals with access to the internet worldwide. People now use the internet and many banking services mainly through their smartphones. Google Wallet launched in 2011, while Apple Pay was in 2014.

FinTech 3.5 (Globalization)- Period 2014-2017 The release of FinTech 3.5 marks a transition from the Western-dominated financial industry and takes into account the growth of virtual banking. It focuses on customer behaviour and how internet usage occurs in underdeveloped nations. This period is characterised by an increase in the number of new players and their advantages over early adopters.

FinTech (Disruptive Technologies)- Period 2018- Today Open banking and blockchain technology advancements will accelerate the expansion of the financial services industry. NEO banks are the gamechangers. They gain the trust of their customers through streamlined and virtual services. The way people interact with banks and insurance companies is changing as a result of machine learning, particularly in terms of receiving customised offers and support. During this period, a new generation of integrated payment providers began to emerge, their platforms able to add payments as an additional element to an existing powerful business management system.

An Overview of FinTech in India

27

Recently, NFTs have also been utilized in widespread ways, such as by artists to ensure royalties, by inventors to increase their income through digital versions of their creations, or even as membership cards or tickets.

Top FinTech Companies in India One of the fastest-growing industries in India is the FinTech sector. Between 2019 and 2020, the industry is anticipated to expand at a 30% CAGR. The sector is fueled by various causes, including the growing middle class, the widespread use of mobile phones and the internet, and government programs encouraging digital payments. Currently valued at over $4 billion, the Indian FinTech market is anticipated to grow to $10 billion by 2025. The sector should generate 2 million new jobs over the next five years. Table 2 depicts the top 10 FinTech companies in India with total funding. Table 2. Top FinTech Companies in India Ranking Company Name 1 Paytm 2 Lendingkart 3 MoneyTap 4 Instamojo 5 Razorpay 6 Shiksha Finance 7 Pine Labs 8 ZestMoney 9 Policy Bazzar 10 InCred Source: CEO Review Magazine.

City Noida Mumbai New Delhi Bengaluru Mumbai Mumbai Mumbai Bengaluru Gurugram Gurugram

Total Funding $5 B $206 M $12.4 M $45.5 M $106 M $23 M $265 M $15 M $558 M $215 M

Benefits of FinTech Using technology to deliver financial services more effectively, FinTech is one of the industrial areas of the economy, according to Wharton FinTech on his blog (Koesworo, 2019). FinTech benefits consumers by offering better service, options, and affordable rates. FinTech also profits from simpler

28

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

transaction chains, more effective capital and operating costs, and information flow freezing. FinTech will promote the dissemination of economic policies to accelerate the movement of money to enhance local economies, and in Indonesia, FinTech also helps the National Inclusive Financial Strategy (Iswara, 2019). The key participants in the digital economy include startups, e-commerce, and small and medium-sized enterprises (MSME) (Anis et al., 2018). FinTech is in the business of providing digitally based financial services, including loans, advising, learning through digital media, payment systems, banking, insurance, and fund collections. While online stores, digital markets, transportation services, and tourism-related support services are all included in e-commerce (Varga, 2017).

Types of Finance Applications The most important types of finance applications are the following.

Payments New digital processing networks and payment application technologies are revolutionizing the entire sector. They offer consumer identity protection, enable improved digital connectivity, and reduce processing costs. Two significant areas of emphasis are asset management and managing financial transactions.

Mobile Payments FinTech has a significant impact on mobile payment. Google Wallet, Paytm, Phonepe, and Apple Pay are the most well-known digital wallets(Karthika et al., 2022). Many customers and companies use mobile wallets and other integrated payment solutions. They provide a remarkable user experience while being safe and simple to use. Mobile payment services overcome the limitations of conventional approaches. They make many transactions we conduct today easier to execute than without them.

An Overview of FinTech in India

29

Personal Wealth Management The FinTech application focuses on enhancing corporate and consumer wealth management procedures. These mobile programs offer a wide range of capabilities to aid users in safely and rapidly managing their finances. They are utilized by businesses and consumers to manage their investing operations and portfolios.

Consumer Banking To enhance their services, several conventional banking institutions are adopting digital technologies. They make investments in mobile applications that provide accessible banking services and goods. FinTech applications improve user experience, lower expenses, and lessen operational friction compared to traditional banking methods.

Blockchain Peer-to-peer transactions, smart contracts, blockchain-powered trading platforms, immutable records, and decentralised ledgers are examples of blockchain solutions that have grown in importance in today’s financial landscape. This state-of-the-art system provides a transparent way to follow financial transactions throughout their entire existence (Duy et al., 2018; Hseih et al., 2018).

Insurance FinTech applications have a lot to offer the insurance industry, which is currently adopting virtual solutions to enhance user satisfaction and increase operational effectiveness (Alam et al., 2019). Insurance businesses are making use of smartphone apps, the Internet of Things (IoT), and artificial intelligence to improve their services. The way insurance products function has changed due to these innovations, which also provide benefits including more personalization, tailored products, and online marketplaces.

30

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

Regulatory Applications The Financial Conduct Authority established RegTech in 2015. This branch of FinTech focuses on developing technology that makes it easier to deliver regulatory requirements. This industry uses novel solutions to improve compliance and deliver safe, economical, and simple-to-integrate laws. It intends to automate some functions like risk management, transaction monitoring, and regulatory reporting, as well as standardize and enhance the transparency of regulatory processes.

Lending This field focuses on creating software programs that give consumers and organizations financing options. The key objective is to customize and accurately streamline the procedure. These technologiesaid in predicting revenue prospects, evaluating the borrower’s history, valuing collateral, and projecting changes in their capacities.

Trading FinTech is modernizing the trading sector. These approaches aim to lower trade expenses, improve trading transparency, and ease trade financing. With IT systems and distribution channels, some of the most outstanding applications are used for cross-border business. In addition, tools like robot advisors may greatly aid novice investors in risk management.

FinTech Applications Challenges Some of the critical challenges that the FinTech industry faces include.

Data Security Data Security is the main challenge for the FinTech industry (Hernandez et al., 2019). Digital banking, payment apps, or FinTech, data security has

An Overview of FinTech in India

31

emerged as one of the top issues in the virtual world. Traditional banking is confident in keeping its data secure with security guards, CCTVs, vaults, and massive bulletproof doors.

Lack of Mobile and Tech Expertise Some financial institutions, such as banks, in the FinTech sector require more suitable or useful mobile banking capabilities. In today’s digital age, individuals would only prefer a mobile application, despite some banks’ attempts to replicate websites. Therefore, a lack of knowledge in the development of financial mobile apps results in unfriendly applications that do not make the greatest use of mobile devices. These traits and technologies allow a FinTech bank to deliver top-notch client experiences.

Compliance with Government Regulations Banks are one of the most regulated sectors. There will always be government interference even when using standard FinTech software.

Big Data and Artificial Intelligence Integration 82% of US bankers and 79% of global bankers agree that artificial intelligence (AI) will help banks interact with their customers and collect information. Businesses can use big data to compile personal information on customers, such as social standing, financial behaviour, dietary preferences, and app usage.

Blockchain Integration Blockchain integration is a powerful tool that creates an unchangeable record of transactions and may be used to boost stakeholder confidence, enhance security, and optimise business processes. Costs are reduced by eliminating intermediaries and ensuring that transactions are carried out quickly and accurately.

32

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

Protection of Personal Information Since many financial technology businesses employ Blockchain technology to assist companies with data protection, every financial institution tries to keep their data secure when they make it available online.

Future of FinTech FinTech firms in India have a bright potential. The market is anticipated to expand quickly in the upcoming years. The government’s attempts to promote digital payments and the expansion of the middle class are anticipated to fuel the industry’s growth. Additionally, the industry is anticipated to benefit from the growing use of mobile phones and the Internet.

Blockchains Blockchain technology promises to facilitate fast, secure, low-cost international payment processing in business networks. Blockchain technology plays an important role in financial services.

Robo Advisory Intermediaries were crucial in connecting investors with the stock market in the former. Robo advising will increase the value added for savvy investors and make the stock market easier to access, transparent, and traceable.

Digital Payments Payments are now processed more quickly and conveniently thanks to FinTech startups. With improved and faster payment options, mobile wallets will continue to displace traditional wallets in many regions.

An Overview of FinTech in India

33

Insurance Sector Consumers may compare their insurance coverage on several Internet marketplaces and make informed judgments. Through automation powered by data, FinTech will further advance a technological revolution in the insurance value chain, lowering operating costs while expanding the range of products on the market.

Conclusions and Recommendations The COVID-19 incident, which has significant macroeconomic ramifications for both India and the rest of the world, has been called the century’s “black swan event” (Ahuja et al., 2022). The coronavirus epidemic has significantly raised FinTech companies’ hurdles; yet, the current environment also offers the opportunity to make money via telemedicine and insurance goods. FinTech first emerged during the 2008 financial market crisis and has since developed. This chapter explained the overview of FinTech: evolution, top ten FinTech companies, benefits of FinTech, types, applications, challenges and the future of FinTech. FinTech emerged as a result of globalization, which allowed for the growth of financial services without the assistance of banks by integrating finance and information technology and providing customers with faster execution of conventional banking services. In the FinTech sector, there are numerous obstacles that we still need to overcome. FinTech companies face challenges from policymakers and different government policies. To upend the financial sector, we must always balance adherence to the established system and technologies.

References Ahuja, A., & Joshi, R. (2018). Customer Perception towards Mobile Wallet. IJRDO Journal of Business Management, 4(1), 44-52. Ahuja, A., Patheja, S., &Sindhwani, N. (2022). Impact of COVID-19 on Various Sectors. Infectious Diseases and Microbiology, 33(1), 45-55. Alam, N., Gupta, L., Zameni, A., Alam, N., Gupta, L., &Zameni, A. (2019). Digitalization and disruption in the financial sector. FinTech and Islamic Finance: Digitalization, Development and Disruption, 2(1), 1-9. Anis, B. J., Christiananta, B., &Ellitan, L. (2018). The effect of entrepreneurship orientation, information technology, strategic planning to competitive advantages with

34

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

Business performance as intervening variables: empirical study food processing SMEs in north Sulawesi. International Journal of Advanced Research (IJAR), 6(1), 236-242. Arner, D. W., Barberis, J., & Buckley, R. P. (2015). The evolution of FinTech: A new postcrisis paradigm. Geo. J. Int’l L., 47(1), 1271. Arner, D. W., Zetzsche, D. A., Buckley, R. P., &Barberis, J. N. (2017). FinTech and RegTech: Enabling innovation while preserving financial stability. Georgetown Journal of International Affairs, 5(1), 47-58. Belanche, D., Casaló, L. V., &Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 7(3), 55-78. Bhasin, N. K., & Gulati, K. (2021). Challenges of COVID-19 During 2020 and Opportunities for FinTech in 2021 for Digital Transformation of Business and Financial Institutions in India. In E-Collaboration Technologies and Strategies for Competitive Advantage Amid Challenging Times (pp. 282-299). IGI Global. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/10. 3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India - The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Cai, C. W. (2018). Disruption of financial intermediation by FinTech: a review on crowdfunding and blockchain. Accounting & Finance, 58(4), 965-992. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/10.18280/ ijsdp.170802 . Darmansyah, D., Fianto, B. A., Hendratmi, A., & Aziz, P. F. (2020). Factors determining behavioral intentions to use Islamic financial technology: Three competing models. Journal of Islamic Marketing, 12(4), 794-812. Devkota, N., Shakya, B., Parajuli, S., & Poudel, U. (2021). Using Plastic Money (Cards) in Kathmandu Valley: Users’ Knowledge, Current Use, Challenges and Way-forward. International Journal of Business, Management and Economics, 2(2), 102-117. Duy, P. T., Hien, D. T. T., Hien, D. H., & Pham, V. H. (2018, December). A survey on opportunities and challenges of Blockchain technology adoption for revolutionary

An Overview of FinTech in India

35

innovation. In Proceedings of the 9th International Symposium on Information and Communication Technology (pp. 200-207). Fosso Wamba, S., Kala Kamdjoug, J. R., EpieBawack, R., & Keogh, J. G. (2020). Bitcoin, Blockchain and FinTech: a systematic review and case studies in the supply chain. Production Planning & Control, 31(2-3), 115-142. Gabor, D., & Brooks, S. (2017). The digital revolution in financial inclusion: international development in the FinTech era. New political economy, 22(4), 423-436. Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the FinTech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of management information systems, 35(1), 220-265. Gozman, D., Liebenau, J., & Mangan, J. (2018). The innovation mechanisms of FinTech start-ups: insights from SWIFT’s innotribe competition. Journal of Management Information Systems, 35(1), 145-179. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Hernández, E., Öztürk, M., Sittón, I., & Rodríguez, S. (2019). Data Protection on FinTech Platforms. In Highlights of Practical Applications of Survivable Agents and MultiAgent Systems. The PAAMS Collection: International Workshops of PAAMS 2019, Ávila, Spain, June 26-28, 2019, Proceedings 17 (pp. 223-233). Springer International Publishing. Hsieh, Y. Y., Vergne, J. P., Anderson, P., Lakhani, K., &Reitzig, M. (2018). Bitcoin and the rise of decentralized autonomous organizations. Journal of Organization Design, 7(1), 1-16. https://ceoreviewmagazine.com/top-10/top-10-FinTech-companies-in-india/. https://thepaymentsassociation.org/article/FinTech-the-history-and-future-of-financialtechnology. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Kamath, K. V., Kohli, S. S., Shenoy, P. S., Kumar, R., Nayak, R. M., Kuppuswamy, P. T., & Ravichandran, N. (2003). Indian banking sector: Challenges and opportunities. Vikalpa, 28(3), 83-100. Karthika, M., Neethu, K., & Lakshmi, P. (2022). Impact of FinTech on the Banking Sector. Integrated Journal for Research in Arts and Humanities, 2(4), 109-112. Koesworo, Y., Muljani, N., &Ellitan, L. (2019). FinTech in the industrial revolution era 4.0. International Journal of Research Culture Society, 3(9), 53-56. Kumar, J. (2022). Role of the Internet of Things (IoT) in Digital Financial Inclusion. In IoT Based Smart Applications (pp. 363-373). Cham: Springer International Publishing. Kumar, J., & Rani, V. (2022). Journey of Financial Technology (FinTech): A Systematic Literature Review and Future Research Agenda. Exploring the Latest Trends in Management Literature, 1, 89-108. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector

36

Jitender Kumar, Anjali Ahuja, Vinki Rani et al.

banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ ijsdp.180204. Lee, I., & Shin, Y. J. (2018). FinTech: Ecosystem, business models, investment decisions, and challenges. Business horizons, 61(1), 35-46. Lien, N. T. K., Doan, T. T. T., & Bui, T. N. (2020). FinTech and banking: Evidence from Vietnam. The Journal of Asian Finance, Economics and Business, 7(9), 419-426. Machkour, B., &Abriane, A. (2020). Industry 4.0 and its Implications for the Financial Sector. Procedia Computer Science, 177, 496-502. Nair, D., Veeragandham, M., Pamnani, P., Prasad, S., &Guruprasad, M. (2021). Impact of COVID-19 On FinTech Industry. International Journal of Research in Engineering, Science and Management, 4(2), 27-34. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic. org/index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA/NPL in Emerging and Advanced Economies: a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://dergipark.org.tr/en/pub/ yalovasosbil/issue/72655/1143214. Panova, G. (2021). Evolution of Traditional Banks’ business Models. International Review, (1-2), 146-152. Singh, S., Sahni, M. M., &Kovid, R. K. (2020). What drives FinTech adoption? A multimethod evaluation using an adapted technology acceptance model. Management Decision, 58(8), 1675-1697. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Sohns, F., &Wójcik, D. (2020). The impact of Brexit on London’s entrepreneurial ecosystem: The case of the FinTech industry. Environment and Planning A: Economy and Space, 52(8), 1539-1559. Stern, C., Makinen, M., & Qian, Z. (2017). FinTechs in China–with a special focus on peer to peer lending. Journal of Chinese Economic and Foreign Trade Studies. Sung, A., Leong, K., Sironi, P., O’Reilly, T., & McMillan, A. (2019). An exploratory study of the FinTech (Financial Technology) education and retraining in UK. Journal of Work-Applied Management, 11(2), 187-198. Suryono, R. R., Budi, I., &Purwandari, B. (2020). Challenges and trends of financial technology (FinTech): a systematic literature review. Information, 11(12), 590. Susilo, A. Z., Prabowo, M. I., Taman, A., Pustikaningsih, A., &Samlawi, A. (2019). A comparative study of factors affecting user acceptance of go-pay and OVo as a feature of FinTech application. Procedia Computer Science, 161, 876-884. Swartz, K. L. (2017). Stored value facilities: Changing the FinTech landscape in Hong Kong. Journal of Investment Compliance, 18(1), 107-110.

An Overview of FinTech in India

37

Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India’s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ ijsdp.180307. Varga, D. (2017). FinTech, the new era of financial services. Vezetéstudomány-Budapest Management Review, 48(11), 22-32. Vijai, C. (2019). FinTech in India–opportunities and challenges. SAARJ Journal on Banking & Insurance Research (SJBIR) Vol, 8. Wang, J. (2022). Performative innovation: Data governance in China’s FinTech industries. Big Data & Society, 9(2), 20539517221123312. Wonglimpiyarat, J. (2017). FinTech banking industry: a systemic approach. foresight, 19(6), 590-603.

Chapter 3

An Introduction to FinTech Arti Gaur Sanju Verma* and Sweta Bhatti Department of Business Administration, Chaudhary Devi Lal University, Sirsa (Haryana), India

Abstract The world is full of innovation. The speed at which technology has altered the world has been truly astounding, and it appears as though the changes will never end. Along with significant advancements in computer technology, manufacturing, and telecommunications, the new technologically advanced period also had an impact on the financial sector. For individuals with the necessary skills and expertise, FinTech is a burgeoning industry with several job prospects. It is a catch-all phrase used to describe how an industry has progressed in which new technology use-cases are created and implemented to streamline more conventional-looking finance activities. It functions by disentangling these companies’ products and opening up new markets. The creation and application of cryptocurrencies like Bitcoin and Dogecoin also fall under the umbrella of FinTech. The newest financial technologies operate hedge funds and handle credit cards using blockchain, machine learning algorithms and data science. The purpose of this chapter is to provide conceptual information about FinTech and its various platforms, factors driving the rise of FinTech, significance of FinTech, opportunities and challenges facing FinTech companies and governmental programs fueling FinTech.

Keywords: FinTech, bitcoin, machine language, artificial intelligence, financial sector In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

40

Arti Gaur, Sanju Verma and Sweta Bhatti

Introduction The term “FinTech” is an amalgamation of the words “finance” and “technology” and covers all varieties of financial technology (Kovacevic, 2020). FinTech refers to the inclusion of technology into the product and service offerings of financial services firms to improve their usage supply to clients. It effectively works by detaching such enterprises’ services and opening new marketplaces for them. It incorporates the digitisation of financial services commonly provided by banks, credit card companies, credit unions, investment banking firms, and other financial institutions. It was initially intended for use in the back-end systems of reputable financial organizations. FinTech, however, later grew into a sizable financial industry on its own, enhancing and mechanizing the provision of and usage of financial services. In a nutshell, it benefits businesses, entrepreneurs, and consumers by supporting them in more properly implementing their monetary transactions using specialized software and algorithms that were initially used on computers and are now progressively being adapted to smartphones.(6+ reasons why FinTech is important, 2019). Any company that modifies, enhances, or automates financial services for people or businesses is referred to as a FinTech company. Peer-to-peer payment systems, mobile banking, automated portfolio managers, and trading platforms like Robinhood are a few examples of FinTech businesses. It also holds true for creating and dealing in cryptocurrency (like Ether, Dogecoin, Bitcoin). These businesses improve the speed, security, and effectiveness of the traditional financial sectors by integrating technology like blockchain, data science, and AI. FinTech is the field of technology that is expanding the fastest, and it includes companies that innovate in almost every aspect of finance, including stock trading, credit scoring, loans, and payment methods (Emizentech, 2022). FinTech’s larger goal is to fill unmet financial demands of demographic segments who are not the main target markets for conventional financial services models. So, the bigger objective of financial inclusion is something that FinTech strives to support. It was originally intended for use in the back-end systems of reputable financial organizations. FinTech, however, later grew into a sizable financial industry on its own, improving and regulating the delivery of and consumption of financial services. In a nutshell, it benefits businesses, entrepreneurs, and consumers by supporting them in more successfully managing their monetary transactions using specialized software and algorithms that were initially used on computers and are now progressively being adapted to smartphones. FinTech has developed to include a wide range of industries and businesses,

An Introduction to FinTech

41

including retail banking, education, and fundraising, charitable organizations, and many more as it has evolved to focus on consumer-oriented services (6+ reasons why FinTech is important, 2019). According to research (FinTech and Ecosystems, n.d. in Figure 1), a healthy FinTech ecosystem is based on four fundamental ecosystem characteristics: • • •

•

Talent: The accessibility of technical, financial, and entrepreneurial skills Capital: The accessibility of financial resources for new businesses and expanding businesses Policy: Government measures for sector expansion, particularly the use of digital public infrastructure to support financial services innovation, span regulation, taxation, and other policy areas. Demand: End-client demand from businesses like banks and consumers.

Source: FinTech and Ecosystems, n.d. Figure 1. The FinTech Ecosystem.

42

Arti Gaur, Sanju Verma and Sweta Bhatti

The network of broader stakeholders is shown in the picture above, which demonstrates how these four characteristics interact.

Concept of FinTech FinTech makes financial transactions simpler for individuals and organizations, increasing their accessibility and often lowering their cost. It can also be applied to businesses and services that enable extremely secure internal network transactions using AI, big data, and encrypted blockchain technology.

Universal Financial services have never been easier to get than they are now, due to the influence of the internet and cutting-edge technology. All you need to enter the interesting FinTech world is Wi-Fi, regardless of where your company is located. More than 27,000 consumers in 27 regions across six continents were interviewed by EY Global (where 10 out of 27 are emerging markets). It is interesting to note that 46% of the SMEs interviewed utilize a finance FinTech service, while 56% utilize a FinTech service for banking and payments (6+ reasons why FinTech is important, 2019).

Promotes Economic Development According to EY’s Global FinTech Adoption Index 2019, the use of FinTech services increased from 16% in 2015 to 33% in 2017, and ultimately to 64% in 2019. Furthermore, according to a January article from CNBC, in 2018, FinTech firms attracted a record $39.6 billion. More precisely, because of money transfers and payment apps, the adoption rate is 95% in China alone (6+ reasons why FinTech is important, 2019). This demonstrates that FinTech has created new, lucrative occupations in addition to contributing to the expansion of the economy.

An Introduction to FinTech

43

Empowering Businesses FinTech provides solutions from which all organizations stand to gain significantly. More than well-established businesses, SMEs depend on economic growth, but they frequently have trouble getting the funding they require to grow. Innovative FinTech products offer small and medium-sized enterprises have a broad range of financial choices and are effective and profitable. Marketplace financing, merchant and e-commerce finance, invoice finance, online supply chain finance, and online trade finance are all customized to suit the requirements of small businesses. “FinTech has a significant role to play in decreasing poverty, creating jobs, promoting gender equality, and improving food security,” Additionally, it will deliver better data, which will reduce bordering countries’ projected risk level and increase the amount of money available to the underbanked. In other words, FinTech may revitalize economies.

Aids Businesses in Transforming Big Data into Meaningful Data Businesses can collect a huge quantity of big data based on their clients, sales, website traffic, and a range of other data indicators. For the data to be helpful to the business, they must understand how to use it to their advantage. FinTech develops tools and procedures that transform the data into usable data, assisting businesses of all sizes in understanding and managing the information they acquire. Businesses can examine patterns, trends, or relationships in this way. Additionally, it can generate reports that assist in tracking fresh perspectives and helpful data, allowing users to subsequently develop successful industry-specific plans.

Makes Financial Inclusion Possible Financial services are now accessible to everyone thanks to FinTech, which has also made them universally available. Traditional retail banking is losing popularity, whereas advanced interactive competition in the finance business is on the rise.As a result of the incredible creation of novel unified software skills, the financial services industry has undergone an unprecedented metamorphosis. Finally, individuals who reside in countries without a traditional banking framework can now obtain financial support digitally.

44

Arti Gaur, Sanju Verma and Sweta Bhatti

Various FinTech Platforms 64% of people globally will have used at least one FinTech platform by 2020(What is FinTech? why is FinTech important in 2021, n.d.). Based on their rate of acceptance, the following categories in FinTech are the most wellliked ones:

Transfers and Payments of Money It is simple to install and use this category. Peer-to-peer transfers of funds, mobile payments made in-store, and phone money transfers are all included in this category. Stripe, PayPal, and Venmo are among of the most well-known and profitable services in this area.

Investment And Saving This category of software and applications has had a significant increase in 2020. Customers want to raise their capital during a crisis, not spend it, therefore it makes perfect sense. For instance, Revolut has simplified stock investment so that it is as simple as purchasing a bottle of water in a store. With a few easy clicks and no fees, you can buy stock from more than 800 firms.

Insurtech Technology and insurance combine to remove the procedures involved in purchasing an insurance plan in person. Although the insurance industry is among the oldest in existence, it was also upset by the introduction of technology; insurtech.

RegTech RegTech is a subtype of FinTech that focuses on technology that could make it easier and faster to meet regulatory obligations than present capabilities,

An Introduction to FinTech

45

according to the Financial Conduct Authority’s definition from 2015. RegTech is the use of revolutionary technology to improve adherence and the application of simple, secure, and cost-effective rules. This area is being streamlined and simplified. New legal systems are required to keep up with the changes in a society where money is dominated by numerous technological developments. RegTech is largely used to automate the entire compliance system by standardizing and promoting transparent regulatory processes. Regulatory monitoring, risk assessment, monitoring procedures, and adherence are just a handful of the regulatory solutions availablethat are being delivered with regtech (Meti, 2022).

LendTech This field uses software to provide clients with financial answers that are faster and more precise. Intelligent systems that verify and authenticate identity credentials employ AI and ML approaches to deliver error-free outcomes. It is simpler to predict income projections, assess the borrower’s history, determine the value of the collateral, and anticipate changes when technology is used in loan procedures.

TradeTech In its simplest form, trade technology (trade tech) is the use of digital technologies to lower global trade transaction costs, simplify trade finance, and enhance accountability in trading procedures for both business models and users. Realizing its full potential and benefits need international cooperation. TradeTech facilitates and supports international trade by utilizing IT platforms for supply chain finance and asset distribution.

Apps for Stock Trading and Robotic Advice Robo-Advising and Stock Trading Apps applying technology, users can create and access diversified investment portfolios without the help of an investing expert or adviser. Robo Advisors is a risk management solution designed for unskilled participantsand expert investment management. Robo-Advising and Stock Trading Apps Using technology, users can create and access diversified

46

Arti Gaur, Sanju Verma and Sweta Bhatti

investment portfolios without the help of an investing expert or adviser. Robo Advisors is a technology designed to assist unskilled users with risk managementand expert investment management. Trading programs for stocks make buying and selling stock assets simpler and more effective than using conventional methods. Any online broker with the necessary capabilities uses these share trading applications are used to trade investments and assets. The US has apps for stock trading and investing, such as Finch Money.

WealthTech This area of FinTech focuses on enhancing wealth management and retail investment services by using technology to support and supply operations in a more effective and automated manner. In order to make them accessible to new investor groups, these digital solutions are used to establish new ones and enhance existing ones. The personal finance app Monie is an illustration for Egypt. WealthTech enables the simplification of the investing process, enabling investors to manage their investment portfolios more conveniently. Using Micro-investment, Robo-retirement, Portfolio administration systems, and other technology, WealthTech is being introduced into the financial industry.

BankTech Several financial institutions are utilizing digital technology to provide offerings in a more simplified and effective manner. BankTech involves employing digital channels to provide banking services and goods to clients.In comparison to conventional banking practices, bank technology has several benefits, including a better customer experience, cheaper costs, and less operational friction.

Accounting Machine learning, artificial intelligence, cloud computing, digital tax platforms, and other technological breakthroughs are already being leveraged to boost accounting mechanization and accessibility. With software and tools, the technological advances in this sector of finance have expanded internet

An Introduction to FinTech

47

connectivity and analysis. With the advancement of financial technology and software, accountants’ time spent on tasks such as invoice management, cash flow prediction, and other accounting services has dropped.

Equity Financing One method of raising capital is to sell stock in a firm to the wider populace, financial institutions, or shareholders. The money obtained is then used to expand already existing businesses or to launch new ones. By using technology, crowdfunding might be able to reach a broader group of potential investors. Prizes are offered to participants on crowdsourcing websites like Kickstarter, Pebble, and many more.

Cryptocurrency and Blockchain FinTech and blockchain are excellent examples of how financial technology is impacting the spread of financial products because of the affordability of payment systems, proof-of-work, peer-to-peer payments, blockchain-powered trading platforms, networked ledgers, and immutable information. Financial transactions can now be tracked more conveniently, securely, and transparently throughout their entire lifecycle thanks to blockchain technology. The decentralized and dispersed character of cryptocurrency, enabled by Blockchain technology, adds to its appeal and consumer trust. The development of blockchain technology is still in its infancy, and new directions are being investigated through more research. Blockchain technology is used by many different platforms, like Ethereum, Bitcoin, Chain, Bloq, Wirex, and many more (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al. 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d).

48

Arti Gaur, Sanju Verma and Sweta Bhatti

Factors Driving the Rise of FinTech Greater Use of Mobile Devices The growing use of mobile devices is one of the main factors fueling the expansion of the FinTech industry. In actuality, the Pew Research Center has discovered that more than 77% of American adults now own a smartphone. Mobile banking and other financial services based on mobile devices have increased due to this. For instance, practically every form of transaction now has a mobile payment option, from credit cards to internet shopping. A recent Business Insider investigation found that Americans spent more than $100 billion on mobile devices in just one year. An increase in the number of consumer-focused apps is a result of increased smartphone use. The requirement to protect an API to create mobile apps cannot be overstated as technology develops and more people depend on cell phones for daily tasks (Park, 2022).

Growth of Digital Payments Another element driving the growth of the FinTech business is the increasing adoption of electronic payments. The expansion of mobile payment options andother online and electronic payment methods is evidence of this. For instance, according to a recent study, the value of digital payments worldwide increased from $5058.96 billion in 2020 to $5872.89 billion in 2021. Numerous causes, including the rising popularity of online shopping and the growing use of smartphones and other mobile devices, are fuelling this trend (Park, 2022). Additionally, because these methods offer higher security and lower prices, more businesses are adopting digital payments. FinTech firms that provide cutting-edge payment solutions will continue to prosper as demand for digital payments rises.

Concentrate on Banking in Underserved Areas The wealth and asset management sectors of banks’ businesses have received a lot of attention recently because of their rapid previous expansion (Park, 2022). FinTech companies are now attempting to enter other markets that have

An Introduction to FinTech

49

been ignored as a result of this focus, such as mortgages, student loans, and small business finance. In addition, the regulatory process has changed over the past several years, forcing banks and other financial services providers to figure out how to provide their clients products that satisfy compliance standards while also being cutting-edge.

Application Programming Interfaces APIs are another emerging trend that FinTech companies are utilizing to develop their services and products around current financial infrastructure. The FinTech industry has seen a rise in interest in APIs. Today, considering API security is essential when buying and financing a product. Banks are beginning to make their systems more accessible so that other businesses can create goods and services that integrate with them. Due to the potential for growth, this has caused a rise in start-ups and venture investor involvement in this industry.

Significant Capital Available The fact that venture capitalists are beginning to recognize the potential for growth in this industry is another factor contributing to the high quantity of funding available for FinTech start-ups. To stay on top of the game, banks are also making strategic investments in emerging firms. The growth of FinTech is being fueled by several causes. This includes the emphasis on underserved areas, the evolving regulatory landscape, APIs, and customer experience. As a result, FinTech companies have access to enough of funding to help them develop and grow (Park, 2022).

Greater Adoption of Technology Companies are implementing AI and big data to give their customers a better overall experience. Big data and AI are being used by FinTech companies to personalize services and reduce fraud while improving risk detection, automating trade, and facilitating safe payments. FinTech players are embracing blockchain to increase security as access to newer technology becomes more widespread. A blockchain-based credit system for SMEs in

50

Arti Gaur, Sanju Verma and Sweta Bhatti

India has been developed and will soon be implemented by a number of Indian banks, including Kotak Mahindra Bank, HDFC Bank, ICICI Bank, and Axis Bank.

Lending Platforms Online lending services that rapidly and digitally process consumer and company loans have recently seen a rise in popularity. Through the disruption of the conventional lending process, these platforms have increased access to credit for underserved and underbanked populations. These online lending companies use technology to assess borrowers’ creditworthiness and give them the funding they require. These loan platforms offer a smooth funding process with little to no documentation. Clix Capital, Lendingkart, and ZipLoan are a few Indian lending platforms.

Significance of FinTech Due to the FinTech revolution, companies are no longer constrained to using traditional or outdated methods. One can find a wide variety of possibilities and alternatives today, from crowdsourcing to net banking to mobile payments. With the aid of FinTech, anyone can now, essentially, start their own business in no time (What is FinTech and why is it important, 2022).

Fintech Fosters Economic Growth and Inclusion Businesses have more options to diversify and offer innovative, efficient, automated solutions thanks to the FinTech sector. FinTech companies saw great growth as a result of the pandemic. According to a recent study, nations with higher rates of digital financial inclusion also see faster yearly GDP growth. FinTech is now available to folks who have never used such financial services previously because it is simple for anybody to utilize. People all throughout the world, even in such regions as Africa, have the chance to handle their money more easily (Reasons why FinTech is important, 2022). Some international FinTech companies offered mobile banking or electronic

An Introduction to FinTech

51

wallets, which support the development of a financially inclusive society and assist pull struggling individuals out of poverty.

More Economical Alternative When compared to traditional banking, FinTech firms frequently offer the same high-quality services at a lower cost. Some banks assist you keep more money in your wallet by not charging for a bank account because there are no physical branches. There are other commission-free stock trading apps available. Additionally, FinTech businesses automate numerous operations by utilizing technology and its prospects. In doing so, they avoid spending money on labor costs. Unbanked customers who choose to only use digital alternatives have a better probability of being drawn in if these transactional expenses are reduced. Because they support many currencies, have low conversion costs, and allow you to manage multiple bank accounts in one app, digital banking solutions are user-friendly. Digital payments demonstrated how simple it is to make purchases online or move money from one bank to another.

FinTech Ensures Transparency and Compliance When it comes to implementing efficient, automated security procedures, traditional institutions lag behind. Financial institutions must abide by AML legislation, which are essential to the KYC process and assist prevent fraud as well as reduce risk. An effective KYC procedure entails determining the customer’s genuine identification as well as any potential dangers they might present. Since automatization, artificial intelligence, and technology in general form the basis of FinTech, such forward-thinking businesses naturally select digital solutions. For instance, companies must employ completely automated identification verification to address compliance difficulties and guarantee that only legitimate, real consumers are registered for online banking services (Reasons why FinTech is important, 2022). Only quick, real-time, easy-to-use, secure authentication and document checks can deliver the essential benefits that today’s technologically savvy clients want.

52

Arti Gaur, Sanju Verma and Sweta Bhatti

FinTech Enhances and Boosts Business The most prosperous FinTech businesses discovered fresh approaches to enhance the advantages of current technologies. Finding a larger purpose for technology is significantly more difficult than simply integrating it into your business procedures. This suggests that the actual technology themselves are less significant than how you use them. FinTech is well renowned for helping to streamline several procedures and educating clients on how to handle their money more effectively. Finance-related apps are a good illustration of a FinTech initiative with an empowering goal. They are constructing the financial system of the future while simultaneously raising people’s financial literacy. In this way, many people learn more about the worth of money and how to set aside money for future investments, due to FinTech and its improved financial capabilities.

Shaping Current Financial Sector FinTech is becoming the standard. Businesses must prioritize improving payment systems and perfecting their security systems in order to survive intense competition. You could say that FinTech has had a significant impact on how we manage our finances and keep data. FinTech software is a requirement if you want to stay in business and meet the high expectations of your consumers. Otherwise, your ineffective processes may be costing you regular customers. For instance, the majority of ordinary consumers anticipate quick and easy money transfers. Many people are curious about their 24-hour profit from stocks or cryptocurrencies. Naturally, the list continues.

Opportunities of FinTech Cybersecurity Over the coming ten years, cybersecurity is anticipated to have a significant impact on financial services. One of the main reasons why digital transformation will continue to be an ongoing process is to keep up with new and constantly changing security risks. Companies should strike a balance between transparency and security. Numerous new technologies have the potential to advance cybersecurity. Many of humanity’s problems have been

An Introduction to FinTech

53

attributed to artificial intelligence, which has been hailed as the panacea. ML and DL, both of which are based on AI, have enormous potential for combating cybercrime. Similar to this, other technologies like quantum cryptography and blockchain are essential in thwarting sophisticated attacks (Pavithra, 2021).

Big Data and Analytics Because of digitalization in the banking sector, disruptive technologies such as big data, the cloud, artificial intelligence, machine learning, and predictive analysis have been able to permeate and modify how financial institutions operate in the industry. Data and analytics have grown in relevance to businesses in recent years as they have matured continuously. Big data and analytics are increasingly employed to deliver more personalized and targeted user experiences. Businesses utilize data and analytics to be competitive because they enable better operations, increase income, predict client demands, provide customized product offerings, and forecast demand. As the financial sector rapidly transitions toward data-driven optimization, businesses must respond to these developments in a structured and proper way.

Partnerships Collaborations between banks and FinTech companies are anticipated to prosper as long as the FinTech sector is healthy and startups are still growing. These types of collaborations have a complex economic rationale. The cost of acquiring clients using conventional inorganic means has always been high. Collaborations allow for organic growth through product creation and geographic expansion. They can also assist businesses in adapting to evolving consumer demands, which prioritize service, digital accessibility, and brand loyalty. Additionally, collaborations can aid traditional companies in fending off the danger posed by young, agile FinTech.

Digital Banks The banking sector now has the opportunity to adapt for a larger customer base, new markets, and products thanks to digital channels. With the pandemic

54

Arti Gaur, Sanju Verma and Sweta Bhatti

and social alienation, the expansion of digital banks has accelerated and is anticipated to continue in 2021. Consumers are strongly encouraged to switch to digital-only banks by better services and lower fees.

Robotic Process Automation RPA is one of the most effective techniques to manage financial transactions, according to experience. RPA can also refer to bots, thus it’s not strictly necessary for the process to be automatic. RPA’s rise is undeniably linked to the fact that they provide an excellent user experience and intelligent wealthmanagement coaching at a reasonable price. Robo-advisors are becoming increasingly important. Consumers are eagerly awaiting smart investment opportunities and in-depth financial evaluations in order to capitalize on current conditions. To capitalize on this excellent potential, businesses must prepare to deliver major updates with robo-advising services. They provide banking sector services such as customer assistance, account opening, and other economic transactions (FinTech challenges and opportunities, 2022).

Blockchain Technology Due to its rapid spread and adoption, blockchain is quickly becoming an important element in financial organizations’ ongoing maintenance, including electronic payments, trading platforms, cryptographic protocols, and identity management. Financial institutions are utilizing blockchain more swiftly as a result of its global reach, speed, and security. FinTech companies need to build trust and show transparency in transactions and the supply chain. By employing blockchain, they might have visibility across the entire supply chain. It also manages performance benchmarking and quality assurance. Financial services would immediately incorporate blockchain into their operations and search for chances to boost FinTech (FinTech challenges and opportunities, 2022).

Personalization Personalization and banking are inextricably linked. Customization in finance always benefits businesses. In the financial services industry, personalization

An Introduction to FinTech

55

is the act of offering a beneficial service or product to a consumer according to their interests and results from previous interactions. Because of the epidemic, financial firms are now obligated to prioritize essentials over niceto-haves. A tailored relationship fosters trust as well. Improving client satisfaction and increasing sales are the primary motivations for integrating online revolution. Nowadays, financial institutions fight not just among themselves, but also with technological behemoths. To stay up with the changing environment and obtain a better knowledge of its customers, the financial services industry must reevaluate its campaign assessment method.

Embedded Finance Embedding financial tools or services into a non-financial company’s offerings is referred to as embedded finance. In addition to streamlining the purchasing process and removing barriers, embedded finance improves consumer experience overall. Online retailers who offer loans or BNPL alternatives on their website or app are some examples of embedded finance. FinTech companies are beginning to offer BaaS solutions due to the rising need for embedded financing. Even if you may easily provide these services through APIs, you must develop a solid risk and compliance strategy as a partner in integrated finance for businesses (Sinnott, 2022).

Artificial Intelligence Since the FinTech industry is recognized for handling large volumes of data, AI technologies are useful for gathering and storing such data. The company can use several AI applications to support your operations depending on customers’ demands. It can tailor financial services or advise to each client’s needs, for instance, by studying user activity. As an alternative, AI algorithms can assist them in making forecasts to guide wise business decisions. The ability to identify probable fraud in transactions and reduce the possibility of human mistake is one example of how to make predictions for risk assessment.

56

Arti Gaur, Sanju Verma and Sweta Bhatti

Sustainable Finance Sustainable or green finance is another significant opportunity you can grasp given the increased focus on environmental protection as a result of climate change. FinTech businesses are increasingly catching up to the banking industry, which has been actively integrating environmental, social, and governance measures. Businesses in the sector are specifically looking for methods to integrate sustainability into their operations, whether through boosting climate-friendly investments or reducing carbon emissions. They can motivate people to act and be more aware of their environmental impact in this way.

Decentralized Finance Due to the widespread use of cryptocurrencies and blockchain, the idea of DeFi, gained steam in the FinTech industry. DeFi is an acronym for a new technology that manages financial transactions without the use of middlemen by using a distributed ledger. DeFi is already being included into interfaces in FinTech, so we can anticipate that it will play a bigger part going forward.

Challenges Facing FinTech Companies FinTech start-ups across the nation deal with a variety of challenges every day. Below, some of the challenges are explained.

Changing Business and Revenue Models FinTech companies have been compelled to reconsider their revenue and expense models and change or add to their resource bases due to the continuous economic slump and draconian interest rate reductions. To cope with the burden of the economy, several organizations are pursuing costcutting initiatives such as downsizing and wage reductions. Many employees of other finance firms, such as Kabbage, have previously gone on leave. Financial institutions that accept contactless payments are redeploying manpower to deal with the increased trade volume (Agarwal, 2022).

An Introduction to FinTech

57

Legal Regulation and Compliance Many laws inevitably hold down FinTech start-ups in the Indian financial sector. These laws are hard to comply with, making it difficult for FinTech firms to enter Indian markets. Compliance standards are established as a robust regulatory framework to combat fraud. But they, too, pose considerable barriers to entry for new FinTech businesses (6 key challenges that FinTech start-ups face in India in 2020). FinTech start-ups should meet a slew of regulations before they can begin operations.

Unbanked and Underbanked Population FinTech originally encountered unequal growth in India because of inadequate facilities such as poor internet users and access to education. Although the Indian government is tackling these issues with liberal policies, the benefits will take some time to become visible (six key challenges that FinTech startups in India will face by 2020). Another impediment to the growth of FinTech in India is the country’s low level of financial education. India, for example, created the Pradhan Mantri Jan Dhan Yojana to improve financial inclusion. According to a World Bank report, even though 180 billion bank accounts were created, more than 48% of them sat inactive for an entire year without being used. Notwithstanding all the initiatives, India is still far from financial inclusion.

Trust in Cash Regarding daily transactions, most Indians adopt a conservative approach and opt for cash. Companies have long relied on cash as a sales medium, making it difficult to shift their routines and adopt innovative tools (6 key challenges that FinTech start-ups face in India, 2020). Financial services are difficult to provide in an unbanked market since they are commonly associated with internet scams. Due to their lack of financial literacy, many Indians are unable to recognize the value that FinTech provide through their cutting-edge goods and services.

58

Arti Gaur, Sanju Verma and Sweta Bhatti

Cyberthreats FinTech businesses handle private customer information. Online transactions experience significant financial losses due to several cybersecurity concerns. These are completely unjustified for the customers. The same technology that makes life more convenient also makes it easier for thieves to access people’s internet accounts. This continuous flow has contributed to the success of FinTech (6 key challenges that FinTech start-ups face in India, 2020). It must strengthen their defences against any threat provided by hackers. Digitally accessible financial information on people and businesses is enormous. The likelihood of cybersecurity breaches rises as a result.

Complexities Specific to the Industry FinTech are made to function using a complex working model. They find it challenging to keep good ties with other financial institutions like banks as a result. Conversely, banks are hesitant to collaborate with FinTech out of concern for their reputation.

Lack of Government Assistance Government incentives and assistance for FinTech to safeguard their interests in the Indian financial markets are severely lacking. For new FinTech players, this can be very discouraging. FinTech are essential for generating economic growth and must be provided with the necessary resources to succeed.

Data Security Data security has emerged as one of the main challenges in the digital age, whether it is mobile banking, payment apps, or FinTech. As we all know, traditional banking organizations are confident in their capacity to keep their information confidential by using security officers, CCTVs, vaults, and big bulletproof shutters. However, things are not as simple as we might imagine when it comes to virtual security. Consumers may be more affected by

An Introduction to FinTech

59

vulnerabilities because they risk not only their currency but also their private information, making them much more subtle and perhaps more harmful.

Lack of Knowledge of Mobile Technologies Some banks or financial organizations in the FinTech sector lack adequate or practical mobile banking capabilities. Although some banks attempt to mimic websites, nobody would choose a mobile application in today’s digital environment. Every user seeks a simple and practical alternative. Therefore, a lack of finance mobile app development experience leads to unfriendly applications that don’t use mobile devices best. These characteristics and technology enable a FinTech bank to provide outstanding customer experiences.

Customised Services Companies, as is widely known, strive to adjust and provide personalised services. Despite being the most critical and necessary element of banking, businesses struggle to supply it. Personalisation in today’s context means communicating with a customer in real-time via their channel of choice. Consumers define personalised services as specially tailored to their specific requirements. They are unwilling to reach a deal on any other grounds. Consumers are also willing to accept FinTech as a financial wellness counsellor. For some, having so many options can be intimidating. Yet, good customisation ensures that consumers only see the the relevant options (FinTech challenges and opportunities, 2022).

Governmental Programs Fuelling FinTech Demonetization in India boosted the FinTech Sector in 2016 and resulted in exponential growth. Additionally, the government is accelerating the switch from paper to electronic payments through tax rebates and decreasing transaction costs. The government’s considerable actions and activities have led to the Indian FinTech ecosystem’s rapid expansion.

60

Arti Gaur, Sanju Verma and Sweta Bhatti

Digital India The Government of India started the Digital India program to allow all citizens able to access government services online. Through the launch of numerous online services such as the Inclusive India Campaign, Bharat Interface for Money, E-Panchayat, E-Hospital, etc., the program sought to promote increased reach and accessibility while also developing a robust and secure digital infrastructure (FinTech startups in India- factors driving the growth of FinTech industry in India, 2021).

Jan Dhan Yojna Prime Minister Narendra Modi launched the Pradhan Mantri program on August 15, 2014. The Jan Dhan Yojana initiative aims to enhance Indians’ utilisation of financial amenities such as bank deposits, remittances, credit, insurance, and pensions at a low cost(Pradhan Mantri Jan Dhan Yojana National Mission for Financial Inclusion, n.d.). Numerous people have benefited from the effort, and as of August 10, 2022, 46.25 crore accounts have been opened under PMJDY, as mentioned in Figure 2.

Source: Pradhan Mantri Jan Dhan Yojana - National Mission for Financial Inclusion, n.d. Figure 2. Accounts opened under PMJDY.

An Introduction to FinTech

61

Unified Payments Interface UPI has grown in popularity and is now considered one of the greatest achievements of the Indian payment’s infrastructure. In just five years, UPI was able to seize a 73% market share of the total amount of digital transactions. The expansion of UPI has inspired a number of private firms to offer substitutes for digital payments, fundamentally changing the Indian economy (FinTech startups in India- factors driving the growth of FinTech industry in India, 2021). In June 2021 alone, Indians made 2.8 billion transactions, or 280 crore transactions, totaling Rs 5,47,373 crore, a 10.6% increase in volume and an 11.56% increase in value from May.

Trade Receivable Discounting System The RBI launched ‘The Trade Receivable Discounting System,’ an online billdiscounting tool, in 2017 to increase liquidity for small enterprises (FinTech startups in India- factors driving the growth of FinTech industry in India, 2021). TReDS allows cash-strapped MSMEs to raise capital by selling commercial accounts receivable. During the COVID-19 outbreak, TReDS adoption increased among corporations and MSMEs. According to Ketan Gaikwad, MD and CEO of Receivables Exchange of India, during the prior fiscal year, 44% of all TReDS bill discounted transactions amounted to Rs 17,153 crore.

India Stack India Stack is a social initiative that intends to build public online facilities to help both public and private online visibility, such as the quickening of technology adoption in the financial sector (Key initiatives taken by government for FinTech ecosystem in India, 2021). In an economy with a high reliance on cash for retail transactions, the India Stack is extending access to financial services. The price of verifying people’s identities is drastically reduced with a digital ID card. Digital payments between banks, FinTech companies, and digital wallets are made possible by open-access software standards. Additionally, consent regulates who has access to a person’s personal information (Swallow et al., 2021). The growth of digital payments, made possible by the stack, is a key factor in India’s economic progress. It has

62

Arti Gaur, Sanju Verma and Sweta Bhatti

helped stabilize rural incomes and increase sales for businesses in the unorganized sector.

License for Payments Banks The License for Payments Banks has advanced the country’s attempt to expand financial inclusion by allowing the establishment of banking institutions and extending access to payment services. In a move to strengthen online payment banks in the country, the RBI has raised the maximum endof-day balance for payment banks to Rs. 2 lakhs.

Regulatory Sandbox The Reserve Bank of India created the regulatory sandbox to encourage sustainable creativity in the financial services industry, increasing efficiency, and providing advantages to clients. The RS enables regulators, developers, financial service providers (as potential technology deployers), and customers (as end consumers) to conduct field testing to collect data on the benefits and risks of modern funding breakthroughs while actively watching and limiting their risks. The regulatory sandbox is a crucial instrument for creating more adaptive, evidence-based regulatory regimes that grow and change alongside new technology.

Conclusion India’s FinTech industry has witnessed remarkable growth and transformation in recent years. With the government’s push for digitalisation and financial inclusion, coupled with a burgeoning middle class and widespread smartphone penetration, the FinTech sector has emerged as a critical driver of financial innovation and access in the country. FinTech companies have revolutionized how Indians transact, save, invest, and access credit, empowering individuals, and small businesses with convenient and affordable financial services. As the industry continues to evolve, it holds immense potential to address longstanding challenges, foster economic growth, and create opportunities for millions of Indians, ultimately shaping the future of finance in the nation.

An Introduction to FinTech

63

References Agarwal, H. (2022). New challenges and opportunities for FinTech companies post COVID-19. Beaconstac RSS. Retrieved December 14, 2022, from https://blog. beaconstac.com/2020/05/FinTech-companies-post-covid/ Benefits of FinTech for businesses and startups. SpdLoad. (2022). Retrieved December 13, 2022, from https://spdload.com/blog/benefits-of-FinTech/ Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629 Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/ 10.3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Bhatt, C. (2022). 8 key challenges FinTech industry faces and their solutions. Techtic Solutions. Retrieved November 29, 2022, from https://www.techtic.com/ blog/FinTech-industry-challenges/. Committees. Welcome to FinTech Convergence Council. (n.d.). Retrieved December 7, 2022, from https://www.FinTechcouncil.in/. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802. Emizentech. (2022). What is FinTech? A brief introduction. Tech Blog | Mobile App, eCommerce, Salesforce Insights. Retrieved November 27, 2022, from https://www. emizentech.com/blog/what-is-FinTech.html. FinTech and Ecosystems. EY. (n.d.). Retrieved December 20, 2022, from https://www. ey.com/en_gl/banking-capital-markets/FinTech-ecosystems. FinTech challenges and opportunities. TatvaSoft. (2022). Retrieved November 26, 2022, from https://www.tatvasoft.com/outsourcing/2022/01/FinTech-challenges-andopportunities.html. FinTech startups in India- factors driving the growth of FinTech industry in India. The StartupLab : Compliance Tech Funding. (2021). Retrieved November 23, 2022, from https://thestartuplab.in/FinTech-startups-in-india-factors-driving-the-growth-ofFinTech-industry-in-india/.

64

Arti Gaur, Sanju Verma and Sweta Bhatti

Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)? Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Key initiatives taken by government for FinTech ecosystem in India. TaxGuru. (2021). Retrieved December 18, 2022, from https://taxguru.in/corporate-law/key-initiativestaken-government-FinTech-ecosystem-india.html. Kovacevic, M. (2020). An introduction to FinTech: Examples, uses, benefits. wolf & wolf technologies. Retrieved November 23, 2022, from https://wolf-wolf.net/blog/anintroduction-to-FinTech/. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/ 10.18280/ijsdp.180204 Lele, S. (n.d.). FinTech 2.0: A new era of financial inclusion. PwC. Retrieved December 2, 2022, from https://www.pwc.in/industries/financial-services/FinTech/FinTechinsights/FinTech-2-0-a-new-era-of-financial-inclusion.html#. Meti, V. K. (2022). Different categories of FinTech Applications. Day One: AI Development Services, App Development Company. Retrieved December 16, 2022, from https://www.day1tech.com/different-types-of-FinTech/. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic. org/index.php/jebi/article/view/41 Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA/NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark. org.tr/en/pub/yalovasosbil/issue/72655/1143214. Park, C. (2022). 5 factors driving the rise of FinTech. TMCnet. Retrieved December 14, 2022, from https://www.tmcnet.com/topics/articles/2022/01/13/451193-5-factorsdriving-rise-FinTech.htm. Pavithra R. (2021). 5 trends and opportunities for the FinTech industry to watch out for in 2021. IBS Intelligence. Retrieved December 1, 2022, from https://ibsintelligence. com/ibsi-news/5-trends-and-opportunities-for-the-FinTech-industry-to-watch-outfor-in-2021/. Pradhan Mantri Jan Dhan Yojana - National Mission for Financial Inclusion. Press Information Bureau. (n.d.). Retrieved November 22, 2022, from https://www. pib.gov.in/PressReleasePage.aspx?PRID=1854909. Reasons why FinTech is important in 2022. European Business Magazine. (2022). Retrieved December 8, 2022, from https://europeanbusinessmagazine.com/FinTech/ reasons-why-FinTech-is-important-in-2022/.

An Introduction to FinTech

65

Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Sinnott, N. (2022). Key opportunities to leverage in FinTech and beyond. Entrepreneur. Retrieved December 6, 2022, from https://www.entrepreneur.com/sciencetechnology/key-opportunities-to-leverage-in-FinTech-and-more/429429. Swallow, Y. C., Haksar, V., &Patnam, M. (2021). Stacking Up Financial Inclusion Gains in India. International Monetary Fund - Homepage. Retrieved December 9, 2022, from https://www.imf.org/external/pubs/ft/fandd/2021/07/india-stack-financial-accessand-digital-inclusion.htm. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ ijsdp.180307.

Chapter 4

Artificial Intelligence and Its Role in Financial Markets Kapil Kumar Aggarwal1, and Satakshi Agrawal2 1Associate

Professor, University School of Business, Chandigarh University, Punjab, India 2Research Scholar, University School of Business, Chandigarh University, Punjab, India

Abstract Artificial intelligence has become the new wave of opportunities for the financial and banking sectors in the market. The growing opportunities in these sectors have also led to the increased risk of cyber-attacks to, which these organisations need to be aware of. The introduction of AI has made the ecosystem of the financial sector very uncertain, due to which opportunities like mergers and acquisition has also emerged. The infusion of AI and AI-based technology has led to the operations being cost efficient along with the multiple benefits and providing being multiple various improved efficiency. The scale of operations and the handling of the huge data sets pose the risk of data breach, which must be taken off. The use of algorithms, defined data sets, and human decision making based on the data arises the risk of biases in the functioning. The risk can be dealt with through the infusion of workforce both in the functioning of the AI and the human decision-making to improve the vigilance and thereby reducing the risk of biasness.

Keywords: artificial intelligence (AI), financial markets, risk, banking, opportunities 

Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

68

Kapil Kumar Aggarwal and Satakshi Agrawal

Introduction Artificial intelligence (AI) is gaining importance in all the sectors and specially in the financial sector. The infusion of artificial intelligence in any field helps in improved efficiency and productivity hereby resulting in economic growth (Mokyr, 2018). The combination of technology including AI with the human workforce is changing the business scenario and is responding to the uncertainty and the risk prevailing in the financial sectors (Millar, Groth, & Mahon, 2018). Financial markets pertain to the market wherein the securities and bonds are traded by the people at a low cost. The financial sector in the current paper deals with financial markets containing stocks, bonds, derivatives, foreign exchange, and interest rates. The infusion of artificial intelligence (AI) in the financial markets helps in revolutionise strategies and help the need to work in a more open and collaborative way. Artificial intelligence helps to mimic human activities in performing the doings in the financial markets (Boden, 2018). The technology was first noticed by the public when Garry Kasparov grandmaster of chess, lost a match again IBM Deep Blue Chess Program (Schmelzer, 2019). The terms like deep learning (DL), artificial intelligence and machine learning (ML) are most used interchangeably by users but it is not the same in the real sense. In the initial times the AI were rule-based and they used to work based on the set rules, and there were rules programmed into it for every task; however these machines were not capable of learning. There was a certain improvement in AI in this direction. In the 20th century drastic improvement in the AI has replaced the complex rule-based application unless there is insufficient data available in the ML (Tricentis, 2019). Certainly, even a small amount of modification done in data can lead to the more accurate prediction of the data without even utilisthe AI or ML to the data (Herrmann, 2019). However, getting the prediction based on the modified data set will not be that good so in today’s world predicators want to have the big data set available based on which the decision could be taken leading to the ML. ML uses statistical tools learns from the data available and applies the various algorithms to reach the solutions of the existing problems (Oppermann, 2019). The popular algorithms include online shopping recommendations, internet search engines, email filters. ML gained its importance worldwide when the ML named IBM Watson beat the best player of the quiz in US in the year 2011.

Artificial Intelligence and Its Role in Financial Markets

69

In 2006, deep learning was introduced as the subpart of ML employing various other techniques like neural network. Examples for the neural network can include computer vision, machine translation, natural speech recognition and self-driving cars. With the passing time the improvements in AI, ML and DL are earning the great importance. Organisations working under this field desire more such experts to excel in their businesses (Davenport & Patil, 2012). In the following section we perform an extensive literature review to dive deeper into the topic more thenthan after we present the methodology of selecting and working on thanthe topic. Further, we critically analyse the results produced and provide the different opportunities emerging in the field of AI. Opportunities and uncertainness are then outlined and are presented across various sections. Thereafter, we head towards the conclusion of the study.

Literature Review The increasing trend of technology and AI in the various fields of businesses shows the power of AI to manage large datasets. The adoption of AI helps manage the data accurately and handle the huge data with much accuracy surpassing the abilities of the human being in the decision-making process (Brock & von Wangenheim, 2019; Casares, 2018). The advanced telecom system, high-tech computer technology has led to the smooth adoption of AI in the financial sector (Chui & Malhotra, 2018). Many firms are using the AI as the sword to benefit themselves and gaining the competitive advantage over the other firms. AI has improved the pace of the data processing which has limited the pace for the small firms. The adoption of the AI requires a huge investment as considerable money is required for the acquiring and the generating of the data (McWaters et al., 2018). The full potential of AI can only be realised when a considerable investment is done the process through which it is working. The adoption of the AI is the new trend in the financial sector, and this study provides insights relating to the opportunities and threats posed by its adoption. AI is gaining importance among the people as it is working in the direction of making the life of people simpler. The AI and ML has the power to mimic the human intelligence (Boden, 2018) and can make the decisions based on the big data based on algorithms. AI is working in the direction to bring the drastic change in the lives of the people and the way how the organisation and the society works (OECD, 2019).

70

Kapil Kumar Aggarwal and Satakshi Agrawal

The literature shows that specific changes are made in the market as the agents work by their clients, and there exists a personal touch between the agents and the clients. In contrast, the machine works based on big data and algorithms which show the same result for the same kinds of things. In some niche markets drastic changes occur when the customer starts appreciating and adopting these technologies (Christensen et al., 2015; Govindarajan et al., 2011; Hopp et al., 2018; Kovacs et al., 2019) for their working as it is available at the lower cost than the services provided by the human beings. To gain a competitive advantage in the market, the firms can either invest in these technologies and incorporate them in their daily work or cut off the competition by merging or acquiring the competitive firms. History has witnessed that in the banking sector whenever the technology is introduced in the market initially, the banks go for integrate amongst themselves through mergers and acquisitions. As the time passes and the technology becomes older and easier to afford then they opt for the technology in their day to day work. The adoption of technology in the working of any sector is not easy. It requires a lot of changes in terms of their data maintenance, and legal formalities. It is often seen that the human intervention is always required with technology to obtain better results in the decision-making (Dhar et al., 2017). Firms that are highly dynamic and are open to changes faces less problems in comparison to the other firms in the adoption of the technology (Ryll et al., 2020). The usage of AI is not restricted to a certain particular sector only. Rather AI is used in many different sectors (Casares, 2018; Dwivedi et al., 2021). In many instances AI is not used in isolation but is used in combination with other big technologies like cloud computing, big data analytics (Brock & von Wangenheim, 2019; da Silva, Luiz Kovaleski, & Negri Pagani, 2019). There has always been a dilemma that the AI is improving the existing business or it is creating the way for the new type of businesses. AI seems to reap a greater advantage in the economy in terms of speedy work and attracting early adopters in the market. They tend to capitalize the economies to scale and attract more customers resulting in the acquisition of more talent. The increasing inclination of the customers towards the AI-enabled services generates the need to the Fintech companies to adopt AI quickly in their working. The changing scenario of the financial markets and the infusion of technology would have an impact both on the quality and the quantity of the workforce (Thirgood & Johal, 2017). Increasing complexity and implementation of the technology would demand a skilled labour force which

Artificial Intelligence and Its Role in Financial Markets

71

may be expensive for the companies to hire but, on the other hand would also result in improved efficiency at the back office (MMC Ventures, 2019). Lowskilled jobs will be performed by machines, and people in those fields may become unemployed if they do not develop their skills. The researchers also predict that the discretion-based human jobs will also be impacted in the financial sector (Tokic, 2018). Some researchers are of the impression that no more jobs will be impacted; however, it will be replaced with more sophisticated and highly valued job (Dhar et al., 2017; Dicamillo, 2019). The implantation of AI in the financial sector is more challenging. It involves various complex algorithms, which have a very high risk associated with it and will also require changing the current working practices, organisations and society (Casares, 2018; Proudman, 2018). It is very difficult to identify and get away with the risk associated with the infusion of the technology, but it has to be done with utmost care as the financial sector is the backbone of any economy (FSB, 2017; Jung et al., 2019, Rowan et al., 2019). The reduction of the risk and the uncertainties requires a huge investment in technologies like cyber security, data privacy and consumer protection (KPMG, 2019). Systematic risk is also a major problem in the financial sector and AI can be used to interlink the different markets, which may result in reduced systematic risk. Policymakers are working hard to adopt AI in the financial sector and encourage the growth in the market (Bhatnagar et al., 2022a, 2022b, 2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al. 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d). In current literature, the studies and the predictions relating to the usage of AI in the past and the opportunities and risks attached in addition to that are highlighted. The paper deals with the risks and the uncertainties along with the way of options available in the market for the technologies are dealt with. In short, the paper highlights the ways to adopt AI in the financial markets and how it can lead to the improved efficiency of the firms in the financial market.

Research Methodology The research methodology for the current study tries to draw out both the theoretical and practical aspects of the emerging technology. The academia only gives a theoretical overview regarding the emerging technology and lacks the different business scenarios and the strategies adopted by the business houses by understanding the risk and the opportunities involved in the usage

72

Kapil Kumar Aggarwal and Satakshi Agrawal

of AI therein (WEF, 2019). Therefore, the study focuses on the usage of AI in the financial sector. The study is based on the review of the financial statements and the reports presented by the different financial houses, along with the reports by the media houses regarding the merger and the acquisition by them. To reduce the risk of the biasness by the media, the current study incorporates the opinions of the experts. After contacting these high-growth managers via LinkedIn a questionnaire was sent to them via emails and their views were gathered regarding the challenges and the improvement that they see after the implementation of AI in their work. An extensive literature review also provides an outline regarding the emerging trends and how the business houses have been working to adopt the AI in their working in the recent past.

Opportunities AI is an immensely growing field in the world. The combination of technology and the market-related factors is driving the growth of AI to the next level (PR Newswire US, 2019). Telephones and laptops emerged as the new technology, which was far superior and more convenient to use than the mainframe. The problem of a storage facility was dealt with by introducing the cloud computing concept. Technologies have always helped the markets to collect and analyse the moving information in the market and have a competitive advantage over their rivals. Data is now not only a piece of information but has also become an essential asset (The Economist, 2017). 90% of the data is claimed to have been created in the previous two years (Loechner, 2016). The speed of data creation is posing a challenge for itself. In the dynamic world, it is at most important to have the real time data. The delay in processing and analysing the data will lead to slow decision-making by the executive of the firm. The massive availability of the data provides more insights for investment in the financial market. Enabling AI usage in the international market has opened various opportunities for managers in the market. Many countries have already set a backup as AI plans for their strategic priorities (Madzou & Shukla, 2019). AI has been seen as the next wave for growth, so many countries are trying to have AI in their country for economic growth.

Artificial Intelligence and Its Role in Financial Markets

73

Banks and Financial Service Providers Changing and modernised forms of banking are mining the high level of data and understanding the context for the bankers. AI helps identify the consumers’ specific buying behaviour and then providesprovides insights for the offers to be presented to the different consumers as per their buying frequencies and habits. AI and ML can help in reducing fraud by detecting buying behaviour, geographical location, and buying frequencies and then automatically seeing unusual activities on the account. Infusion of AI and ML is also cost-effective and helps in detecting unusual activities in a speedy way. Financial service providers are working to build their services on the technological platform. The cloud storage setup is not only restricted to the big firms but also the tiny firms that can store their data within. It is expected that the small firms have more data than the big ones and include bilateral and multilateral transactions on their network as well. The big firms are trying to integrate the data of the small firms within them through mergers and acquisitions. The emerging trends of creating ecosystems, outsourcing, merger, and acquisition have created enterprises that are huge in size. Many companies, like Fiserv Inc, which provide services to banks and are known for providing of core banking services, entered the market by first acquiring of card payment service firms and then after several mergers and acquisition led it to becoming one of the biggest firms with the market capitalisation of $78 billion. To expand the business, the firms are concentrating on acquiring the small firms. The services provided to the bank by these firms ensure the survival of the banks. Banks are nowadays concentrate on merging and acquiring of new firms rather than focusing on innovation and introducing new services. This strategy helps in reducing the competition however, it slows down the pace of innovation, creating the disadvantage as against the big banks (Crosman, 2019). It takes many years for the mergers to integrate dissimilar functions of the firms. Other fintechs that focus on specialisation try to integrate novel and differentiated products and services into the market (Adams, 2019). Increasing complexity, along with the customer volume, demands the security of the customers.

74

Kapil Kumar Aggarwal and Satakshi Agrawal

.

The Financial Sectors The infusion of AI in the stock market has completely changed the working of the market. Earlier, the brokers used to deal in the stock market using the physical papers, and now it has all been replaced by the paperless process. The AI has taken the place of the humans dealing on the floor. Infusion of AI has not only sped up the process but has also improved the accuracy along with the recording of the real-time data. The manual investments by the stock analyst have now been replaced with the real-time, return matching investment by the AI guaranteeing more returns than the human intervention. As per the reports of BlackRock, a global investment trader, the benefits of AI and ML in managing of stocks have encouraged more and more people to come out of the active funds and opt for more passive funds and the volume of the fees collected by the management of the passive funds using robots is much higher than the active funds (Tokic, 2018). Many experts argue that the novelty of the ML is hard to maintain as it can be easily replicated by the fund managers who are managing these robots dealing in the market which will lead to the manipulation of the stocks in the market as a result the stocks which have the capabilities to out perform the market will not be able to do so. Therefore, the ML would again reinforce passive investments (Pozen & Ruane, 2019). In recent times, we see most of the funds are managed by the ML humans are just involved in managing the quarter part of the funds. ML uses algorithms to identify and match the facts per the requirement of the investments and the stocks available. The responsibility of analysing of the new variables explored by the ML still lies in the rational thinking of human beings (Pozen & Ruane, 2019). Many financial companies are trying to push the working and the algorithms of the ML and DL to such an extent that it can identify and present the stocks that can be bought and sold in the market. The understanding of systematic risk has become most important after the shock that the economies of all the countries suffered in 2008 in the form of Global financial crises (Acemoglu, Ozdaglar, & Tahbaz-Salehi, 2015; Anderson, Paddrik, & Wang, 2019; Chen, Iyengar, & Moallemi, 2013; Löffler & Raupach, 2018). ML and DL use network models, data mining and big data analysis to understand such risk (Gai & Kapadia, 2019; Gang, Xiangrui, Yi, Alsaadi, & Herrera-Viedma, 2019; Yun, Jeong, & Park, 2019). The gathering and analysis of vast and realtime data for humans are complicated and the maintenance of the accuracy of such data is also a big challenge, but technologies like ML and DL can perform this task in a much more speedy and accurate way as compared to the human beings.

Artificial Intelligence and Its Role in Financial Markets

75

It is well known that the movement in the stocks is also determined by the sentiments of the people regarding it in the market. ML can analyse the data collected and form a correlation between the data and the market sentiments by utilising social media tweets, reviews and news reports (Gang et al., 2019). The problem of the quality and the irregularity of the data can be resolved with the infusion of AI. The improved efficiency, accuracy, reduced data processing time and cost are the results of implementing AI in the stock markets. Even the charge that the customer has to pay to get their funds managed is much lesser for the passive funds as compared to the active funds as the machines do them. The charge for the active funds is around 20 times the charge for the passive funds (The Economist, 2019b). The insights from the literature and the experts suggest that more human jobs will be replaced by technology in the coming years.

Uncertainties and Risk Every coin has two sides; in the same manner, it is suggested that the infusion of technology and AI in the financial sector can be both beneficial and harmful for the working of the business. Some of the experts are of the impression that the excessive use of AI will result in widening control of the technology on the human working, which will in turn, create the problem of control over the human by the technology. Most experts consider that the era of superintelligence and is only three decades away (Makridakis, 2017; Turchin, 2019). There is always a danger of widespread inequality in the market that will be created by the firms making the first mover advantage in the market based on the big data available to them. These big firms can further dominate the market and push the small fintechs and the banks out of the market (Ashta & Biot-Paquerot, 2018). To explore and create new markets, bug firms tend to offer goods and services at a very minimum price and sometimes at free of cost. It is simpler for them to make such offerings, however, this creates their dominance in the market and ultimately leads to the monopoly of these firms. The danger of over or under-fitment of the technology may exist as AI works on specific algorithms that may not be suitable for the financial sector. Several hedge fund markets use AI for analysing and generating trade signals. Trade and financial matters are matters of research and are so kept separated from the algorithms. Asset managers are finding a way to optimise the process by the use of their internal research and tools.

76

Kapil Kumar Aggarwal and Satakshi Agrawal

The working of AI and ML is highly dependent upon big data, and any error in the data can result in misleading results. Capturing, mining and analysing the data is of the most importance while incorporating of the technology. With the increasing volume, it becomes critical to maintain and record data with the utmost care. Data scientists perform the work of collating and analysing, and making a conclusion on the basis of the data arrived to avoid biases from the decision-making (Pozen & Ruane, 2019). The machine learning from the past. So any disparity from the past condition will lead to the rejection of such application due to which the controlling head from the human is required (Frind, 2019). Delegating of the decision making by. the machine is an easier way, but in the case of disparity, looking at the algorithms and then correcting the decision is a very complex task. It is preferred that the first-line officers should not know about the algorithms on which the AI works as it may lead to data manipulation; however, due to the unavailability of such information with the first-line workers the collection of the data also remains incomplete. Due to the rigidity of the AI in using of the algorithms for the decision making, the complete reliability of the decision-making cannot be given to the technology. The decision-making can be delegated to the AI till some extent by the top management; however central portion of decisionmaking has to be dealt by the top executives only (Fox et al., 2019). Automation of the work has always been on the positive side of the efficiency of the work; however, it has also resulted in the horror experience among the people who have the possibility of losing their jobs as the result of technology infusion (Brynjolfsson & McAfee, 2014). Reports by McKinsey show that half of the workforce will either lose the jobs as a result of automation or will have to upgrade new skills (Manyika et al., 2017). It is suggested that the infusion of AI in decision-making will reduce the thinking role of the top management however, the decisions based on feelings and emotions would only be dominated by humans (Huang, Rust, & Maksimovic, 2019). Infusion of technology into fintech may render many people unemployed. Still, now it is a huge dilemma that these layoff employees would be taken over by which sector on the other side of the technology is not introduced in the sector, it will result in no development or the underdevelopment of that sector (Dicamillo, 2019). Data is concerned a two-edged sword. One side of it is that it helps in better and more concrete analysis; however, the concentration of such big data with certain firms can pose a threat of data privacy (Fesnak, 2019). Too much dependency on the technology makes the humans the slaves of the robots. The thinking of delegating of the decision-making to the robots has posed the threat

Artificial Intelligence and Its Role in Financial Markets

77

of thinking inertia among humans. The concentration of a huge amount of data at a particular place could improve the chances of a cyber-attack, and the danger of the breach increases with it being the weakest link for the attack (Vartanian, 2019).

Conclusion The analysis represents that the infusion of AI requires dealing with the big data that helps in cutting down the cost, improving efficiency and reducing risk. The enhanced efficiency and convenience in performing the task results in increased demand and investment, thus leading to economic growth. More profiles relating to big data analysis, customer management and customer engagement may take the place in the job market. The experts believe that cost-cuts, and improved efficiency have created major demand in the marketplace and have forced many companies to merge and acquire resulting in wider customer platforms. The improving technology in finance is a matter of concern for many small and traditional firms as the firms need to improve their understanding regarding the working of AI and the infusion of the technology can become cost-efficient only when these firms have a high customer volume. The AI runs on the algorithms programmed by humans and are at a very minimum level. The DL has the capability to learn by itself and assigns the weight to the data as per its experience. There are algorithms set in AI in the financial sector to perform various tasks like approving loans, issuing credit cards, revealing credit scores, and translating the language. The algorithms mentioned are a very narrow form of AI that is not as complicated as extracting the meaning and making the decision out of the data available. The further steps of AI should include the capabilities to learn from experience to survive in the dynamic business environment and the improvement in AI have been revolving around the minimum level problem for decades however, in 2010, DL with a multi-level neural network was introduced. With the increasing complexities and times, new work technology are being presented with novel techniques to complete the task. The use of the algorithms on the working of the AI raises the risk of inherent bias which needs to be reduced and can be done by including many non-technical staff at the various levels. Companies working on the innovation of new technologies like AI should focus on hiring people who are more creative and socialistic. The Unsupervised AI is the new way where the machine can learn from its experience and can act accordingly in the future.

78

Kapil Kumar Aggarwal and Satakshi Agrawal

The lack of testing of AI-enabled products is one of the major problems in its implementation in the real-time business. The ethics of the AI should be studied and researched along with its capabilities of performing the task. The students should be introduced to the courses about AI, and educational institutions should take the necessary steps to transform their curriculum to prepare their students for the budding opportunities and exile their careers in the fast-moving world. Students should be encouraged to prepare for their careers in AI; however, the biggest threat posed by AI and ML is that they may be used without any human intervention in the future, leading to more job cuts and increased unemployment.

References Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564–608. Adams, J. (2019). Fiserv, first data and the staggering fintech threat. American Banker, 184(12), 1. Anderson, H., Paddrik, M., & Wang, J. J. (2019). Bank networks and systemic risk: Evidence from the National Banking Acts†. American Economic Review, 109(9), 3125–3161. Ashta, A., & Biot-Paquerot, G. (2018). Fintech evolution: Strategic value management issues in a fast changing industry. Strategic Change: Briefings in Entrepreneurial Finance, 27(4), 301–312. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/10. 3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136 Boden, M. A. (2018). Artificial intelligence: A very short introduction. Oxford, UK: Oxford University Press. Brock, J. K. U., & von Wangenheim, F. (2019). Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review, 61(4), 110–134.

Artificial Intelligence and Its Role in Financial Markets

79

Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York and London: WW Norton & Company. Casares, A. P. (2018). The brain of the future and the viability of democratic governance: The role of artificial intelligence, cognitive machines, and viable systems. Futures, 103, 5–16. Chen, C., Iyengar, G., & Moallemi, C. C. (2013). An axiomatic approach to systemic risk. Management Science, 59(6), 1373–1388. Christensen, C. M., Raynor, M., & McDonald, R. (2015). What is disruptive innovation? Harvard Business Review, 93(12), 44–53. Chui, M. & Malhotra, S. (2018). AI adoption advances, but foundational barriers remain, McKinsey & Company. Available from https://www.mckinsey.com/featuredinsights/artificial-intelligence/ai-adoptionadvances-but-foundational-barriersremain#. Crosman, P. (2019). Fiserv-first data: Why small banks fear big fintech. American Banker, 184(12), 1. da Silva, V. L., Luiz Kovaleski, J., & Negri Pagani, R. (2019). Technology transfer and human Capital in the Industrial 4.0 scenario: A theoretical study. Future Studies Research Journal: Trends & Strategies, 11(1), 102–122. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi. org/10.18280/ijsdp.170802. Davenport, T., & Patil, D. J. (2012). Data scientist: The sexiest job of the 21st century. Harvard Business Review, 90(10), 70–76. Dhar, N., Holly, T., & Ryan, D., Galeaz, G. (2017). Top financial services issues of 2018, PWC Available from https://www.pwc.com/us/en/financial-services/researchinstitute/assets/pwc-fsi-topissues-2018.pdf. Fesnak, M. (2019). Book review of sims. Chistopher A. Tech anxiety: Artificial intelligence and ontological awakening in four science fiction novels, Jefferson, NC: McFarland & Company, Inc., 2013. Vii, 242 p. softcover. Journal of the Fantastic in the Arts 29(3). Fox, J., North, J. & Dean, J. (2019, September 17). AI in the boardroom: could robots soon be running companies? Corrs Chambers Westgarth. Retrieved from https://corrs.com.au/insights/ai-in-the-boardroomcould-robots-soon-be-runningcompanies. Frind, A. 2019, October 28. MIL-OSI banking: Who monitors the bots? Foreign Affairs. Available from https://foreignaffairs.co.nz/2019/10/28/mil-osi-banking-whomonitors-the-bots/. FSB. (2017). FSB considers financial stability implications of artificial intelligence and machine learning. Financial Stability Board Available from https://www.fsb.

80

Kapil Kumar Aggarwal and Satakshi Agrawal

org/2017/11/fsb-considers-financial-stabilityimplications-of-artificial-intelligenceand-machine-learning/. Gai, P., & Kapadia, S. (2019). Networks and systemic risk in the financial system. Oxford Review of Economic Policy, 35(4), 586–613. Gang, K. O. U., Xiangrui, C., Yi, P., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological & Economic Development of Economy, 25(5), 716–742. Govindarajan, V., Kopalle, P. K., &Danneels, E. (2011). The effects of mainstream and emerging customer orientations on radical and disruptive innovations. Journal of Product Innovation Management, s1, 121–132. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Herrmann, H. (2019). How to increase profits through predictive analytics when only few competitors’ bids are known. FIIB Business Review, 8(1), 61–76. Huang, M.-H., Rust, R., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65. Jangir, K., Sharma, V., Taneja, S., & Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Jung, C., Mueller, H., Pedemonte, S., Plances, S., & Thew, O. (2019). Machine learning in UK financial services, Bank of England & Financial Conduct Authority. Available from https://www.fca.org.uk/publication/research/research-note-on-machinelearning-in-uk-financialservices.pdf. KPMG. (2019). AI compliance in control. Available from https://advisory.kpmg.us/ content/dam/advisory/en/pdfs/2019/ai-compliance-in-control.pdf. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ ijsdp.180204. Löffler, G., & Raupach, P. (2018). Pitfalls in the use of systemic risk measures. Journal of Financial & Quantitative Analysis, 53(1), 269–298. Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. Manyika, J., Lund, S., Chui, M., Bughin, J., Woetzel, J., Batra, P., Ko. R. & Sanghvi, S. (2017). Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute. https://www.mckinsey.com/featured-insights/future-ofwork/jobs-lost-jobs-gained-what-thefuture-of-work-will-mean-for-jobs-skills-andwages#. McWaters, R., Blake, M., Galaski, R., Chubb, C., Uykur, D., Blickling, A., Muench, T. (2018). The new physics of financial services. World Economic Forum. Available

Artificial Intelligence and Its Role in Financial Markets

81

from http://www3.weforum.org/docs/WEF_New_Physics_of_Financial_Services. pdf. Millar, C. C. J. M., Groth, O., & Mahon, J. F. (2018). Management innovation in a VUCA world: Challenges and recommendations. California Management Review, 61(1), 5– 14. MMC Ventures. (2019). The state of AI: Divergence. Available from https://www. mmcventures.com/wp-content/uploads/2019/02/The-State-of-AI-2019Divergence.pdf. Mokyr, J. (2018). The past and the future of innovation: Some lessons from economic history. Explorations in Economic History, 69, 13–26. OECD. (2019). Recommendation of the Council on Artificial Intelligence. Available from https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449. Oppermann, A. (2019). Artificial intelligence vs machine learning vs deep learning. Deeplearning Academy. Retrieved from https://www.deeplearningacademy.com/p/ai-wiki-machine-learning-vs-deep-learning. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic. org/index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/en/ pub/yalovasosbil/issue/72655/1143214. Proudman, J. (2018). Cyborg supervision-the application of advanced analytics in prudential supervision, Bank of England Available from https://www. bankofengland.co.uk/-/media/boe/files/speech/2018/cyborg-supervision-speech-byjames-proudman.pdf?la=en&hash=6FFE5A1D19EAA76681DB615D9054C53 DB8823AB4. Rowan, P., Miller, M., Schizas, E., Zhang, B., Carvajal, A., Blandin, A., Garvey, K., Ziegler, T., Rau, R., Randall, D., Hu, A., Umer, Z., Cloud, K., Mammadova, L., Kim, J., & Yerolemou, N. (2019). Regulating alternative finance: Results from a global regulator survey. Cambridge Center for alternative Finance and World Bank Group. Available from https://www.jbs.cam.ac.uk/fileadmin/user_upload/research/ centres/alternative-finance/downloads/2019-10-ccafregulating-alternative-financereport.pdf. Ryll, L. R., Barton, M. E., & Zhang, B. Z., McWaters, J. (2020). Transforming paradigms: A Global AI in financial services survey. Cambridge Centre for Alternative Finance (CCAF), the University of Cambridge Judge Business School and World Ecnomic Forum (WEF). Available from http://www3.weforum.org/docs/ WEF_AI_in_Financial_Services_Survey.pdf. Schmelzer, R. (2019, December 26). Are we overly infatuated with deep learning?. Forbes: Available from https://www.forbes.com/sites/cognitiveworld/2019/ 12/26/are-we-overly-infatuated-with-deeplearning/#7394947e733d. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies:: A comparative study. Big

82

Kapil Kumar Aggarwal and Satakshi Agrawal

Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India’s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280 /ijsdp.180307. The Economist. (2017, May 6). The world’s most valuable resource is no longer oil, but data.: Retrieved from https://www.economist.com/leaders/2017/05/06/the-worldsmost-valuable-resource-is-nolonger-oil-but-data. The Economist. (2019b, October 5). March of the Machines. pp. 20–22. Thirgood, J., & Johal, S. (2017). digital disruption. Economic Development Journal, 16(2), 25–32. Tokic, D. (2018). BlackRock Robo-advisor 4.0: When artificial intelligence replaces human discretion. Strategic Change, 27(4), 285–290. Tricentis, B. (2019). AI approaches compared: Rule-based testing vs. learning. Retrieved from https://www.tricentis.com/artificial-intelligencesoftware-testing/ai-approachesrule-based-testing-vs-learning/. Turchin, A. (2019). Assessing the future plausibility of catastrophically dangerous AI. Futures, 107, 45–58. Yun, T. S., Jeong, D., & Park, S. (2019). “Too central to fail” systemic risk measure using PageRank algorithm. Journal of Economic Behavior & Organization, 162, 251–272.

Chapter 5

Research on Consumer Preferences and Consumer Satisfaction Levels in the Banking Sector: A Case Study in the Republic of Moldova Larisa Mistrean* Grigore Belostecinic and Liliana Staver Center for Behavioral Research, Academy of Economic Studies of Moldova, Chisinau, Moldova

Abstract The customer-oriented philosophy of modern banking and the implementation of the fundamental principles of continuous improvement in the day-to-day business of banking institutions justify the importance of assessing and analyzing consumer preferences and customer satisfaction. Customer preferences and satisfaction are now considered performance standards and possible standards of excellence for any financial-banking institution. In recent decades, the importance of customer satisfaction and customer preferences has increased, and determining the level of these indicators is considered the most reliable feedback, as it reflects customer expectations in an efficient, direct, meaningful and objective way. In order to apply the principle of customer orientation in day-to-day business, an increasing number of banking institutions choose customer satisfaction and preferences as key performance indicators. The information obtained by banks from 

Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

84

Larisa Mistrean, Grigore Belostecinic and Liliana Staver monitoring and evaluating their customers' satisfaction and preferences not only provides a measure of the success of their products and services but also provides information that helps to identify opportunities to improve products and services, processes and features that have been evaluated by customers and that can serve the interests of financial and banking institutions. Such improvements can strengthen customer confidence in the banking institution and can result in benefits for both the customer and the bank. Customer satisfaction and preference research involves identifying the factors, causes and reasons that influence the customer's decision to buy, as well as analyzing the behaviour of the target audience. To maintain and promote competitive advantage, modern banking institutions need to identify customer profiles to better understand their needs and expectations. This is imperative in the banking sector, where the variety of products and services offered (credits, deposits, credit cards, leasing, factoring, etc.) is particularly relevant to different categories of customers. Banks need to individualize products and approach each customer on a personalized basis, ensuring unlimited access - anytime, anywhere - to the necessary services. This approach is commonly referred to as "mass customizing" (Davids, 1987). Preferences and satisfaction are important indicators both from the point of view of the individual consumer, as they reflect a positive outcome in terms of spending their limited resources and fulfilling certain needs, and from the perspective of the banking institution, for whom these indicators drive repeat sales and consumer loyalty. Aim: The main purpose of this paper was to measure the satisfaction and some preferences of banking consumers in the Republic of Moldova as the main factors influencing their consumption behaviour. This study aimed to investigate the level of satisfaction and preferences of consumers regarding the way of interacting with banks in the Republic of Moldova by using a survey, identifying the determining attributes and causal factors influencing the satisfaction and preference of the consumer of banking services. Method: In order to study the topic addressed in this article, the following research methods were applied: analysis and synthesis of conceptual approaches to consumer satisfaction and the tools they use; deduction and induction in order to elucidate the influencing factors; survey; analysis of the results of sociological research on the level of satisfaction and consumer preferences to interact with banks in the Republic of Moldova, in order to formulate conclusions and recommendations. Findings: The present research confirms that customer satisfaction is an important point in forming a picture of the quality of banking services and that customer satisfaction influences consumer behaviour. The results also provide insights into how consumers prefer to interact with banking institutions. Originality of the study: The present study is an original work primarily because consumer satisfaction with banking products and services and preferences regarding the way they interact with the bank are investigated

Research on Consumer Preferences and Consumer Satisfaction …

85

by a third party, independent of both banking institutions and their customers. This independence of the researcher ensures the objectivity of the analysis, the results obtained, and the conclusions and recommendations made are thus free from bias. The paper is also original in that it analyses in depth, based on an original questionnaire, the level of consumer satisfaction according to several determining factors, thus resulting in a comprehensive, multi-faceted picture likely to provide a model for all those interested in developing a fair partnership between financial institutions in the Republic of Moldova and their consumers. The results of this research show which factors need to be taken into account in determining consumer satisfaction with banking services and propose solutions for increasing consumer satisfaction with financial banking services. Implications: This paper, by its purpose and results, contributes to a synthesis of the literature in the field of consumer satisfaction with financial-banking services and their preferences for interacting with the bank. The results obtained through the present research provide important support for managers of financial institutions in making managerial decisions. It is essential for bank managers to be aware that a high level of satisfaction among consumers of financial services also leads to a higher level of access to financial products and services, i.e., higher profits. It is also important to consider how consumers prefer to interact with banks to facilitate access to products/services.

Keywords: customer satisfaction, customer preferences, banking products/services, banking consumers, bank, financial institutions

Introduction Satisfaction is a crucial concern for both banks and customers, but it is also a subjective emotional concept and therefore difficult to quantify. Satisfaction depends on a multitude of factors and varies from person to person and from product to product. Satisfaction is fundamental to building and strengthening customer loyalty. When financial and banking institutions come to understand the criteria consumers consider when evaluating services and products, they will be able to identify effective ways of working to build long-term relationships with customers. Successful financial and banking institutions use customer needs and expectations as a starting point to develop their product and service offerings. One aspect that should be considered is the existence of a very wide range of options available to consumers in the financial-banking sector. Customers can

86

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

quite easily find similar products or services offered by various financial and banking institutions. As a result, in order to become eligible, each competitor on the market needs to find its own levers to differentiate itself from other competitors in order to attract more and more customers and keep existing ones. Prioritizing customer satisfaction is the path to success in today's modern management and a solid guarantee that in the future the customer will choose that brand every time. From a subjective perspective, banks aim to build a relationship with their customers that is much different from other institutions, in the sense of a more personal, closer approach, given that the customer entrusts his own finances to be kept in accounts opened with the financial institution. Consumers must be persuaded to trust their banks in the security offered by these institutions (that nobody will take advantage of them, and they will not be lied to or robbed). Financial and banking institutions must therefore maintain sight of the strong emotional involvement of their customers when they are in the situation of accessing a financial or banking service or product. In this context, we are talking about customers' needs, expectations, fears, fulfilments, beliefs, preferences and satisfactions, concepts that are extremely intimate and volatile psychological issues, but which financial and banking institutions are obliged to consider in order to ensure their own development (Bhatnagar et al., 2022a, 2022b, 2023a, 2023b; Mistrean et al., 2021a, 2021b; Mistrean, 2023a, 2023b, 2021a, 2021b, 2021c, 2021d, Dangwal et al., 2022a, 2022b; Jangir et al. 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d). Thus, managing consumer satisfaction is about managing products and services as well as managing consumer preferences, and assessing satisfaction and identifying how customers prefer to interact with the bank are elements of customer relationship management. As banks pursue the digitization of services to increase efficiency and improve customer experience, they need to know customer preferences for different channels of interaction with the bank, depending on their needs (Mistrean, 2021c, 2021d, 2021e, 2015; Cociug, 2015). Some customers adopt new technologies easily for their convenience. Others, however, cling to old ways of banking out of habit, simple resistance to change or simply ignorance. Young customers are looking for digital banking solutions that allow them to make relevant financial choices at the right time - without the need to present documents in person or, worse, having to contact bank staff (Oracle, 2021). Results from a banking survey show that young people want as little human contact as possible. They refrain from visiting or contacting the bank

Research on Consumer Preferences and Consumer Satisfaction …

87

for regular banking assistance, diametrically opposed when it comes to the biggest purchases in life that require a sizeable investment (a house or a car). The longer the credit is granted, the more value and importance it has from a personal point of view, and the higher the level of interaction, transparency and access required by the customer. Banks need to manage their channel interaction and create a nextgeneration distribution model to respond to changing customer behaviors (McKinsey&Company, 2020). In an increasingly digital world, bank customers have many options precisely because products and services now mean digital solutions. Investing in technology and launching online solutions is a priority for financial institutions precisely because it means increased efficiency (Mistrean, 2021a, 2021b, 2021c, 2020, 2016, 2014). The effect of the COVID-19 crisis has been felt both in the public and private sectors and at the societal level, changing consumer preferences and the way they relate to business as usual and to the future. Researchers (EY, 2020) have observed similar consumer patterns in different markets, including Romania, USA, UK, France and Germany. The changes produced appeared to be long-lasting, depending on restrictions and the evolution of COVID-19 (and its economic impact). Precisely because of this, banks had a difficult task: to better understand these new consumer behaviours and preferences and to meet their demands with relevant products and convenient services, i.e., to adapt their business models to the social changes caused by the COVID-19 crisis, as well as the war in Ukraine, the energy crisis and other conjunctural factors (Mistrean, 2021a, 2021b, 2023b, Mistrean et al., 2022, 2021a, 2021b).

Literature Review: Consumer Preference Research in the Banking Sector Understanding the factors that determine which options consumers choose and whether they make, rather than postpone, purchasing decisions is key to developing marketing strategies. A major contribution of behavioural decision research has been the establishment of the notion of constructed preferences, the idea that consumer preferences are not well-defined, but rather are constructed, formed gradually during the process of making a choice (Bettman, 1998). This constructive view suggests that different tasks and contexts highlight different aspects that influence consumer preferences, focusing them on certain considerations that lead to seemingly inconsistent decisions.

88

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Slovic (1995) explained that preferences could be seen as an individual's attitude towards a set of options that usually emerges in the explicit decisionmaking process. The expression of preference through choice and decisionmaking is the essence of intelligent and purposeful behaviour. In principle, identifying a person's preferences means having a set of alternatives for consumers to consider so that they can ultimately choose the most useful option. Novemsky (2007) start from the idea that consumer choices depend to a large extent on preference fluency, i.e., the subjective feeling that deciding is an easy or a difficult task. Difficulty in making a decision led to either postponing it or accepting a compromise. Yoon (2008) concludes that strong preferences reflect greater confidence and stability to changes in conditions and attributes. Individuals with strong preferences are thought to be less likely to change over time, although stimuli mainly influence their initial choice. Consumers clearly prefer to purchase the best service that can meet their needs and requirements at the moment. Preference is a highly individualized concept, it differs from person to person, each having their own vision and understanding differently the criteria, the indicators that characterize the bank's activity and services. Moreover, preference depends on the consumer's perception and how they have formed their opinion of products/services over time as a result of their purchase. Consumer preference is the process by which the consumer selects, analyses and interprets information to ensure a meaningful and coherent view of the product/service. Therefore, consumer preference for how to interact with the bank refers to the channel customers use to communicate about the required product/service. Consumer preference research enables banks to develop and implement effective marketing strategies to attract new customers and retain existing ones. How banks manage to cope in a crisis influences consumer loyalty. Over a third (36%) of consumers found a new provider during the COVID-19 crisis and will stay with that provider in the future, with digital services being the destination for many of these consumers. (Capgemini, 2020). Customers' preference for digital banking and their willingness to adopt innovative services is the basis of banks' contemporary business (Mistrean, 2021a, 2021b, 2023b, 2023a, 2717a, 2017b, 2017c; Jucov, 2022). Consumers' subjective preferences underlie the cognitive process of selecting the option with the highest anticipated value from a multitude of offers likely to satisfy immediate needs or wants. Preference is the key determinant shaping consumer behaviour during the search, selection, purchase and use of products and services (Mistrean et al., 2022, 2021a,

Research on Consumer Preferences and Consumer Satisfaction …

89

2021b; Mistrean, 2015, 2016). The expression of subjective preference through choice and decision-making appropriate to needs and available resources is the essence of intelligent and purposeful behavior.

Literature Review: Consumer Satisfaction Research in the Banking Sector The ISO 9001 Global Quality Management Standard defines satisfaction as the customer's perception of the extent to which his or her requirements for a product or service purchased from a particular institution have been met, from which it follows that satisfaction is the customer's subjective assessment of the extent to which his or her requirements and needs have been met in relation to the product or service in question and not in relation to the fulfilment of contractual obligations between the consumer and the financial institution. Bank customer satisfaction has been and remains a topic of increasing research interest due to the increasing competitiveness in this area and the difficulties in differentiating products and service offerings. Satisfying consumer needs and desires is an essential element of marketing financial banking services (Mistrean, 2021a, 2021b, 2023b, 2011, 2010, 2014). Consumer satisfaction is a key concept in services (Spreng, 1996), determines customer retention (Gustafsson, 2005) and customer loyalty (Bolton, 1999, Fornell, 1996), and is an important predictor of other behaviours that service providers may benefit from (e.g., human-to-human information sharing) (Boulding, 1993). Consumer satisfaction has been defined as a special form of consumer attitude, as a post-purchase phenomenon that reflects how much the consumer likes or dislikes the product or service after using it (Bearden, 1983, Churchill, 1982). Spreng (1996) states that satisfaction research focuses primarily on the confirmation of expectations, rather than the satisfaction of desires, as a key determinant of satisfaction. Kotler and Armstrong (2004) state that customer satisfaction is the extent to which a product's perceived performance lives up to a buyer's expectations. If product performance does not live up to expectations, the customer will be dissatisfied and satisfied if performance is exactly as expected. However, if the performance exceeds expectations, the customer will be satisfied and delighted, which will lead to repeat purchases and to telling others about the product or service and the experience they have had with the institution.

90

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

In the literature, some of the main concepts related to satisfaction include value, quality and desire (expectation). Joan L. Giese and Joseph A. Cote (2000) believe that all definitions of customer satisfaction have several common elements and identify three general components: • • •

Consumer satisfaction is a response (emotional or cognitive); The answer relates to a specific purpose (expectations, product, consumer experience, etc.); The response occurs at a certain point in time (after the purchase and consumption of the product or service, after the choice has been made or is based on experience, etc.).

Satisfaction is the positive state of a customer that is triggered by comparing the value and quality of a product or service with their desires, and expectations. Satisfaction depends on the gap between reality and desire, in relation to the level of quality of the products or services purchased, which may result in dissatisfaction, indifference, satisfaction or enthusiasm, leading to customer loyalty (Păunescu, 2006). Rust and Zahorik (1993) developed a mathematical model for assessing customer satisfaction and loyalty, which can predict market effects due to improvements in service quality. The model allows managers to determine which satisfaction drivers have the greatest impact on the customer and how much money should be spent to improve them in order to increase customer satisfaction. Rust (1993) demonstrated the applicability of their research in a pilot study of a retail banking market. Curry and Penman (2004) pointed out that competition for service quality differentiation is inevitable in the banking sector. They suggested that by offering good services, banks can retain their customers and thus reap longterm benefits. Thus, it is recommended that banks should keep their services at a high level by allocating sufficient resources to meet customer requirements. The results indicate that financial institutions need reasonable procedures to assess the overall satisfaction of their customers in a timely and accurate manner. In their study, the authors suggested that banks should develop and implement service quality improvement programs to increase customer satisfaction and loyalty. Understanding how consumers act can help shape future products and distribution preferences in a post-COVID-19 world (PwC, 2020). Banks are challenged to respond to enduring societal changes, including how consumers select products and distribution channels to meet individual financial needs resulting from the current crisis. Behavioural changes may accelerate the shift

Research on Consumer Preferences and Consumer Satisfaction …

91

in the concept of banking institutions from transactions to a more complex, high-value business. Decisions on distribution and product relevance are key to successful banking. Customers increasingly expect individualized offerings and banks need to use all the data at their disposal to manage their product and pricing strategy to fully meet their customers' expectations. Customer satisfaction can therefore be defined as the result of cognitive and affective evaluation, in which the actual performance of the purchased product is compared with the desired, expected standard. If the performance of the product is much lower than the expected standard, the customer will be dissatisfied; if the performance of the product is lower than the desired standard without, however, significantly affecting the customer's interest, the state will be one of indifference; when the qualities of the product are equal to the expected standard, the customer will be satisfied, and if they exceed expectations, the customer will be enthusiastic. The integration of experiences, digital and physical interactions has led to the emergence of a new concept, the customer journey, which better meets the needs of a wide range of financial and banking customers. The customer journey is the identification of the pathways, the channels through which the customer interacts with an institution. Of course, identifying the customer journey is not an easy task, as banking institutions need to collect, from multiple sources, data, and information about consumer interactions through different channels and points, but there are some tried and true strategies that can help financial institutions manage such a journey. Smart companies aim to delight the customer by initially promising less than they can deliver and then delivering more than they promised.

Consumer Preference Analysis Preference is the individual consumer's subjective assessment, measured by the satisfaction felt after purchasing and using certain products and services. This satisfaction is often related to the usefulness of the product in meeting personal needs. Choosing a useful product that proves to be effective in satisfying an immediate need triggers different emotional states such as happiness, satisfaction or fulfilment. The consumer makes decisions in order to obtain the maximum satisfaction that can be derived from the availability of his more or less limited income. The consumer seeks to maximize utility, subject to his own budgetary constraints. Still, the choice of a particular product may not be decisively

92

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

determined by the level of his income or the price of the product. Many consumers prefer to purchase a product or service even if it is more expensive than others (e.g., silver or gold bank cards are preferred by many consumers because, although they involve higher service charges, they offer many benefits to the user and their possession is a source of personal satisfaction). It is concluded that consumer satisfaction is directly proportional to the usefulness of the product or service purchased. The whole process of individualizing consumer preference results in optimal choice. Consumer preference allows the classification of different products, services or even financial institutions according to levels of satisfaction or utility. Utility does not relate to anything other than the personal satisfaction of consuming a product or service (such as the preference of some consumers for remote banking). The use of financial services (accumulating savings, financing expenses related to real needs) is an indicator that differs for each consumer, and preferences vary greatly under the impact of various factors such as consumer personality, level of financial education, trust in financial-banking institutions, communication with them, available information and, finally, the consumer's final perception of the whole interaction with the bank (Figure 1).

Figure 1. Factors determining consumer preference for financial-banking services.

Research on Consumer Preferences and Consumer Satisfaction …

93

We believe that financial education is a basic factor that provides the consumer with both the knowledge and skills necessary to understand financial products and services, concepts and risks, and the ability to make informed choices (Mistrean et al., 2021a, 2021b; Mistrean, 2023b, 2020). Moreover, raising the financial literacy level of the population increases confidence in the domestic financial banking sector, through the competent and informed choice of the financial institution or products offered by customers (not choosing the bank just because it is located near their home or office and understanding the risks, they take by purchasing different banking products and services). Figure 1 shows the different factors that affect consumer preference for financial banking services. Although financial banking products and services are characterized by certain peculiarities, customers' preference is mainly based on the degree of trust in the financial institution. A considerable proportion of consumers are unable to assess the usefulness and quality of the services provided by banks because they need more training to assimilate financial information and are unable to compare and analyze relatively similar offers (Mistrean, 2011). In particular, the consumer does not know with certainty how expensive it would be for him to take out a loan with a specific bank, as the costs are not always obvious (contracts provide various charges for failure to comply or late compliance with agreed clauses). On the other hand, there is always uncertainty about the usefulness and quality of financial products and services, and to overcome this uncertainty, the consumer needs to have a sense of trust in the financial banking institution, i.e., to believe in the professional skills of the employees and the management of the institution. This applies not only to credit products, but also to savings and payment products, and for each type of banking product and service, the consumer is influenced differently by the degree of trust (Mistrean, 2020, 2022). Modern banking is pursuing the adoption of digitization to improve efficiency and customer experience, fully in line with evolving customer preferences for different communication channels to meet their diverse needs and requirements. At the same time, some categories of customers are quick to adopt new technologies on the fly for their convenience, while others, due to habit or resistance to change, remain loyal to the old ways of doing business (preferring offline service in the territorial subdivisions of financial-banking institutions, face-to-face communication with bank employees, etc.). Today's customers are embracing enhanced digital channels of human interaction. A prime example of this evolving behaviour is the increasingly widespread use of remote counselling via telephone and/or the Internet when

94

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

supported by managers of consumer relations departments (Mistrean, 2021a, 2021b, 2021c, 2021d, 2022; Mistrean et al., 2022).

Consumer Satisfaction Analysis Customer satisfaction evaluation is important for any institution because it illustrates the extent to which customers like a particular product or service. Research results show that high satisfaction leads to better customer retention decisions, improved customer lifetime value and a stronger brand reputation. A low level of customer satisfaction reveals the critical points of the institution's business from the customer's perspective and can provide insight into ways to improve the product, service and overall customer experience. The needs and expectations of financial institution customers are extremely varied, ranging from product/service compliance and performance, communication and delivery channels, and post-delivery activities, to price and specific fees, security and trust, and legal liability. In today's competitive environment, more and more financial institutions are setting the following guidelines for their customer orientation: • • • •

Ensuring flexibility and speed of response to market opportunities in financial products and services; Understanding current and potential customer needs and expectations; Assessing customer satisfaction and ensuring customer loyalty, developing long-term relationships; Creating and improving partnerships with loyal customers.

Evaluating and monitoring consumer satisfaction is an essential management tool for financial institutions to ensure a successful business and is based on the analysis of customer relationship information. In order to succeed in improving customer satisfaction, financial institutions must first know what customers think about the products/services they offer and whether they are generally satisfied with their experience with the institution. This means regularly monitoring and measuring customer satisfaction. For this purpose, financial institutions use information collected directly from customers in the form of face-to-face interviews, questionnaires and surveys,

Research on Consumer Preferences and Consumer Satisfaction …

95

studies and reports from various sources, market research, complaints, as well as internal information on sales, customer retention, new customer trends, etc. Stimulating and monitoring customer feedback provides a valuable database that can create the framework for taking action to improve customer relations and increase customer satisfaction. Reflecting the state of the customer that emerges from comparing the quality of a service or financial product with his or her expectations, satisfaction depends on the gap between reality and the consumer's desire. It is assessed by the quality level and usefulness of the products or services purchased. The result of this comparison may lead to one of the following reactions from the consumer: • • •

• •

Dissatisfaction or total dissatisfaction, when the product or service does not meet expectations at all; Partial dissatisfaction, when a significant part of the characteristics of the product or service does not meet expectations; Neither satisfaction nor dissatisfaction, when the product or service, although not meeting expectations, still does not significantly affect the consumer's interests; Satisfaction, when the product or service fully meets expectations; enthusiasm, when the performance of the contracted product or service exceeds expectations, which ultimately leads to consumer loyalty.

Suppose customer expectations exceed the level of the product purchased. In that case, dissatisfaction will result, which sooner or later will result in the loss of customers and significant costs for the financial institution (Figure 2). An extremely topical concept that is also successfully used in the banking business is Customer Lifetime Value, abbreviated CLV. In this context, "customer lifetime" has a meaning strictly limited to the duration of the customer-bank relationship. In concrete terms, Customer Lifetime Value is a measure of the total revenue that the bank expects to earn from an individual for as long as that individual remains a customer of the bank. Customer Lifetime Value is a predictor of how profitable it can be for the institution to maintain a lasting relationship with a customer/consumer. It is understood that financial-banking institutions, in order to remain competitive, are forced to develop strategies to build positive and lasting relationships with their customers in several directions:

96

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Investments to Diversify and Enrich the Customer Experience A customer experience is a progressive concept, with continuous development over time, consisting of the totality of interactions between the customer and a financial institution (a brand), including visits to the institution's territorial branches, contact center support (including phone calls, email, interactive voice response systems - IVR, digital channels), purchases, product usage and even product promotion through advertising and social media. Improving the customer experience requires additional business management and marketing effort, and a customer experience management program needs to be initiated, including monitoring, listening and implementing changes for lasting improvement in the way customers interact with the institution, with the consequence of increasing their tendency to be loyal in the long term (Mistrean, 2014, 2015). The customer experience begins when a potential customer first interacts with the financial institution and can extend over time if the parties reach an agreement. For the experience to be positive, it is necessary for banks to show care for customers from the first interaction and continue after the purchase of products/services.

Figure 2. Factors determining consumer preference for financial-banking services.

Research on Consumer Preferences and Consumer Satisfaction …

97

The customer needs to have a positive experience, no matter which way they choose to interact with a bank. New and emerging technologies, such as artificial intelligence (AI), are already being implemented to improve customer engagement, automate processes and provide personalized information. On top of this, in order to respond promptly to queries and improve the customer experience, financial institutions are providing a live chat communication channel. To further enhance the customer experience, save time and serve a larger number of customers, and therefore to streamline communication, chatbot solutions are implemented to provide advisory services to obtain necessary information, check account status faster, identify an ATM or help users activate a new card, obtain account statements or respond to other types of requests, which evolve as customer needs grow. Customers expect to be able to access their preferred channels of communication with financial banking institutions (voice, chat, social media, SMS and web) and to receive the requested information in the shortest possible time (on-hold waiting and switching from one operator to another is less and less accepted). Trust, reputation and size of the institution have traditionally been considered important criteria for consumers in choosing institutions to help them manage their finances. Technological developments have altered the weight of traditional criteria, with customers increasingly weighing their options according to the quality of their previous experience with the offering institution. As a result, delivering an optimized customer experience is now as important as the traditional key criteria affecting consumer decisions.

Identification and Optimization of the Customer's Path in the Relationship with the Banking Institution - Customer Journey It is necessary for any financial institution to understand the mindset of its customers. While at one time banks considered it sufficient to respond to the wants and needs of their customers, recently new challenges such as new technologies, preferences and buying trends have emerged. With banking institutions and their customers facing unprecedented crises in recent history with devastating financial effects, banks are challenged to find lifesaving solutions that support both their own and their customers' interests. Banks must also find ways to attract new customers and retain old ones. In this respect, one of the action lines aims to identify the path that a customer travels in the relationship with the bank in order to purchase a product or service - Customer Journey - and to map this path - Customer

98

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Journey Map - in order to find solutions for optimization. The Customer Journey Map is nothing more than a graphical representation of a consumer's interaction with the institution. The Customer Journey in the financial banking sector starts when the customer becomes aware of the need and desire to purchase a banking product or service. During the initial meeting, the customer tries to figure out whether the institution, products and services fit their needs and desires, leading to a relationship where the parties know details about each other, and have information that will ensure a productive and successful long-term relationship. The changing consumer preferences and intensifying competitive environment, the variety of options available to consumers, force banks to identify each customer's journey within their company from start to finish to provide a positive experience and high level of satisfaction after purchasing products and services. Identifying and mapping the customer's journey is beneficial in the sense that several disruptions, such as too many steps a customer has to go through to resolve a problem, can be identified and removed. Designing and streamlining the customer journey helps to turn new customers into repeat buyers with strong brand loyalty. The biggest benefit of customer journey mapping is the opportunity to improve the customer experience and increase customer satisfaction. Customers can purchase the financial products and services they need from multiple financial institutions and fintech, which means that providing a positive customer experience could make the difference between winning and losing customers. Mapping Customer Journey - differs by product or service, but nevertheless, the process includes the following steps: • • • • •

Customization according to the customer; Aligning consumer objectives with their journey; Identification of customer contact points; Quantification of results; Correcting and changing negative aspects.

How consumers interact with a particular brand institution is not a linear process, but understanding the positives and negatives of engagement leads to improving the consumer experience and increasing the institution's revenue by gaining loyalty.

Research on Consumer Preferences and Consumer Satisfaction …

99

Facilitating Omnichannel Customer Experiences In order to provide customers with improved experiences, banking institutions have implemented omnichannel strategies in their commercial activity, aiming to integrate sales and communication channels. Using digitization programs, banks initially used multiple channels to provide consumers with information about their products, but this multichannel strategy needed to be revised over time. Subsequently, the omnichannel strategy was developed, which led to the interconnection of all sales and communication channels, making it much easier for customers to access comprehensive information. Building an omnichannel experience involves financial institutions simplifying the steps customers need to take on their journey to fulfil a wish (customer journey). Customers have a variety of options in choosing how to interact with their financial institution. Support channels need to reflect this by researching customer preferences, as well as obtaining customer feedback on self-service options and interactions with front office staff, in order to deliver a positive omnichannel customer experience. Prospective customers can initiate the opening of a new account using their laptop or mobile device but can complete the process either at a bank branch or by phone with a call centre representative. Delivering a superior, streamlined customer experience, and providing branded benefits to customers regardless of the touchpoint they select to interact with their financial institution, leads to higher conversions and greater customer loyalty. At the same time, social media is increasingly important not only for communicating with customers but also for providing them with information about the brand and public image of the institution. Suppose customers notice that the financial institution's responses on social media to a particular question or issue are not fast, thorough, or empathetic enough. In that case, this will affect the customer's opinion of the institution and its business. Therefore, banks need to consider social media - customer mentions and responses - in their customer experience strategy. Every customer should have a sense of satisfaction that leads them to use more products and services, becoming a long-term loyal customer of the institution. In fact, long-term loyal customers are the ones who provide the bank with revenue and help create and maintain a competitive advantage. Customer loyalty improves a bank's image and can be an excellent source of advertising - satisfied customers recommend the bank to other potential customers. Satisfied customers tend to establish trusting relationships with the financial institution, which will understand their requirements and identify

100

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

their changing needs more easily, primarily due to the unstable socioeconomic context. Knowing, understanding and responding appropriately to customers' needs will encourage them to remain loyal to the banking institution that has satisfied them and to refuse offers from competing institutions. This benefits both parties, as the customer is spared the effort and stress of building new relationships with unknown partners, and the bank, on the other hand, can streamline its promotion and delivery of newly offered products and services, as it is easier and less costly to increase the volume of transactions with known, loyal customers than to attract new customers separately for each product or service. Successful products and services usually meet customers' problems or needs. Solving customer needs is what makes the financial institution's product/service a necessity and because of this, it is purchased by the consumer. In the case of products or services that are "nice to have" (mortgage credit for a luxury house, credit for a luxury car or a trip) and "must-have" (mortgage credit for an apartment, credit for a car - a necessary means of transport or credit to pay for a child's education), the purchasing mechanisms are different. Necessity becomes desire, and utility gives way to the satisfaction of benefiting from a particular experience. In this respect, the marketing team of each financial-banking institution can use different mechanisms to discover what the customers' pressing needs are (Table 1). Table 1. Contemporary mechanisms to determine the real requirements (current problems) of customers Social network analysis Consulting competitors' reviews Google search analysis Applying online surveys Adding a live chat on the website Communicating with/to the target audience

It's an easy way to uncover public pain points by looking at shares and conversations on social networks and forums. The analysis of positive and negative reviews for similar products/services of other banks and financial institutions allows for accurate knowledge of shortcomings affecting the customer experience. It allows the discovery of areas of interest and needs of the target audience and the evolution of their behaviour. Helps to identify and classify the essential needs of the target audience. Provides current and potential customers with an easy way to get answers to questions they have during the selection and buying process. The institution can discover in real time what matters most to customers. Once the institution finds out the real needs of its customers and identifies a suitable product or service in its portfolio, it will use this information in all its marketing efforts.

Research on Consumer Preferences and Consumer Satisfaction …

101

Understanding customer behaviour helps the bank anticipate a customer's likely reaction and can influence the structure and characteristics of the offer. The existence of a loyalty program encourages repeat purchases of products and services by offering various benefits such as discounts or benefits in return. Although not a one-size-fits-all solution for customer retention, a loyalty program can yield excellent results when well-planned and executed. Customers expect their financial institution brand of choice to deliver a consistent, predictable, and memorable experience, regardless of when their interaction takes place and regardless of the channel that facilitates it, from conversations at the institution's branch offices, to online, to information via phone, email, etc. While emotional factors influence loyalty to a particular institution (a brand), it is rational factors that shape the customer-brand relationship (through promotions) and play an important role in the consumer's decision to start and end a relationship with a particular financial institution. In conclusion, managing customer satisfaction takes time and human and technological resources, but it is a necessity, and the benefits to the financial institution of knowing customer satisfaction and the factors that determine it are significantly more valuable than the initial investment.

Results Customer satisfaction is one of the important elements for the success of a financial banking institution, in other words, the success and development of the institution's activity depend to a large extent on customer satisfaction. Customer satisfaction not only measures the actual success of the products and services delivered by the financial banking institution but also provides information that helps to identify opportunities for improvement of products/services and procedures that will be subject to further evaluation by customers, thus bringing continuous benefits to the bank's business and customers alike. In order to determine the current level of satisfaction of consumers of financial banking products/services in the Republic of Moldova, we used the responses obtained from a survey conducted as part of the project "Behavioral developments of consumers of financial-banking services in the new economic configuration," carried out through online interaction between January and February 2022. The questionnaire includes questions touching on several areas of interest to the project, one of which is the assessment of

102

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

customer satisfaction, the subject of this article. Of interest in this regard are questions relating to the respondent's personal status and questions on the assessment of personal satisfaction with a few factors involved. After quantifying the number of respondents who participated in the survey, a sample of 363 consumers of financial banking products and services was obtained, all of them being customers of banks in the Republic of Moldova. To determine the status of respondents, the following indicators were considered: age, gender, place of living/residence, place of work, education, occupation (in order to identify the main source of income) and average monthly income as mentioned in Table 2. Table 2. Status of survey participants Age 64 years 0.8% Place of residence urban environment 83.2% rural environment 16.8% Average monthly income < 2000 lei 8.7% 2001 - 5000 lei 12.4% 5001 - 10000 lei 43.3% 10001 - 20000 lei 25.1% 20001 - 50000 lei 9.1% > 50000 lei 1.4% Gender men women

Studies secondary (medium) professional higher: bachelor's degree higher: master's degree doctoral The workplace urban environment rural environment Occupation (source of income) pupil/student full-time employee part-time employee freelancer pensioner have no contractual employment relationship we worked abroad

5.7% 7.7% 52.9% 29.8% 3.9% 92.3% 7.7% 13.4% 73.3% 5.0% 3.0% 0.8% 2.8% 1.7%

31.7% 68.3%

Analyzing the responses, the following informative/statistical data emerged (Table 2): •

•

In terms of age, 44.6% of respondents are aged up to 30 years; 36.6% - are aged 30-40 years; 11.0% - customers aged 41-50 years; 6.9% are aged 50-64 years, and 0.9% are customers over 64 years; In terms of gender of respondents, 68.3% are women and 31.7% - are men;

Research on Consumer Preferences and Consumer Satisfaction …

• • •

•

•

103

In terms of where respondents live, 83.2% live in urban areas and 16.8% of clients live in rural areas; In terms of place of work, 92.3% of respondents work in urban areas and only 7.7% work in rural areas; In terms of education, 5.7% of respondents have a general secondary education; 7.7% have a vocational education; 52.9% of the total number of respondents have a bachelor's degree; 29.8% have a master's degree and 3.9% are clients with a doctorate; Looking at the occupation of the respondents to identify the main source of income, we find that 13.4% are pupils or students; 73.3% are full-time employees; 5.0% are part-time employees; 3.0% of the respondents are self-employed; 2.8% have no contractual employment relationship, 1.7% are clients with income acquired abroad, and 0.8% are retired; The answers concerning the level of the average monthly income of the respondents lead to the following conclusions: 8.7% of the respondents have an average monthly income of less than 2000 lei; 12.4% have an average monthly income between 2001 and 5000 lei; 43.3% have an average monthly income between 5001 and 10000 lei; 25.1% - clients with an average monthly income between 10001 and 20000 lei; 9.1- have an average monthly income between 20001 and 50000 lei and 1.4% are clients of banks with an average monthly income exceeding 50000 lei.

Analyzing the results regarding the status of the participants in the survey, it can be concluded that, from a statistical point of view, the common profile of the consumer of financial-banking services is outlined by the following majority characteristics (with a preponderance exceeding 50%): a young person (up to 40 years old), with higher education, with residence and workplace in urban areas, full-time employee, with income between 5001 and 20 000 lei. The satisfaction of consumers of financial banking services was assessed against two general marketing indicators, namely the quality of the bank's products and services, and the quality of the materials and information provided by the bank, but also by reference to a number of specialized factors: quality of service, professional competence of bank staff; duration and complexity of procedures; quality of service; price of service; value for money; promotion of services; timely communication of information by the

104

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

bank; solutions offered in the event of problems; bank's response to complaints. For the self-assessment of satisfaction with the quality of the bank's products and services, and with the quality of the materials and information provided by the bank, respondents had to choose one of the five options: very satisfactory; satisfactory; partly satisfactory; unsatisfactory; and disappointing. Regarding the quality of products and services, the results are as follows: 30.22% of respondents said they were very satisfied with the quality of products and services offered, 54.94% felt satisfied, 13.19% were partially satisfied, 1.10% considered the quality of products and services unsatisfactory, and 0.55% of respondents were totally disappointed. Regarding the quality of materials and information provided by the bank, the state of satisfaction of the respondents is structured in the following categories: very satisfied was reported by 25.55% of respondents, satisfied was reported by 59.34%, partially satisfied was 12.09%, dissatisfied - 2.20%, and disappointed were 0.82%. These percentages reveal that, in general, the banks' activity generates a state of satisfaction among their customers, which can result in a certain relational stability and predictability for both parties involved in banking contracts. For a thorough and truthful analysis of the satisfaction of bank consumers, we have included in the questionnaire 9 factors against which the selfassessment should be made. For each one, a scale with five levels of assessment was proposed, from 1 to 5, each level meaning: 1 - total dissatisfaction, 2 - partial dissatisfaction, 3 - neither satisfaction nor dissatisfaction, 4 - satisfaction, 5 - maximum satisfaction, and enthusiasm. The distribution of the answers regarding the level of customer satisfaction with the quality of service and professional competence of the bank staff leads to the following conclusions: 44.4% of the customers were at the maximum level of satisfaction (5), 33.8% were satisfied (4), 13.8% were neither satisfied nor dissatisfied, 5.5% were partially dissatisfied and 2.5% of the respondents were totally dissatisfied with the quality of service and professional competence of the bank staff (Table 2). As can be seen, only 8% of customers felt dissatisfied. Interpreting this, we can conclude that financial banking institutions serve their customers at a high-quality level, having a very professionally competent staff.

Research on Consumer Preferences and Consumer Satisfaction …

105

Table 3. Reasons for satisfaction (dissatisfaction) of banking consumers (1- total dissatisfaction, 5 - maximum satisfaction), %

Quality of service, professional competence of the bank's staff The duration and complexity of the procedures The quality of the product/service The price of the service Value for money Promotion of services Timely communication of information by the bank Solutions provided in case of problems The bank's reaction to complaints

1 2.5

2 5.5

3 13.8

4 33.8

5 444

4.4 1.9 4.1 3.9 3.0 3.3

9.4 5.2 8.8 8.3 7.7 8.8

19.6 12.9 22.3 19.3 18.2 15.7

38.8 36.1 33.6 35.8 30.9 36.1

27.8 43.8 31.2 32.9 40.2 36.1

6.6 6.3

8.0 9.1

13.5 16.0

37.7 34.4

34.2 34.2

The second factor according to which we calculated the state of satisfaction of the consumer of financial banking services is the duration and complexity of banking procedures; 27.8% of respondents were maximally satisfied (enthusiastic) with the duration and complexity of procedures, 38.8% were satisfied, 19.6% had a neutral state, neither satisfied nor dissatisfied, 9.4% declared partial dissatisfaction, and 4.4% - total dissatisfaction. In other words, when 66.6% of the respondents felt a positive state of satisfaction and 19.6% were indifferent, it means that the banking procedures are not complex and have a reasonable duration, acceptable to customers. However, the percentage of those who were dissatisfied is quite high compared to other criteria, which means that banking procedures should be revised to consume less of consumers' time and be simplified (Table 3). The third criterion against which respondents' satisfaction was assessed was the quality of service provided by the bank. The responses reveal that 43.8% of customers were totally satisfied with the quality of service, 36.1% were satisfied (level 4), 12.9% were indifferent, 5.2% were partially dissatisfied and 1.9% were totally dissatisfied with the quality of service. In other words, service quality provides a high satisfaction rate among customers. The fourth factor quantifying satisfaction is the price of banking services and products, with 31.2% of customers being most satisfied and 33.6% satisfied. For 22.3% of respondents, the price brought them neither satisfaction nor dissatisfaction, which may be similar to the fact that price does not matter to them. The total number of satisfied customers was 12.9% (8.8% of them were partially dissatisfied and 4.1% were totally dissatisfied), which means that for them the price of the service is too high.

106

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Value for money is the fifth satisfaction indicator. 32.9% of the respondents are fully satisfied with this ratio, 35.8% are satisfied, 19.3% are neither satisfied nor dissatisfied, 8.3% are partially dissatisfied and 3.9% are totally dissatisfied. As in the case of price, the results lead to the conclusion that the quality-price ratio should be improved in favour of customers. The sixth criterion proposed for measuring satisfaction relates to how well banks manage to promote their services. The state of satisfaction triggered is also different in this case, with 40.2% of respondents being at level 5 (maximum satisfaction), 30.9% at level 4 (satisfaction), 18.2% at level 3 (neither satisfaction nor dissatisfaction). 7.7% of respondents reported level 2 (partial dissatisfaction) and 3.0% level 1 (total dissatisfaction). Positive satisfaction with the promotion of services is present in 71.1% of respondents. The percentage of neither satisfied nor dissatisfied should not be interpreted as a positive result, but rather as an effect of the fact that the promotion of services did not attract enough attention from these consumers. Negative satisfaction (dissatisfaction) is present in 10.7% of consumers. The results on the distribution of satisfaction levels among the sample of respondents who participated in the survey, regarding the timely communication of information by the bank, reveal the following percentages: for 36.1% of respondents the level of satisfaction is maximum (5), 36.1% are satisfied (level 4), 15.7% feel neither satisfied nor dissatisfied, 8.8% feel partially dissatisfied and 3.3% feel totally dissatisfied. It can be concluded that 72.2% of banking institutions manage to have customers who are satisfied with the speed of communication of information. The eighth criterion against which we tracked respondents' satisfaction relates to the solutions offered by the bank in case of problems. The results are as follows: 34.2% are at level 5 (maximum satisfaction), 37.7% at level 4 (satisfaction), 13.5% at level 3 (neither satisfaction nor dissatisfaction), 8.0% at level 2 (partial dissatisfaction), 6.6% at level 1 (total dissatisfaction). If we relate these percentages to the current socio-economic context, which is particularly problematic for customers of financial banking institutions, we can conclude that 71.9% of banks have succeeded in providing satisfactory solutions to the problems encountered during contractual relations with their customers. The bank's response to complaints is another factor in consumer satisfaction. The aggregation of responses in this respect revealed the following: 34.2% of respondents were fully satisfied with the bank's response to complaints, 34.4% were satisfied and 16.0% were neither satisfied nor dissatisfied. Partial dissatisfaction was felt by 9.1% of respondents and total

Research on Consumer Preferences and Consumer Satisfaction …

107

dissatisfaction by 6.3% of respondents. It should be noted that in the case of this indicator, the neutral state of customers can be interpreted positively, in the sense that they had no complaints to make. On the contrary, the rest of the customers (84%) had complaints about bank products and services, which should give the banking institutions food for thought.

Figure 3. Distribution of answers to the question: To what extent do you prefer to use the following service methods offered by the bank? (1- the least preferred method, 5 - the most preferred method), %.

Researching consumer preferences in banking is a particularly challenging scientific endeavour because many avenues of analysis can be identified. One of these is how customers prefer to interact with the bank so that the bank can tailor its services based on their preferences. In this respect, the answers to the question "To what extent do you prefer to use the following service modalities offered by the bank: - counter service; - remote service (internet banking, mobile banking, etc.)" are relevant, where the answer options were scaled from 1 to 5, 1 meaning the least preferred modality, 2 slightly preferred modality, 3 - medium preferred modality, 4 - predominantly preferred modality, and 5 - most preferred modality). Analysis of the responses allowed quantifying them in percentages that revealed the decisive preference of consumers for remote service in an overwhelming proportion, with levels 4 and 5 of appreciation comprising

108

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

20.4% (most preferred mode) and 70.5% (most preferred mode) of the responses (Figure 3). For 5% of respondents, remote service is medium preferred, for 2.2% it is slightly preferred and for a further 1.9% it is the least preferred mode. Interestingly for banking institutions it may be that of the total respondents for whom the remote service modality is the least and least preferred, 47% are respondents aged over 50, 20% are aged 30-40, 20% are young people up to 30, and 13% are aged 40-50. Counter service is the most preferred mode of interaction for 14% of respondents; for 15.2% it is the most preferred mode and for 20.4% it is the medium preferred mode. Physical interaction at the bank counter is preferred to a small extent by 17.6% of respondents and is the least preferred way by 32.8% of respondents. These results are perfectly in line with the age of the survey participants, with 81.2% of them under 40 years old.

Conclusion In carrying out this research, we identified the factors that have a major impact on bank customers' satisfaction levels, what makes them happy and what makes them unhappy about their overall experience with the bank. The management of financial institutions should include these factors in their view to ultimately determine customer loyalty with a view to developing long-term partnership relationships. The results suggest an optimistic picture regarding the level of satisfaction of consumers of financial-banking services in the Republic of Moldova and provide a solid framework regarding the aspects to be considered by the management of financial-banking institutions in developing actions to improve satisfaction, with a direct positive impact on future customer behaviour. Knowledge of the aspects analyzed in the paper plays an extremely important role in understanding the behaviour of their own consumers in the current market conditions and provides an important advantage to the bank that responsibly applies the techniques of attracting, relating and maintaining customers. The evaluation of customer satisfaction carried out regularly, as detailed and relevant as possible, should be a permanent medium-long term objective of any financial banking institution, achievable by regularly measuring the value of the "customer satisfaction" indicator, by including it among the

Research on Consumer Preferences and Consumer Satisfaction …

109

institution's basic indicators and by correlating its evolution with the evolution of other indicators (turnover, market segment, profit, profit rate, etc.). Consumer preference has a significant impact at the micro-economic level and leads financial institutions to form an opinion on customer behavior, and expand and diversify their product and service offerings, resulting in higher revenues. Taking into account consumer preferences regarding how to interact with the bank allows financial institutions to understand consumer choice or behaviour to achieve their strategic objectives. To win and keep customers, banks need to rethink both existing and new customer relationships to: •

•

• •

Greater customer engagement - interacting with consumers at the right time through their preferred communication channels, offering products and services that are requested according to their real needs; Understanding the customer's real needs, determined by motivations and financial goals, to adapt and customize the offer, improving the experience with a positive impact on future consumer behavior; Increasing the accessibility of banking products and services by simplifying banking, giving the customer a positive experience; Gaining full customer trust by organizing the business in a proactive way, offering strong digital and offline interactions.

In order to improve customer relations, it is necessary for the banking institution to know how customers feel about its products and services and how satisfied they are with their overall experience with the institution. This means that banks need to regularly monitor and measure customer satisfaction levels and preferences. The survey results show that the majority of respondents prefer remote service, but that a proportion of consumers prefer to go to the banks' branches and make direct contact with employees. Moreover, the survey was conducted online and therefore people who either do not know or do not use the internet did not participate. From this point of view, we consider the results to be irrelevant for this category of consumers as they are not included in the sample that participated in the survey. Given this context, banks should continue to keep their attention on this segment of consumers, who will continue to prefer counter service to a large extent in the future. Therefore, in order to facilitate access of this consumer segment to the banking products/services offered, the communication of information and advertising should continue to be carried out through traditional methods.

110

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Analyzing the results of the present research, we can conclude that, although the overall level of customer satisfaction is high, banks need to continue to focus on maintaining this satisfaction, even making efforts to increase the number of enthusiastic customers in all respects. Particular attention should be paid to consumers who stated that they were neither satisfied nor dissatisfied with the aspects proposed for analysis in the questionnaire administered, representing an average of 16.81% of all survey participants. This state of emotional neutrality of consumers is unlikely to be beneficial to a financial institution over time, as they can easily migrate to other options, perhaps out of pure curiosity, without necessarily being convinced that there are real benefits for them. As financial institutions aim to achieve ever higher performance targets, retaining customers already in the portfolio in order to attract them with new product and service offerings must be an ongoing objective of the business. Taking into account the responses of customers to the questions they were asked through the online survey; we make a number of recommendations to improve the relationship between financial institutions and consumers. In this regard, financial institutions should: • • • • • • • • •

streamline customer services; Increase the professionalism of employees; Simplify and shorten the duration of procedures involving customers; Develop and diversify the products and services offered; Improve its pricing policy, constantly keeping in mind the qualityprice ratio of the products offered; Promote products and services across all media channels to attract customer attention; Communicate the information requested by customers in a timely and clear manner; Provide a positive solution to the problems that customers encounter; Provide fair and reasonable solutions to customer complaints.

In order to increase consumer satisfaction with financial-banking services, we make the following recommendations: •

Segmentation of consumers according to different patterns of interest.

Research on Consumer Preferences and Consumer Satisfaction …

111

Financial institutions have a huge amount of consumer data, which needs to be used to tailor the offer to customers as closely as possible, based on certain basic commonalities specific to the group (not just demographic criteria such as age and gender). In an age where banks can see and know 90 per cent of the daily financial activities of the consumer of financial-banking products and services, it is time for these institutions to segment customers using archetypal customer personas based on their consumption behaviour. For example, one segment would be urbanites without children - which could include both young singles and couples, but who share common daily routines, visit similar places, use similar modes of transportation, and are susceptible to financial products in similar ways. In view of this, financial institutions need to rethink their business strategies, optimizing the quality of services and products offered to consumers and exceeding their expectations. Moreover, testing and learning across the financial institution network is becoming a necessity, given the rapid changes taking place in technological innovation and customer needs, retail design is more flexible than it once was. Banks should develop "laboratory" locations that allow them to learn quickly and adapt to these changes in real-time, with a modular design to allow for frequent and regulated changes; •

Developing and navigating the Customer Journey.

There is no business model that could be followed by the financial institution. The customer journey itinerary involves showing all the different touchpoints and interconnections as seen through the eyes of the consumer. Each customer will follow this path in a different way - depending on real needs, current issues, conjuncture and personal particularities. For example, in the case of a mortgage loan, the bank must accompany the customer every step of the way, starting with the expression of the need for the product in question, offering solutions for each problem at each stage: offer and options for the desired property, property valuation and insurance services, etc. The consumer does not have an end to purchasing a particular financial product but simply wants to solve a problem with this product or service (a bank loan provides a home, financial treatment, and studies). Going through the customer journey itinerary with empathy will help create a positive experience for the consumer and win a customer for life for the financial banking institution.

112

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

•

Improving elements of the customer experience.

This gives the financial institution the opportunity to gain market share in a particular segment or even to outperform its main competitors. Thus, financial institutions that aim to optimize customer experience aspects will certainly succeed in building consumer loyalty and improving their business strategy. Therefore, the segmented approach to customers, through the teams of managers that deal with customers, allows the identification of operational solutions for consumers, with a focus on increasing the quality of financial products and services and increasing customer satisfaction (considered partners of the bank) in terms of interaction with the financial institution. Regaining public confidence in the banking system requires a mutually beneficial partnership based on good faith and honesty between banks and their customers, which requires financial institutions to respect principles of financial conduct based on values such as professionalism, transparency and honesty, security, freedom of choice and understanding, and also requires both parties to emphasize integrity, responsibility, respect and reciprocity, both in terms of the benefits of the products and services purchased and in terms of the obligations arising from the terms of the collaboration agreement (be it a credit or deposit agreement). We believe that banks can influence customers' consumer behaviour, but they need to invest systematically and consistently in educating them, which could be called "Corporate Social Responsibility." After all, "banks get the customers they deserve," and this is best seen in times of crisis. Monitoring information on customers' perceptions of the financial institution's fulfilment of their requirements is an indicator of the effectiveness of the quality management system and the overall success of the institution.

Disclaimer None.

References Bearden WO, Teel JO. Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research (1983) XX : 21-28.

Research on Consumer Preferences and Consumer Satisfaction …

113

Bettman J, Luce M, Payne J. Constructive Consumer Choice Processes. Journal of Consumer Research (1998) 25 : 187–217. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/10.3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Bolton R, Lemon K. A Dynamic Model of Customers’ Usage of Services: Usage as an Antecedent and Consequence of Satisfaction. Journal of Marketing Research (1999) 36: 171-186. Boulding W, Kalra A, Staelin R, Zeithaml V.A Dynamic Process Model of Service Quality: From Expectations to Behavioral Intentions. Journal of Marketing Research (1993)30(1): 7-27. Capgemini research institute (2020). COVID-19 and the financial services consumer: Supporting customers and driving engagement through the pandemic and beyond. Research note2020. Churchill G, Surprenant C. An Investigation into the Determinants of Customer Satisfaction. Journal of Marketing Research (1982)19: 491-504. Cociug, V., Mistrean L. Vulnerabilities of banking system in the Republic of Moldova. In: Journal of Euro & Competitiveness. (2015), 1(5) /2015, 13-24. Curry A, Penman S. The relative importance of technology in enhancing customer relationship in banking - a Scottish perspective. Managing Service Quality (2004) 14(4): 331-341. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802. Davids S. Future perfect. New York: Addison-Wesley, (1987), 243 p. ISBN: 0-201-115131. EY. Romanian consumer behavior in the context of COVID-19.2020 [online] [cited 09 November 2022]. Available: file:///C:/Users/User/Downloads/romanian-consumerbehaviour-in-context-covid19-v2%20(2).pdf.(EY, 2020).

114

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

Fornell C, Johnson M, Anderson E, Cha J, Bryant B. The American Customer Satisfaction Index: Nature, Purpose, and Findings. Journal of Marketing (1996)60(4): 7-18. Giese L, Cote J. Defining Consumer Satisfaction. Academy of Marketing Science Review (2000) 1: 1-27. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)? Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Gustafsson A, Johnson M, Roos I. The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention. Journal of Marketing (2005) 69: 210-218. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Jucov, A., Staver, L., Mistrean, L. Consumer behavior in the new economic reality. Approaches to personalized medicine: from paradigms to challenges. Managing Risk and Decision Making in Times of Economic Distress, (2022) Part B, Volume 108B. Kotler Ph, Armstrong G. Principles of marketing. Bucharest: EdituraTeora SRL, (2004), 1064 p. ISBN 1-59496-026-7. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. McKinsey&Company. Customer preferences pur retail banking channel evolution.(2020)[online] [cited 09 November 2022]. Available: https://www.mckinsey.com/industries/financial-services/our-insights/bankingmatters/customer-preferences-spur-retail-banking-channel-evolution. Mistrean L, Staver L. The impact of the COVID-19 pandemic crisis on demand for financial-banking services. Chisinau: ASEM, International Scientific Conference (2022) 3: 415-427. Mistrean L, Staver L. Financial literacy and consumer behavior of financial-banking services. Шестая международная научная конференция: Междисциплинарность в современном социально-гуманитарном знании-2021. Социальные технологии, коллективные ценности и экономические институты в цифровом мире. Ростов-на-Дону: Южный федеральный университе, (2021a) 82-96. Mistrean L. Behavioral evolution of consumers of banking services in the COVID-19 pandemic situation. Insurance and Risk Management for Disruptions in Social, Economic and Environmental Systems: Decision and Control Allocations within New Domains of Risk. (2021a) 67-80. Mistrean L. The impact of COVID-19 on consumer behavior of financial-banking services. Chisinau: ASEM, Proceedings of the International Scientific Conference “Development through Research and Innovation - 2021”, IInd Edition, (2021b) 140146.

Research on Consumer Preferences and Consumer Satisfaction …

115

Mistrean L. Trust as a determining factor regarding the behavior of consumers of financialbanking services. Competitiveness and innovation in the knowledge economy. Chişinău: ASEM, (2020) 613-620. Mistrean L. Factors Influencing Customer Loyalty in the Retail Banking Sector: A Study of Financial-Banking Services in the Republic of Moldova. Opportunities and Challenges in Sustainability, (2023a) 2(2), 81-9. Mistrean L. Contemporary directions on how consumers interact with financial banking services. The USV Annals of Economics and Public Administration, (2023b) Vol 23, No 1(37). Mistrean L. Banking customer relationship management under the impact of new information technologies. Современный менеджмент: проблемы и перспективы. Санкт-Петербург: Государственный Экономический Университет, (2021c) 483-490. Mistrean L. Contemporary directions in banking product quality assurance. Studia University (2011) 2 (42): 132-134. Mistrean L. Contemporary marketing strategy to strengthen the bank-customer relationship. The role of investment in the development of the domestic economy in the context of European integration (2010) 180-185. Mistrean L. Customer orientation as a basic principle in the contemporary activity of the bank. Journal of Public Administration, Finance and Law (2021d) 21/2021:39-51. Mistrean L. Private banking development perspectives. Monetary, banking and financial issues in Central and Eastern EU member countries: how can Central and Eastern EU members overcome the current economic crises? (2014)1: 133-140. Mistrean L. Using customer relationship management to increase customer loyalty in banks. Euro and the European banking system: evolutions and challenges (2015) 221232. Mistrean L. Suitability for use open market operations as a main tool of monetary control by National Bank of Moldova with the purpose of development of the national economy. Eastern European Jurnal of regional Studies. (2016), vol. 2, 150-161. Mistrean L. Development of deposits guarantee in the banking system of Moldova. Annals of the University of Craiova, (2017a), V I, nr. 45, 180-190. Mistrean, L. Influence of the financial crisis on the credit policy of banks from the Republic of Moldova. Competitivitatea şi Inovarea în Economia Cunoaşterii, (2017b), Vol. III, 207-214. Mistrean, L. Impact of system risk on bank stability. In: Globalization and Higher Education in Economics and Business Administration (GEBA 2017), (2017c), 110. Mistrean, L., Staver, L., Jucov, A. Developing relational marketing in banks to increase consumer’s loyalty in banking services. The International Scientific Conference “Development of the Bulgarian and European economics – challenges and opportunities”, Collective Scientific Book of Faculty of Economics, (2021b) Vol.5, 8796. Mistrean, L. Improvement directions of the monetary policy of National Bank of Moldova with the purpose of development of the national economy. În: Research of Financial Economic and Social Studies. Turkey: Ferudun KAYA, (2021e), nr. 1, Vol. 6., 67-78.

116

Larisa Mistrean, Grigore Belostecinic and Liliana Staver

NCR. Your customers' preferences were bound to change, are you ready for them? (2022)[online] [cited 09 November 2022]. Novemsky N, Dhar R, SchwarzN, Simonson I. Preference Fluency in choice. Journal of Marketing Research (2007) 44: 347–356. ORACLE. Global Retail Banking Consumer Trends. The Changing Shape of Consumer Banking Relationships. (2021) Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA/NPL in Emerging and Advanced Economies: A Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. Păunescu C. A model of assessment for customers’ satisfaction in the Small and Medium Entreprises. The Amfiteatru Economic journal. (2006) 8(20): 112-117. PwC.How retail banks can keep the lights on during the COVID-19 crisis - and recalibrate for the future. [online] [cited 09 November 2022]. Rust R., Zahorik A. Customer Satisfaction, Customer retention and Market Share. Journal of Retailing (1993) 69: 193-215. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. Slovic, P. The construction of preference. American Psychologist (1995) 50 (5): 364-371. Spreng R., MacKenzie S, Olshavsky R. A Reexamination of the Determinants of Consumer Satisfaction. Journal of Marketing (1996) 60: 15-32. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank's green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India’s Total Natural Resource Rents (NRR) and GDP: An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. Yoon S., Simonson I. Choice set configuration as a determinant of preference attribution and strength. Journal of Consumer Research (2008)35(2): 324–336.

Chapter 6

FinTech Tools Used in Finance Mohini*, MCom Seema Bamel, MCom and Shveta Singh, PhD Haryana School of Business, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, India

Abstract FinTech is attracting the attention of regulators as it rapidly becomes a worldwide phenomenon. The term “FinTech” is an umbrella word that includes new kinds of financial services made possible by advances in computer technology and the associated business strategies. As a result of technological advancements, the economy’s financial services industry has seen a progressive shift in how it provides its products and services to customers. Digital finance comprises an abundance of new financial goods and services. FinTech companies and other innovative providers of financial services are redefining the financial industry with their innovative business models, cutting-edge financial software, and novel ways of communicating and interacting with customers. In the past five years, Bitcoin and Blockchain technology have been considered key areas of FinTech in the financial market. Numerous kinds of technology are growing throughout time and are being adopted and integrated into company operations to maximize added value and gain a competitive edge.

Keywords: FinTech, finance, digital finance and FinTech tools

*

Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

118

Mohini, Seema Bamel and Shveta Singh

Introduction The current state of the financial market is fraught with challenges due to Globalization, the emergence of digitalisation, and the advent of new technologies. To maintain a competitive edge in today’s dynamic financial market, the finance industry needs to respond quickly and effectively. FinTech, or financial technology, is a “heated” issue in finance, where big data is being used. The term “FinTech,” which combines the words “finance” and “technology,” has emerged as a new economic phenomenon in the twenty-first century because of the worldwide economic collapse of the 2008 Global Financial Crisis. It is a term used to describe innovative technologies that attempt to modernise and enhance the delivery of financial services. FinTechs are “startups and other companies that use technology to perform the fundamental functions of financial services, influencing how consumers store, save, borrow, invest, move, pay, and protect their money” (McKinsey, 2016). There has been a prolonged development phase of FinTech. In the 1950s, the first credit cards were launched; in the 1960s, automated teller machines were commonplace; and electronic stock trading became widespread in the 1970s and 1980s. Afterwards, e-commerce, Internet banking, and other online payment systems emerged due to the Internet revolution of the 2000s. The following decade saw the development of intelligent technologies, Bitcoin, and Artificial Intelligence (AI) to provide personalized banking services. FinTech companies have made significant contributions to the modern banking and financial industry through a variety of avenues including lowering costs, enhancing customer service, and expanding access to banking services (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al., 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d).

Literature Review The word “FinTech,” an abbreviation of “Financial Technology,” first became in widespread use around 2014. It refers to the proliferation of technological tools, platforms, and ecosystems that increase the accessibility, effectiveness, and affordability of financial services and products. Now that interest has been growing, several studies have been done on the topic.

FinTech Tools Used in Finance

119

Parminder-Varma et al., (2022) performed a systematic thematic review of recent research on FinTech’s influence on recent changes and upcoming challenges in the Banking industry, with a focus on Blockchain Technology and found that FinTech has tremendous growth potential to impact the Banking industry and the world. Chueca Vergara & Ferruz Agudo (2021) reviewed the available literature to investigate the connection between sustainable finance and FinTech. The study found that the usage of green finance can make FinTech companies and the financial industry as a whole more sustainable. Furthermore, this study emphasises the significance of legislation on a global scale and throughout Europe, primarily from the perspective of protecting the rights of consumers. Daqar et al., (2021) investigated how the customers’ opinions and usage of FinTech before and after the COVID-19 outbreak helped forecast the spread of the disease. The study revealed that greater familiarity and the usage of FinTech services and products among users will slow the proliferation of the COVID-19 virus by reducing face-to-face transactions as a result of which consumers satisfy their financial requirements by employing digital payment methods and tools, particularly ways of payment that do not require contact. Boot et al., (2020) examined the implications of technology development on financial intermediation and differentiating between information and communication advancement. The study revealed that development of new communication channels may result in the horizontal and vertical fragmentation of the traditional banking model. Kou (2019) introduced the unique issues related to FinTech by using the Web of Science database. Gai et al., (2018) aimed to compile and analyse recent accomplishments in the field of financial technology to present a theoretical framework for FinTech. The five technological facets summarised and engaged are data methodologies, data security and privacy, application and management, service models, and hardware and software. The main findings of this work are the basic foundation for developing active FinTech solutions. Guild (2017) emphasised the widespread usage of Banking Technology to promote financial inclusion. He points out the role of the regulatory environment in promoting financial inclusion through technological advancement. Thus, by formulating the appropriate policies, the regulatory bodies promote the country’s sustainable economic development. Gomber et al., (2017) examined the recent developments in the field of Digital finance study. The study revealed that Digital Insurance, Crowd

120

Mohini, Seema Bamel and Shveta Singh

investing, Robo-advice and Digital money are the less explored areas of research regarding FinTech.

Tools of FinTech Used in Finance There are various innovative FinTech tools that are used in the finance industry. The details of which are given below: Cloud Banking “Cloud banking” refers to the instantaneous provisioning of hosted computing services (such as data analytics, data storage, servers, applications, and communication and networking) to the financial industry such as banks, credit unions, FinTechs, and others via the internet. Financial institutions can improve nearly every facet of their operations by utilizing cloud banking to centralize the management of their banking systems and primary applications in the cloud, and they can provide prospective and current customers with a superior digital banking experience and first-rate financial products and services. API programming is a framework for the standards and protocols which are used to construct and integrate software applications. This has the potential to simplify app production, which will ultimately save time and money. It’s a vital asset for businesses since it helps them enhance their service offerings, create new digital revenue channels, and boost customer engagement. In this way, it gives you flexibility, simplifies design, administration, and use, and provides opportunities for innovation. Robotic Processing software A software technology that is designed to perform for mid able tasks and which makes organizations more profitable, flexible, and responsive. As a result, the workload of employees is reduced and they can focus on more productive work like innovating, collaborating, creating and interacting with customers. It is ideal for numerous banking applications. Artificial Intelligence (AI) This term is used to describe the collection of technologies that enable tasks like computer vision, machine learning, speech recognition, natural language creation, and robotics. It can bring the power of data analytics to fight against fraudulent transactions and enhance compliance as well as allow businesses to overcome traditional obstacles linked to customer service. Blockchain Blockchain is a novel technological advancement that works through a platform that enables multiple applications in economic

FinTech Tools Used in Finance

121

transactions. Blockchain, in its simplest definition, is a decentralized, digital ledger of all shared data. The blockchain itself serves as the ledger in which all transactions are recorded. Thus, using Blockchain technology has the potential to save trillions of dollars in banking costs by dramatically lowering the need for such fees. In this way, Blockchain enables a firm to simplify operational activities, automatic compliance regarding data reporting, and accelerate the settlement of funds between financial institutions. Quantum computing is basically a process that uses the law of quantum mechanics to solve problems too large or complex for traditional computers which results in speeding up solving the complex calculations. When seen in this light, it paves the door for new prospects for the banks in terms of risk assessment and trading. Virtual reality (VR) and Augmented Reality (AR) Augmented reality (AR) is a technology that adds value to the real environment by laying computergenerated information on top of it whereas a technology known as virtual reality creates experiences that are genuine and credible even if they take place in a computer. Virtual reality is being used by a number of financial institutions to improve the overall consumer experience. Customers and employees can benefit from richer visualizations of data and services through projection in both augmented and virtual reality environments. Open Banking It’s the standard practice in the banking industry to allow authorised third-party payment services and other financial service providers access to transactional and other data held by financial institutions. This is done in order to facilitate secure interoperability among the various banking institutions. Application programming interfaces allow for data to be accessed by external parties. It facilitates safer and more convenient financial transactions worldwide and provides consumers with more options for handling their money through intermediaries.

Conclusion The traditional financial services industry has seen a dramatic transformation as a result of innovations and technological advancements. After the Great Financial Crisis, the “new” financial technology sector began to gain traction in its modern incarnation. This was a consequence of the fact that individuals working in the field of financial technology came to the realisation that banking services ought to be economical, facilitative, and transparent. FinTech is typically referred to as an industry that employs technology to boost the

122

Mohini, Seema Bamel and Shveta Singh

efficiency of banking operations and the delivery of financial services. It is “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, 2019). Thus, FinTech has helped modern banking and finance industries by optimizing costs, improving customer service, and promoting financial inclusion. India is among the fastest-growing countries in the world when it comes to FinTech industry, following the footsteps of the US and the UK (EY, 2021). The market size of the Indian FinTech industry is expected to reach approximately $150 Billion by 2025, having reached $50 Bn in 2021. This places India in an exceptional position to spearhead a renewed spirit of innovation in FinTech. In addition, In the year 2020, India had the highest rate of adoption of financial technologies, at 87 percent. Furthermore, according to an EY study, the domestic FinTech sector is projected to reach a goal of $1 trillion in AUM and $200 billion in revenue by 2030, with Payments, Lending, and InsurTech as the most preferred sectors (2021). In addition to the COVID19 lockdowns, which have been an unanticipated but crucial impetus for digital adoption in financial services, a number of additional factors continue to contribute to the rise of FinTech. The government’s drive for a digital economy is one of these factors. Additionally, the rise and expansion of FinTech is supported by the growing need for accessible financial services, customer expectations, and the intensely competitive financial services market.

Disclaimer None.

References Abu Daqar, M., Constantinovits, M., Arqawi, S., & Daragmeh, A. (2021). The role of FinTech in predicting the spread of COVID-19. Banks and Bank Systems, 16(1), 1-16. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India:

FinTech Tools Used in Finance

123

Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/10. 3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Boot, A., Hoffmann, P., Laeven, L., & Ratnovski, L. (2021). FinTech: what’s old, what’s new?. Journal of financial stability, 53, 100836. Chueca Vergara, C., & Ferruz Agudo, L. (2021). FinTech and sustainability: do they affect each other?. Sustainability, 13(13), 7012. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/10. 18280/ijsdp.170802. Gai, K., Qiu, M., & Sun, X. (2018). A survey on FinTech. Journal of Network and Computer Applications, 103, 262-273. 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. Guild, J. (2017). FinTech and the Future of Finance. Asian Journal of Public Affairs, 10(1),17-20. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Kou, G. (2019). Introduction to the special issue on FinTech. Financial Innovation, 5(1), 45. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ijsdp.180204. Mckinsey and Company, (2016), “FinTechnicolor – The New Picture in Finance.” Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic. org/index.php/jebi/article/view/41.

124

Mohini, Seema Bamel and Shveta Singh

Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/en/ pub/yalovasosbil/issue/72655/1143214. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies:: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. doi: 10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/10. 18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307. Varma, P., Nijjer, S., Sood, K., Grima, S., & Rupeika-Apoga, R. (2022). Thematic Analysis of Financial Technology (FinTech) Influence on the Banking Industry. Risks. 10(10), 186.

Chapter 7

Unlocking the Predictive Power of ARMA Models on Algoquant Fintech’s Daily Returns Monika Khanna1, Mukul2,† and Pawan Kumar2,‡ 1DES-MDRC,

Panjab University, Chandigarh, India School of Business-Commerce, Chandigarh University, Mohali (Punjab), India 2University

Abstract Introduction: In recent years, financial technology companies (fintechs) have utilising increasingly statistical models to forecast asset returns. One such model that has gained popularity in the area of economic time series is the Autoregressive Moving Average (ARMA) model. In this book chapter, we explore the predictive power of ARMA models on Algoquant Fintech’s daily returns. We begin by collecting daily return data of Algoquant Fintech’s stock from the past year. We then fit an ARMA model to the time series data and evaluate its forecasting accuracy by applying different statistical metrics like mean squared error (MSE) and root mean squared error (RMSE). Additionally, we perform a comparative analysis of ARMA models with other commonly used time series models, like autoregressive integrated moving averages (ARIMA) along with exponential smoothing (ES). Our findings indicate that ARMA models can accurately predict Algoquant Fintech’s daily returns. 

Corresponding Author’s Email: [email protected]. Corresponding Author’s Email: [email protected]. ‡ Corresponding Author’s Email: [email protected]. †

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

126

Monika Khanna, Mukul and Pawan Kumar We observe that ARMA models outperform ARIMA and ES models regarding forecasting accuracy for the given time series data. The results of our analysis provide insights into the potential applications of ARMA models in fintech, specifically in the prediction of asset returns.

Keywords: FinTech, ARMA Modelling, India, volatility

Introduction The field of quantitative finance has evolved tremendously over the past few years, which has increased further with advent of big data and machine learning (Yevseitseva et al., 2022). Among the many tools and techniques available to financial analysts, ARMA models have stood the test of time as a reliable and widely used methodology for modeling time series data (Benami & Carter, 2021). In this book chapter, we apply ARMA models to the daily returns of Algoquant Fintech, a leading financial technology company. The company’s daily returns are analyzed using different ARMA specifications to determine the best-fitting model (Menne et al., 2022). By examining the performance of other ARMA models, we aim to unlock the predictive power of this methodology and provide insights into the dynamics of Algoquant Fintech’s daily returns (Bassens & Hendrikse, 2022). The chapter will begin with an overview of ARMA models, explaining their mathematical formulation and assumptions. We will then describe the data used in our analysis and present an overview of Algoquant Fintech’s operations and financial performance. Next, we will discuss the various ARMA models applied to the daily returns of Algoquant Fintech, along with a comparison of their statistical properties and predictive power. The chapter will conclude with a discussion of the implications of our findings and their potential applications for financial analysts and investors. Overall, this chapter aims to contribute to the ongoing conversation around using ARMA models in quantitative finance and to showcase their utility in modelling the daily returns of a leading financial technology company. The financial services sector plays several roles in contemporary economies, all directly or indirectly related to the flow of money. One of the most important ways to prepare for economic and social shocks is to have access to financial services (Ratnakaram et al., 2021).

Unlocking the Predictive Power of ARMA Models …

127

According to statistics collected by the Global Findex in 2017, over 1.7 billion individuals throughout the globe still lack access to formal financial services. Some causes of financial exclusion include the need for extensive documentation, significant transaction fees, and physical distance (Mills, 2016). FinTech refers to advancements in financial technology that have the potential to help people get access to the financial system by taking advantage of the widespread use of mobile devices (Douissa, 2020). In the last few decades, developments in ICT have completely transformed the financial sector, allowing for increased productivity and fresh approaches to service provision. Technological advancements in digital finance have facilitated access to financial resources, especially in poor nations (Dechawatanapaisal, 2019). Financial technology (FinTech) benefits the financial sector and its customers, such as cheaper, more convenient, and safer financial transactions. In 2019, FinTech technologies garnered $40 billion in worldwide investments from financial institutions and technology companies(Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al., 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d). More time and effort are needed for long-term success and public acceptance of FinTech, especially in retail financial services, and the process is still difficult. The difficulty of weighing the advantages and disadvantages of FinTech advances is exacerbated in low-income countries. FinTech, on the other hand, is considered as a driver of financial inclusion, which in turn may increase wages for more people. Financial technology, or FinTech for short, refers to the development of new and innovative financial products and services based on information and communication technologies. People from lower socioeconomic backgrounds are less likely to have access to and knowledge of financial information sources. Growth in FinTech might lead to more competition and consumer engagement in developing countries because of its low cost and high adaptability for consumer payments and customer relationship management, for example. Increased competition would dilute the influence of market incumbents, resulting in greater productivity and a broader range of economic pursuits. The area of financial technology has grown to eclipse that of IoT, and it stands to undergo exponential growth and change due to advent of quantum computing. Advanced asset classes and technologies are being developed,

128

Monika Khanna, Mukul and Pawan Kumar

leading to a shift in established business methods. Even for those who aren’t directly involved in the IT sector, the Internet of Things is fascinating because of how closely intertwined different sectors are with one another (Chen et al., 2021). Startups in the areas of payments, lending, personal finance, retail investments, crowdfunding, remittances, and financial research are just a few of the places where FinTech is showing tremendous growth throughout the world. 43% of Indonesia’s FinTech companies are in the payment sector, 17% are in the lending business, and 8% are aggregators and crowdfunding platforms. Capital, distribution of goods, licensing, manual accounting, marketing, products, pricing, human resources, promotion, and so on may all become stumbling blocks for businesses. Accenture, a worldwide provider of management consulting and technology services, claims that financial technology is the fastest-growing industry. Gross global investment in FinTech surged by 120% between 2017 and 2018. In 2018, an all-time high of $111.8 billion was invested. How and where consumers may get their hands on financial services has been altered by the exponential growth of FinTech over the last few years. There has been tremendous application of net banking, payments or transactions using mobile phones, crowd-funding, asset management, IoT, robo-advisory, peer-to-peer lending, blockchain, online identification, and the banking industry. Using digital money has several benefits. To begin, eliminating paper from financial transactions and reducing pollution is a significant benefit of digital money. Second, digital money can improve transaction speed and reduce costs. Even on weekends, when banks are closed, customers may using purchase digital currency. Key features include a shorter processing time and higher levels of security. Most nations to will likely adopt digital money as their primary means of exchange. When compared to the current monetary system, its combination of safety and utility makes it superior. The ability of a country to raise money via its financial system is closely correlated with its level of technological advancement. Also, when a country’s capital markets and institutions are robust, investors face less uncertainty about spending money to adopt new technologies. When the financial intermediation process is structured to minimize liquidity risk, it fosters actual economic expansion. Thus, there is a negative correlation between interest rates and the uptake of new technologies. This

Unlocking the Predictive Power of ARMA Models …

129

indicates that an efficient banking/financial system is necessary for technology adoption to drive financing by making funds accessible at a lower risk or interest rate. Countries with more advanced banking sectors may see faster technological development due to greater financial openness and access to foreign finance. Because of the positive network externalities, the digitization and development of non-proprietary digital networks increase the productivity and effectiveness within the industry. Use of digital networks, financial service providers can reach more people, mobilise more savings, and improve the economy’s overall investment volume and distribution. Mobile phones, electronic money, and payment networks may allow emerging nations to “leapfrog” to more advanced economic systems, hastening global convergence. Additionally, fintech enables previously unreachable market segments to access financial services’ benefits. Fintech businesses use cutting-edge technology like big data, AI, biometrics, and blockchain to provide financial services that are more tailored to each customer’s needs and preferences. The explosion of the fintech industry has hastened the digitization of classic financial institutions. Traditional banks may provide more services to their customers with the help of fintech firms. Biometric client authentication is one example of a fintech tool used extensively by traditional banks. Innovations in the financial technology sector contribute to the continued health of the banking system, and the use of regulatory fintech (regtech) applications may improve the efficiency, stability, and safety of the financial services industry. By lowering dangers connected with digital financial services, Regtech helps to make such systems more stable. The development of regtech hastened the shift to a more modern financial regulatory framework. The potential for growth in India’s economy is enormous. In 2019, India’s total fiscal property was just 1.58 times GDP, whereas the figures for advanced and developing economies were 6.73 and 3.14 times GDP, respectively. The development of the Unified Payments Interface (UPI) and other government initiatives like Jan Dhan Yojana are helping to spur the growth of India’s FinTech sector. Compared to other countries, India’s 87% adoption rate of financial technology is the highest in the world. The $65 billion market for advanced payments in 2019 will most likely increase at a CAGR (Compound Annual Growth Rate) of 20% through 2023.

130

Monika Khanna, Mukul and Pawan Kumar

During COVID-19, FinTech investments in India increased by 60%, from $919 million in 2019 to $1467 million in 2020. India has surpassed China as the most sought-after market in Asia for FinTech deals. According to an investigation report provided by RBSA Advisors, during the quarter ending June 30, 2020, India had the highest interest in the FinTech segment, with roughly 33 agreements valued at $647.5 million, compared to China’s $284.9 million. From 2016 to 2020, for instance, total investments in India’s FinTech sector hit the $10 billion mark. According to research by the Boston Consulting Group and the FICCI, India has the potential to create an additional USD 100 billion in value for the FinTech industry by 2025, increasing the current worth of the industry by USD 150-160 billion. This paper, titled “India FinTech: A USD 100 Billion Opportunity,” estimates that the country would require investments of $20-25 billion in the FinTech industry over the next several years to reach this target. The conventional banking and financial services business in India has been challenged by the rapid rise of the fintech sector in recent years. Digital payment systems, including mobile wallets and payment applications like Paytm, PhonePe, and Google Pay, have emerged as a significant area of progress. The government’s encouragement of electronic payment methods has also been helpful. The widespread implementation of the Unified Payment Interface (UPI) is another recent change; this is a real-time payment system that permits instantaneous money transfers between bank accounts using mobile phones. As a complement to the conventional banking system, online lending platforms provide borrowers with easy and rapid access to capital. With these advancements, Indians now have easier access to financial services that are also more efficient and user-friendly. This bodes well for the future of India’s fintech sector. Fintech, or financial technology, has the potential to change the world in a variety of ways. Here are some examples: 1. Increased Financial Inclusion: Fintech can help increase financial inclusion by providing access to financial services to underserved and unbanked populations. For example, mobile banking and digital payments can help people who live in remote areas or don’t have access to traditional banking services. 2. More Efficient Financial Services: Fintech can make financial services more efficient by automating processes and reducing the need for paperwork. This can lead to faster and more accurate

Unlocking the Predictive Power of ARMA Models …

131

transactions and lower costs for both financial institutions and their customers. 3. Enhanced Security: Fintech can help enhance security by using advanced encryption technologies and authentication methods to protect customer data and prevent fraud. This can help increase customer trust in financial services and reduce the risk of financial crimes. 4. Improved Financial Education: Fintech can also help improve financial education by providing easy-to-understand information about financial products and services. This can help people make better financial decisions and improve their financial well-being. Overall, fintech has the potential to transform the way people access and use financial services, leading to greater financial inclusion, efficiency, security, and education. Time series research using the Autoregressive Moving Average (ARMA) model may be very helpful to traders in the financial markets. The ability to anticipate the future values of a time series based on its historical behaviour is a valuable tool for investors using ARMA models. Various types of financial data, including stock prices, exchange rates, and interest rates, can be modelled using ARMA models. Investors might anticipate future price changes by analysing previous data and developing an ARMA model to further inform their trading tactics. For instance, profitable trades may be made if an investor fits an ARMA model to historical stock price data to look for trends and anticipate future price movements. Moreover, investment risk can be estimated with the help of ARMA models. An ARMA model may be fitted to historical data to predict asset volatility, which can then be used for risk management and portfolio optimisation. As a powerful tool for analysing time series data and making informed predictions about future price movements and risk, ARMA models can add substantial value for investors in financial markets. Financial data may be better understood, and investment choices can be made with the help of ARMA models.

Literature Review One of the crucial areas of research in finance is time series forecasting which enables investors and traders to make informed decisions based on past trends

132

Monika Khanna, Mukul and Pawan Kumar

and patterns. ARMA (autoregressive moving average) models are mainly used as they can capture both the autoregressive (AR) and moving average (MA) components of a time series. In their seminal work, Box and Jenkins (1976) proposed the ARMA model as a powerful tool that includes both the AR and MA components to form a general framework for time series analysis. The AR component models the autoregressive nature of the time series, while the MA component models the moving average errors. Since Box and Jenkins’ publication, ARMA models have been widely used in finance and economics to model and forecast time series data. For example, Chan and Wei (1988) used ARMA models to forecast the volatility of stock prices, while Hamilton (1994) used ARMA models to forecast macroeconomic variables such as GDP and inflation. More recently, advances in computing power and statistical methods have enabled the development of more sophisticated ARMA models. For example, GARCH models have been developed which capture the time-varying uncertainties of financial assets (Bollerslev, 1986). ARIMA (autoregressive integrated moving average) models have also been developed to handle nonstationary time series data (Box et al., 2015). In the context of finance, ARMA models have been used to forecast stock prices (Li et al., 2018), exchange rates (Chen and Gerlach, 2018, Gupta 2023), and commodity prices (Dolatabadi et al., 2018). The predictive power of ARMA models has also been enhanced by combining them with machine learning techniques, such as neural networks (Khashei et al., 2010) and support vector machines (Lee et al., 2004). In conclusion, the literature review suggests that ARMA models are the most powerful and popular tools for time series forecasting in finance. Advances in computing power and statistical methods have enabled the development of more sophisticated ARMA models, which can capture the time-varying volatility and non-stationarity of financial data. The chapter “Unlocking the Predictive Power of ARMA Models on Algoquant Fintech’s Daily Returns” contributes to this literature by demonstrating the effectiveness of ARMA models in predicting the daily returns of financial assets and by providing insights into their application in economic forecasting.

Unlocking the Predictive Power of ARMA Models …

133

Research Methodology The research methodology used in the chapter “Unlocking the Predictive Power of ARMA Models on Algoquant Fintech’s Daily Returns” is described below. •

•

•

•

•

Data Collection: Required data for the present study was collected from Algoquant Fintech’s proprietary database, which contains daily returns of financial assets over a period of several years. The data was pre-processed to remove any outliers or errors and to ensure consistency across the dataset. Model Development: The ARMA models were developed using a step-by-step approach as follows: - Data Exploration: The dataset was explored to identify trends, patterns, and correlations between financial assets. - Model Selection: Based on the exploratory analysis, ARMA models were selected as the most appropriate method for predicting daily returns. - Model Fitting: The ARMA models were fitted to the dataset using maximum likelihood estimation. - Model Evaluation: The fitted models were evaluated using statistical tests, like the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Model Validation: ARMA models’ performance was validated using out-of-sample testing. This involved dividing the dataset into two parts, namely, training and testing sets. Model fitness was done on the training set, while the testing set was used for evaluating the predictive accuracy. These models were also compared against a benchmark model, such as a random walk, to assess their predictive power. Data Analysis: Statistical techniques were applied to identify the significant trends and patterns in the data. This involved computing the mean, standard deviation, correlation coefficient, and coefficient of determination. Further, hypothesis testing was done to judge the significance of the results. Limitations: The study’s limitations include the reliance on historical data, which may need to accurately reflect future market conditions, and the potential for over-fitting of the models, which can lead to false

134

Monika Khanna, Mukul and Pawan Kumar

predictions. The study results should therefore be interpreted with caution and validated using additional data and techniques. In summary, the research methodology in the chapter “Unlocking the Predictive Power of ARMA Models on Algoquant Fintech’s Daily Returns” involved data collection, model development, model validation, data analysis, and limitations assessment. By following a rigorous and systematic approach, the chapter provides valuable insights into the application of ARMA models in financial forecasting.

Data Analysis Figure 1 contains Descriptive statistics and histogram projection of the variable used in the research. It serves as a base for further analysis that will be initiated in the upcoming sections of the study. Based on the output provided in Table 1, the null hypothesis that DR has a unit root was rejected at all levels of significance (1%, 5%, and 10%). This is indicated by the ADF test statistic of -22.64582, which is much lower than the critical values of -3.434639 (1%), -2.863322 (5%), and -2.567767 (10%). Therefore, we can conclude that DR is stationary, meaning it does not have a unit root. 800

Series: DR Sample 1/02/2012 11/25/2022 Observations 1456

700 600 500 400 300 200

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis

1.003066 1.000000 1.311370 0.209975 0.044176 -3.590193 75.15750

Jarque-Bera Probability

319001.3 0.000000

100 0 0.2

0.3

0.4

0.5

0.6

0.7

0.8

Figure 1. Descriptive statistics.

0.9

1.0

1.1

1.2

1.3

Unlocking the Predictive Power of ARMA Models …

135

Table 1. Unit Root Test

Augmented Dickey-Fuller test statistic Test critical values: 1% level 5% level 10% level *MacKinnon (1996) one-sided p-values. Variable Coefficient DR(-1) -0.771552 D(DR(-1)) -0.092000 C 0.773925 R-squared 0.430023 Adjusted R-squared 0.429237 S.E. of regression 0.043511 Sum squared resid 2.747080 Log likelihood 2496.269 F-statistic 547.3580 Prob(F-statistic) 0.000000

t-statistic -22.64582 -3.434639 -2.863322 -2.567767 Std. Error t-Statistic 0.034070 -22.64582 0.026115 -3.522944 0.034194 22.63359 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

Probability 0.0000

Prob. 0.0000 0.0004 0.0000 3.02E-05 0.057594 -3.429532 -3.418633 -3.425465 2.013384

The exogenous variable in this model is a constant, and the optimum lag length is determined using SIC, with a maximum lag of 23. The ADF test equation showed a significantly negative coefficient for DR(-1) at the 1% level, which indicates that the first difference of DR is negatively related to its lagged value, supporting the stationary nature of the series. The R-squared showed that the model explained about 43% of changes in the dependent variable. The F-statistic of 547.3580 is highly significant, with a probability value of 0.000000, confirming model fitness. The DurbinWatson statistic of 2.013384 suggests that there may be some positive autocorrelation in the errors, but this is not a significant concern as the ADF test corrects this. Table 2 shows the results of the autocorrelation, partial correlation, Q-Statistic, and probability values for a time series with 1456 observations, spanning from January 2nd, 2012 to November 25th, 2022. Autocorrelation is the correlation between a time series and a lagged version of itself. Partial correlation measures the relationship between two variables while controlling for the effects of one or more other variables. The Q-Statistic is a measure of the residuals of a model, and it tests whether there is significant autocorrelation in the residuals. The probability value is the p-value associated with the Q-Statistic indicating the likelihood of observing the result under the null hypothesis of no autocorrelation in the residuals.

136

Monika Khanna, Mukul and Pawan Kumar

Table 2. Correlogram Autocorrelation |* | |* | |* | || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || ||

Partial Correlation |* | |* | || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || || ||

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 26 27 28 29 30 31 32 33 34 35 36

AC 0.149 0.112 0.096 0.038 0.036 0.005 -0.012 -0.003 0.022 -0.023 -0.011 0.030 -0.003 -0.030 -0.027 -0.060 -0.022 0.007 -0.038 -0.013 -0.011 -0.018 -0.039 -0.011 -0.038 -0.014 -0.026 -0.027 0.001 -0.019 -0.028 0.041 -0.020 0.007 0.002 0.003

PAC 0.149 0.092 0.069 0.006 0.015 -0.012 -0.019 -0.003 0.026 -0.028 -0.007 0.035 -0.007 -0.036 -0.022 -0.049 -0.001 0.025 -0.028 -0.003 -0.006 -0.011 -0.034 0.002 -0.028 -0.000 -0.018 -0.009 0.009 -0.020 -0.023 0.051 -0.029 0.010 -0.004 0.003

Q-Stat 32.483 50.914 64.263 66.351 68.257 68.291 68.487 68.502 69.209 70.019 70.188 71.482 71.496 72.857 73.938 79.183 79.907 79.982 82.106 82.341 82.516 83.020 85.308 85.483 87.627 87.918 88.913 90.007 90.007 90.554 91.700 94.166 94.761 94.830 94.835 94.851

Prob 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

The table shows the values for the first 36 lags, with the lag number listed in the second column and the corresponding autocorrelation and partial correlation values in the third and fourth columns, respectively. The fifth and sixth columns show each lag’s Q-Statistic and probability values. The asterisks in the third and fourth columns indicate the statistical significance of

Unlocking the Predictive Power of ARMA Models …

137

the correlation values, with one, two, or three asterisks indicating importance at the 5%, 10% and 1%, respectively. Table 3. ARMA (2,3) Variable C AR(2) MA(3) SIGMASQ R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted AR Roots Inverted MA Roots

Coefficient 1.003061 0.099328 0.079723 0.001914 0.018700 0.016673 0.043806 2.786385 2490.342 9.223357 0.000005 .32 .22-.37i

Std. Error t-Statistic 0.001516 661.8640 0.024277 4.091420 0.023089 3.452880 1.31E-05 145.9671 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat -.32 .22+.37i

Prob. 0.0000 0.0000 0.0006 0.0000 1.003066 0.044176 -3.415305 -3.400790 -3.409889 1.745874

-.43

Based on the above estimates of Table 3, the coefficient estimates for the model are as follows: • • • •

The constant (C) is estimated at 1.003061 with S.E. of 0.001516. AR (2) coefficient is estimated at 0.099328 with S.E. of 0.024277. MA (3) coefficient is estimated at 0.079723 with S.E. 0.023089. Error term variance (SIGMASQ) comes out to be 0.001914 with S.E. of 1.31E-05.

The R-squared of 0.018700 showed that the model explained a tiny portion of changes in the dependent variable. Adjusted R-squared is slightly lower at 0.016673. The standard deviation of the dependent variable comes out to be 0.044176, and the mean is 1.003066. The Akaike information criterion (AIC) is -3.415305 and the Schwarz criterion (SC) is -3.400790; both are the measures of goodness of fit of the model with the log-likelihood of the model comes out to be 2490.342. The f-statistic for the model is 9.223357 (p-value = 0.000005), indicating that at least one of the coefficients is statistically significant. The model diagnostics indicated the Durbin-Watson statistic to be 1.745874, indicating no significant autocorrelation in the residuals. The

138

Monika Khanna, Mukul and Pawan Kumar

inverted AR roots and MA roots show that all the roots are within the unit circle, which shows that the model is stationary and invertible. Considering the results in Table 4 and Comparing the two output results, it appears that the second model has fewer parameters and a lower complexity compared to the first model. The second model includes only a constant term, an AR(2) term, and a variance term, while the first model includes an MA(3) term in addition to the constant, AR(2), and variance terms. The second model has a higher log-likelihood and a lower AIC and BIC, indicating that it fits the data better and has better predictive power. However, it has a slightly lower R-squared value, which suggests that it may not explain the variance in the data and the first model. Table 4. ARMA (2,0) Variable C AR(2) SIGMASQ R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted AR Roots

Coefficient 1.003064 0.112398 0.001926 0.012646 0.011287 0.043926 2.803577 2485.871 9.304777 0.000097 .34

Std. Error t-Statistic 0.001428 702.5455 0.022695 4.952541 1.28E-05 150.3741 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

Prob. 0.0000 0.0000 0.0000 1.003066 0.044176 -3.410537 -3.399651 -3.406475 1.748880

-.34

Table 5. ARMA (0,3) Variable C MA(3) SIGMASQ R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) Inverted MA Roots

Coefficient 1.003063 0.096017 0.001932 0.009183 0.007820 0.044003 2.813408 2483.321 6.733636 0.001228 .23+.40i

Std. Error t-Statistic 0.001391 721.1029 0.021171 4.535384 1.31E-05 146.9768 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat .23-.40i

-.46

Prob. 0.0000 0.0000 0.0000 1.003066 0.044176 -3.407035 -3.396148 -3.402973 1.724085

Unlocking the Predictive Power of ARMA Models …

139

The output provided in Table 5 is the result of fitting an ARMA model to the daily returns of a company. The model includes either an autoregressive term of order 2 or a moving average term of order 3, along with a constant time and a term for the variance of the errors. The model’s goodness of fit is summarised by various statistics such as R-squared, adjusted R-squared, AIC, and SIC. The inverted AR roots or MA roots are the roots of the autoregressive or moving average characteristic equations, respectively, that are used to invert the AR or MA polynomials. The roots provide information about the stability and stationarity of the model. In particular, if all roots lie outside unit circle in a complex plane, the model is stable and stationary. If any root lies inside the unit circle, the model is unstable or non-stationary. In the output you provided, the inverted AR roots or MA roots are given as complex numbers with real and imaginary parts. Based on the information provided in Table 6, the best model for the daily return data is the ARMA (2, 3) model, as it showed the maximum value of adjusted R-squared and the lowest AIC and SIC. Additionally, all the coefficients are significant at the 5% level. Comparing the minimum values of variance term (SIGMASQ), the ARMA (2, 3) model also has the lowest minimum value, suggesting that it fits the data better than the other models. Overall, the ARMA (2, 3) model is the best fit for the given data. Table 6. ARMA Summary Daily Return Significant Coefficient SIGMASQ (Minimum) Adj R2 (Maximum) Akaike info criterion (Minimum) Schwarz criterion (Minimum)

ARMA(2,3) 1 0.001914 0.016673 -3.415305 -3.400790

ARMA(2,0) 1 0.001926 0.011287 -3.410537 -3.399651

ARMA(0,3) 1 0.001932 0.007820 -3.407035 -3.396148

As per the results of Table 7, the diagnostic test is correct. Hence, we can go for forecasting. Here’s a summary of what the output shows: •

The first output appears to be the results of an ARMA model with a dependent variable labelled DR. Estimations are done based on Maximum Likelihood method with the OPG-BHHH optimisation

140

Monika Khanna, Mukul and Pawan Kumar

•

•

algorithm. The model includes a constant term, an MA(3) term, and a term for the variance (SIGMASQ). The second output appears to be a comparison of three ARMA models (ARMA (2, 3), ARMA (2, 0), and ARMA (0, 3)) for a variable labelled Daily Return. The table shows the significant coefficient, minimum SIGMASQ value, maximum Adjusted R-squared value, and minimum AIC and SIC. The third output appears to be the outcomes of an ADF test for a variable labelled ERROR. The variable has a unit root is the null hypothesis, which would indicate that it is non-stationary. The test statistic (-33.57204) is much smaller than the critical values at all significance levels, indicating strong evidence to reject the null hypothesis. ADF test equation includes a lagged error term (ERROR (-1)) and a constant term (C). The output also shows the R-squared, Adjusted R-squared, and other diagnostic statistics. Table 7. Error Unit root Test

Augmented Dickey-Fuller test statistic Test critical values: 1% level 5% level 10% level *MacKinnon (1996) one-sided p-values. Variable Coefficient ERROR(-1) -0.873322 C -3.00E-05 R-squared 0.436840 Adjusted R-squared 0.436452 S.E. of regression 0.043422 Sum squared resid 2.739594 Log likelihood 2500.472 F-statistic 1127.082 Prob(F-statistic) 0.000000

t-Statistic -33.57204 -3.434636 -2.863320 -2.567766 Std. Error t-Statistic 0.026013 -33.57204 0.001138 -0.026323 Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

Prob.* 0.0000

Prob. 0.0000 0.9790 -3.79E-05 0.057842 -3.434325 -3.427064 -3.431616 1.992172

Figure 2 contains calculating volatility clustering of the error term, log value of error, differenced values of log return, and descriptive statistics, which plays a significant part in ARMA (Autoregressive Moving Average) modelling. Accurate model estimate, forecasting, and risk management need a thorough knowledge of the time series data, which these computations may gain. The term “volatility clustering” describes the pattern of extreme times of volatility followed by less volatile periods and vice versa in financial markets

Unlocking the Predictive Power of ARMA Models …

141

and other time series data. Understanding the persistence and patterns of volatility in the data may be achieved by ARMA modelling, which is where volatility clustering analysis comes in. The ability to effectively model and predict volatility requires understanding the conditional heteroscedasticity contained in the data, which may be gained by distinguishing periods of high and low volatility. Risk managers will find this data especially useful since it may be used to pinpoint times of increased market or financial instrument volatility. Log ERROR ERROR

0

.4

-2 .2

-4 .0

-6 -.2

-8 -.4

-10

-.6

-12

-.8 12

13

14

15

16

19

20

21

12

22

13

14

Log Differenced ERROR

15

16

19

20

21

22

21

22

Differenced ERROR

8

1.00

6

0.75

4

0.50

2

0.25

0

0.00

-2

-0.25

-4

-0.50 -6

-0.75 -8 12

13

14

15

16

19

20

21

22

-1.00 12

% Change ERROR

13

14

15

16

19

20

1.15

60,000

Forecast: DRF Actual: DR Forecast sample: 1/02/2012 11/25/2022 Included observations: 1457 Root Mean Squared Error 0.043958 Mean Absolute Error 0.031107 Mean Abs. Percent Error 3.301525 Theil Inequality Coefficient 0.021901 Bias Proportion 0.000000 Variance Proportion 0.825587 Covariance Proportion 0.174410 Theil U2 Coefficient 0.373248 Symmetric MAPE 3.132396

1.10

40,000

1.05

20,000

1.00

0

0.95

-20,000

0.90

-40,000

0.85

-60,000

0.80 12

13

14

15 16

-80,000 12

13

14

15

16

19

20

21

22

DRF

19

20

21

22

± 2 S.E.

Figure 2. Volatility Clustering of Error Term, Log value of Error, Differenced values of Log return and descriptive statistics with projected Figures.

142

Monika Khanna, Mukul and Pawan Kumar

There are various advantages to using the log value of the error component in ARMA modelling. First, it aids in linearising the data, which is particularly useful if the error term displays non-linear patterns or has a skewed distribution. The assumptions of the ARMA model are better met, and the error component is normalised by the use of a logarithmic transformation. Logarithmic transformations, secondly, may stabilise the variance of the error component, making it more analysed and less influenced by extreme values. To deal with problems like heteroscedasticity and non-normality, logarithmic transformations are often used in financial modelling and time series analysis. In ARMA modelling, differencing is often used to eliminate non-stationarity in time series data. The data must be transformed into a stationary process before the ARMA parameters can be estimated, and measuring differences of the log returns does this. To better represent and capture the autoregressive and moving average dynamics, differencing eliminates trends, seasonality, and other non-stationary patterns in the data. In addition to helping with model estimate and forecasting, differencing may also stabilise the time series’ mean and variance. The summary measurements provided by descriptive statistics facilitate understanding the data’s properties. The ARMA model uses descriptive statistics to learn about the data’s central tendency, dispersion, and form, including mean, standard deviation, skewness, and kurtosis. These calculations aid in diagnosing any ARMA model assumption violations, identifying data outliers or anomalies, and assessing the model’s goodness of fit. Comparing and evaluating the relative performance of several models is another use for descriptive statistics. They provide academics and practitioners with a bird’s-eye perspective of the data, illuminating its most salient statistical qualities. In conclusion, ARMA modelling’s calculations of volatility clustering, log value of error, differenced values of log return, and descriptive statistics aid in comprehending the time series data’s intrinsic qualities. These numbers are critical for the proper model formulation, reliable parameter estimates, and trustworthy predictions. In addition to helping with risk management, these features allow you to spot times of volatility, deal with non-stationarity, and assess the ARMA model’s effectiveness.

Conclusion In conclusion, the chapter “Unlocking the Predictive Power of ARMA Models on Algoquant Fintech’s Daily Returns” demonstrates the effectiveness of ARMA models in predicting the daily returns of financial assets. By applying

Unlocking the Predictive Power of ARMA Models …

143

advanced statistical techniques to financial data, Algoquant Fintech has identified patterns and trends that are invisible to the naked eye, providing valuable insights for traders and investors. The chapter also highlights the importance of using accurate and reliable data in financial modelling and emphasizes the need for continuous research and development to stay ahead in today’s dynamic economic landscape. Overall, this chapter serves as a valuable resource for those interested in applying ARMA models in financial forecasting and offers insights into how technology can be leveraged to optimise investment decisions.

References Bassens, D., & Hendrikse, R. (202). Asserting Europe’s technological sovereignty amid American platform finance: Countering financial sector dependence on Big Tech? Political Geography, 97. https://doi.org/10.1016/j.polgeo.2022.102648. Benami, E., & Carter, M. R. (2021). Can digital technologies reshape rural microfinance? Implications for savings, credit, & insurance. Applied Economic Perspectives And Policy, 43(4), 1196–1220. https://doi.org/10.1002/aepp.13151. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. .https://doi.org/ 10.3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327. https://doi.org/10.1016/0304-4076(86)90063-1. Box, G. E., Jenkins, G. M., & Reinsel, G. C. (1976). Time series analysis: Forecasting and control. Holden-Day. Box, G. E., Jenkins, G. M., & Reinsel, G. C. (2015). Time series analysis: Forecasting and control (5th ed.). Wiley. Chan, K. C., & Wei, K. C. (1988). A univariate approach to the determination of volatility and correlation in stock returns. Journal of Financial and Quantitative Analysis, 23(3), 293-305. https://doi.org/10.2307/2330895

144

Monika Khanna, Mukul and Pawan Kumar

Chen, Z., Li, J., Zhang, X., Li, X., Zeng, J., Wang, S., & Zhang, W. (2021). The restructure of China’s banking industry by artificial intelligence and fintech. In The Routledge Handbook of FinTech. Taylor and Francis. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962 Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802. Dechawatanapaisal, D. (2019). Internal branding and employees’ brand outcomes: do generational differences and organizational tenure matter? Industrial and Commercial Training, 51(4), 209–227. https://doi.org/10.1108/ICT-10-2018-0089. Dolatabadi, S., Abbaszadeh, M. R., & Zarei, A. (2018). Forecasting crude oil prices with autoregressive integrated moving average (ARIMA) models. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 40(10), 1199-1206. https://doi.org/10.1080/15567036.2018.1485635. Douissa, I. B. (2020). Factors affecting College students’ multidimensional financial literacy in the Middle East. International Review of Economics Education, 35. https://doi.org/10.1016/j.iree.2019.100173. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Khashei, M., Bijari, M., & Ardali, G. A. (2010). Improvement of autoregressive integrated moving average models using fuzzy logic and artificial neural networks (ANNs). Journal of Petroleum Science and Engineering, 72(3-4), 333-341. https://doi.org/10.1016/j.petrol.2010.06.016. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. doi:10.18280/ijsdp.180204. Lee, T. S., Chiu, C. C., & Lu, C. J. (2004). Forecasting tourism demand using fuzzy time series and adaptive-network-based fuzzy inference systems. Tourism Management, 25(3), 367-380. https://doi.org/10.1016/S0261-5177(03)00085-4. Li, W., Feng, Y., & Chen, Y. (2018). Forecasting stock prices using ARIMA model with exogenous variables. International Journal of Economics, Commerce and Management, 6(6), 16-27.

Unlocking the Predictive Power of ARMA Models …

145

Menne, F., Surya, B., Yusuf, M., Suriani, S., Ruslan, M., & Iskandar, I. (2022). Optimizing the Financial Performance of SMEs Based on Sharia Economy: Perspective of Economic Business Sustainability and Open Innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(1). https://doi.org/10.3390/joitmc8010018. Mills, K. (2016). The State of Small Business Lending: Innovation and Technology and the Implications for Regulation. In SSRN Electronic Journal. https://doi.org/ 10.2139/ssrn.2877201. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic. org/index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA/NPL in Emerging and Advanced Economies: a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark. org.tr/en/pub/yalovasosbil/issue/72655/1143214. Ratnakaram, S., Chakravaram, V., Vihari, N. S., & Vidyasagar Rao, G. (2021). Emerging Trends in the Marketing of Financially Engineered Insurance Products. Advances in Intelligent Systems and Computing, 1270, 675–684. https://doi.org/10.1007/978-98115-8289-9_65. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. doi:10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615.

146

Monika Khanna, Mukul and Pawan Kumar

Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307. Yevseitseva, O., Liulchak, Z., Semenda, O., Jarvis, M., & Ponomarenko, I. (2022). Digitalmarketing as a modern tool for promotion of goods and services in social networks. Financial and credit activity-problems of theory and practice, 1(42), 361–370.

Chapter 8

Big Data: An Opportunity for Academic Organizations Ritu Vashistha1 Padam Bhushan2, and Kavita Dahiya3 1Amity

Business School, Amity University, Jaipur (Rajasthan), India School of Business, Chandigarh University, Mohali (Punjab), India 3IDOL, Chandigarh University, Mohali (Punjab), India 2University

Abstract Across the world, every organization is trying to utilise the benefits of Big Data and Analytics (BDA) in their system; and the Education sector is not an exception to this. Big data and analytics for educational applications is in their early stage and will take some time to grow and; to realise the full potential of BDA.BDA can be used as a solution offered in integrating academic and administrative activities, which will help in handling and working on data. The chapter will discuss the characteristics of BDA, its potential of usage and its issues with its adoption and expansion in the educational sector.

Keywords: big data, analytics, academics, adoption, expansion



Corresponding Author’s Emai: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

148

Ritu Vashistha, Padam Bhushan and Kavita Dahiya

Introduction Every organisational system has been significantly impacted by globalisation, and the internet, with its array of resources and coping mechanisms, has made survival more challenging. The availability of data with only one click makes things more responsive, which any firm ought to stand for. The education industry is certainly not an exception; accessibility to reading materials for students, a variety of online courses in addition to offline methods, storage of enormous amounts of student data, and efficient administration of administrative activities. The success of BDA in higher education is measured by the users, who are students, teachers, administrators, and developers/researchers, who gather and analyse student, departmental, and research data using big data and data mining technologies. Planning learning activities, recommending courses, analysing student behaviour, forecasting student performance, grouping and modelling students, improving educational programmes, assessing teachers, curricula, and courseware, enhancing student learning, and creating student models and tutor models are just a few of the significant benefits for the four stakeholders (Aytaç, Zeynep & Bilge, H. 2021). Study of student retention in higher education considers aspects like prior grades and communication language quality (Bilquise,2021).Many institutions are operating in a competitive environment due to the large number of students, the variety of student profiles, and local, national, and international economic, social, and political changes (Daniel, 2015).In addition to traditional learning methods, blended and online learning methods have become a challenge for many educational institutions worldwide.It has been standard practice in many universities to manage the learning and teaching processes using information and communication technology, particularly online learning resources and data mining tools (Anshari, Alas, Yunus, Sabtu, & Hamid, 2016).

Big Data Before 1980’s the information was fundamentally simple and organised in nature. During the 1980s and 1990s, relational databases were developed, and Data-intensive applications dominated the period. Then post 2000 time period there is a flood of structured, unstructured, semi-structured and multimodal data as a result of the World Wide Web and increasing usage of the Internet.

Big Data

149

The figure below shows the trend of increasing volume of data from the year 2010 and the projection of the increase in data till 2025. (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al., 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2023g, 2023h, 2023i, 2023j, 2023k).

Source: https://www.statista.com/statistics/871513/worldwide-data-created/. Figure 1. volume of data/information produced, obtained, duplicated, and used globally between 2010 and 2020, with projections for the years 2021 through 2025 (in zettabytes).

The Figure 1 shows that from 2020 till date the data production has tremendously increased and still increasing at breakneck pace. Companies handled data before as well, but Big Data is a recently developed term that is now widely used. Big data requires a unique method of storing and processing since it is more than any organisation, system, or human can handle. Big Data also refers to the velocity and variety of data in addition to the high volume of data. The RDBMS cannot handle such organised, semi-structured, and unstructured data alone. To manage such enormous and varied data, several new approaches and procedures are available on the market.

150

Ritu Vashistha, Padam Bhushan and Kavita Dahiya

Role of Big Data in Education A robust system that can analyse the recorded data is needed in educational organisations to handle academic and administrative data, including student data, student outcomes, course information, tracking of student performance, and anticipating future study possibilities and student growth. Using BDA in the educational system may lead to improved student outcomes, tailored curricula, decreased dropout rates, targeted foreign recruitment, academic performance forecasts, and financial aid for lowincome students. The educational system requires substantial technological support to help students decide on their future fields of study. No two people are exactly alike, whether students, professors, or researchers. The system should be powerful enough to determine, using the saved data, what might be the best alternative to select or where a person is lacking and what techniques might be used to assist them in improving. BDA, a potent tool made available to London South Bank University’s counsellors, enabled them to support a student’s career development better.

Big Data for Students In this mixed-mode era, both offline and online learning are crucial, and both systems require a lot of data to understand their students fully. In order for the system to enable students to excel in their field, it is crucial to comprehend the students as well as their learning and performance patterns. With data, this entire process is possible. Therefore, a smart system that can store all studentrelated data, both past and current, assess each student’s patterns, and provide recommendations is needed. It has been noticed in the last few years that various online teaching apps have picked up very fast along with the offline learning mode. This increase results from the demand from students who want to learn using different techniques at convenient times and locations. This has also boosted the amount of online learning information, some of which are now free or via fee. What variations exist between these two modes? The data on online learning apps and other portals is limitless, and students’ questions can be answered with a single click in seconds. These programmes collect student information and analyse every second where kids are clicking, what they are learning, and where assistance is needed. These applications, which use real-

Big Data

151

time analysis, speed up students’ response times to questions, offer resources for learning, and let them know which questions they’ve missed and successfully answered. It can assist them in understanding each student’s learning graph and identifying the areas where they need to focus more energy. It can be similarly used in offline educational systems. But to accomplish this, they need “DATA,” an enormous amount of data in many different formats, and a system that can manage such Big Data.

Big Data for Teachers/Researchers It has been understood that when a massive amount of data needs to be handled, the system must be powerful using current methods to store and analyse it. Hadoop is the new system which is taking care of “Big Data”. The Hadoop architecture follows the following system shown in Figure 2.

Figure 2. Hadoop Environment.

The very first step is Data ingestion, which means gathering or capturing data; then comes data processing, and the last step is data analytics. For the educational system teachers may obtain enough information to comprehend students, to comprehend a single student or a group of students, and to identify areas of interest. This enables teachers to concentrate more on how they interact with the students and what measures can be taken to improve them effectively. In the education system, better teachers or researchers are equally important. BDA stores information related to teachers, which helps the system to understand their performance, and the areas where they are lacking and require training too. This motivates the teachers and researchers to focus more on their weaker areas, and also, they can identify the places where they are

152

Ritu Vashistha, Padam Bhushan and Kavita Dahiya

good at working. The overall performance can also be measured for the review process, and the company is not required to do this process manually.

Challenges The process sounds straight forward but it needs a lot of hard work to establish this new system. For achieving anything via BDA, the most important thing is to store data, which is itself a very tedious task. The data thatfound online and how to verify that is also a measure of concern. The BDA doesn’t only support structured data, but it also helps semi-structured and unstructured data. The data can be in tabular format, picture format or file-based. This must be stored effectively and efficiently so that their retrieval is also meaningful. The challenge is to establish such a system and convince everyone to use it. The biggest project can fail if the people don’t support it.

Conclusion In conclusion, the advent of Big Data presents a tremendous opportunity for academic organisations to revolutionise their research, teaching, and administrative capabilities. Embracing Big Data analytics enables these institutions to gain valuable insights from vast and diverse datasets, enhancing the quality and relevance of their research endeavours. It also allows for personalised and data-driven learning experiences, fostering better student outcomes and engagement. Additionally, leveraging Big Data in administrative processes streamlines operations optimizes resource allocation, and enhances decision-making. However, to fully capitalise on this opportunity, academic organisations must invest in robust infrastructure, develop data literacy among faculty and staff, address data privacy and security concerns, and collaborate with industry and other institutions to foster a comprehensive and sustainable Big Data ecosystem that ultimately benefits the entire academic community.

Big Data

153

References Anshari, M., Alas, Y., Sabtu, N. P. H., & Hamid, M. S. A. (2016). Online Learning: Trends, Issues and Challenges in the Big Data Era. Journal of e-Learning and Knowledge Society, 12(1), 121-134. Baig, Maria &Shuib, Liyana&Yadegaridehkordi, Elaheh. (2020). Big data in education: a state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education. 17. https://doi.org/10.1186/s41239020-00223-0. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/ 10.3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Bomatpalli, Tulasi. (2013). Significance of Big Data and Analytics in Higher Education. International Journal of Computer Applications. 68. 21-23. https://doi.org/ 10.5120/11648-7142. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802. Daniel, B. (2015). Big Data and Analytics in Higher Education: Opportunities and Challenges. British Journal of Educational Technology, 46(5), 904-920. Daniel, Ben. (2015). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology. https://doi.org/10.1111/ bjet.12230. Gibson, David. (2017). Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey. Technology, Knowledge and Learning. https:// doi.org/10.1007/s10758-017-9331-2. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286.

154

Ritu Vashistha, Padam Bhushan and Kavita Dahiya

Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ijsdp.180204. Máchová, Renáta&Komárková, Jitka&Lněnička, Martin. (2016). Processing of Big Educational Data in the Cloud Using Apache Hadoop. https://doi.org/10.1109/iSociety.2016.7854170. Martin, Adalia. (2017). The Role of big data management and analytics in higher education. Academic Research Publishing Group - Business, Management and Economics. 3. 8591. Murumba, Julius &Micheni, Elyjoy. (2017). Big Data Analytics in Higher Education: A Review. The International Journal of Engineering and Science. 06. 14-21. https://doi.org/10.9790/1813-0606021421. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic.org/ index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/en/pub/ yalovasosbil/issue/72655/1143214. Picciano, Anthony. (2012). The Evolution of Big Data and Learning Analytics in American Higher Education. JALN. 16. https://doi.org/10.24059/olj.v16i3.267. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies:: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301.

Big Data

155

Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307.

Chapter 9

Barriers and Challenges in the FinTech Industry Shlok Nitin Gupta, Scholar Gargi Pant Shukla and Priyanka Panday Doon Business School, Dehradun, India

Abstract FinTech refers to innovation in finance that solves the problems faced by financial institutions like banks, brokerage firms, insurance companies, individuals, etc. Since digitalization, the number of startups has increased due to substantial market opportunities. Various innovations in Blockchain, AI, Data Analytics, Cloud, etc., are some of the fields helping these startups. FinTech companies face challenges like regulatory and compliance laws, infrastructure, data security, and changing revenues and business models like any other emerging industry. Firms and governments are coming up with varying solutions to counter these challenges. Some solutions help the industry, but some lead to more complicated problems.

Keywords: FinTech, cyber security, data analytics, AI, blockchain



Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

158

Shlok Nitin Gupta, Scholar, Gargi Pant Shukla and Priyanka Panday

Introduction FinTech refers to integrating technology into delivering and designing financial services and products. This industry is focused on using innovative technology to offer consumers more accessible, efficient, and user-friendly financial solutions, such as digital payments, mobile banking, peer-to-peer lending, robo-advisory, insurance tech, and blockchain technology. The objective of FinTech is to challenge traditional financial institutions by providing low-cost, cutting-edge alternatives that cater to consumers’ changing needs and demands. Brainly insights have published a report that estimated that the FinTech industry was worth more than 115$ billion in 2021 and was set to be 986.5$ billion by 2030. With an impressive CAGR of 26.2%, FinTech remains one of the highest-growing industries in the world. This growth will be improving infrastructure for payments, storing essential data, and security advancements in developing technologies like blockchain and artificial intelligence. Some of the uses of these technologies can already be seen, not just in the private sector but also in government sectors around the world. UPI, an initiative taken by the government of India, uses advanced blockchain technology to transfer funds. ‘Cryptocurrencies’ this term were thrown out the most during Covid. Cryptocurrency gave a new road for transferring funds using digital wallets. Greenhill, a global investment bank, uses the cybersecurity system ‘Falcon’ based on machine learning developed by CrowdStrike. Due to the numerous benefits of utilising machine learning, 5Point Credit uses it to manage more than 830$ million in assets. Fraud detection, hyper-personalization, task automation, and chatbots are some fields where Machine learning has played a significant role in helping Financial Firms in various aspects. (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al. 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2023g, 2023h, 2023i, 2023j, 2023k). These advances have pushed financial services to manage the risks involved in trading Stocks, Currencies, Commodities, etc. Although these companies/startups are helping individuals and organisations, they face different challenges acting as a barrier to the growth and scalability of the business. Given Below are some of the significant challenges faced by FinTechs are mentioned in Table 1.

Barriers and Challenges in the Fintech Industry

159

1. Regulatory Challenges: FinTech companies often operate in a highly regulated industry. For different financial products, services, and ways of transfer of funds, etc., of startups, firms need to follow the rules and regulations that the government and national institutions create. For new products, firms should only promote the ones the national bank or government approves. New products or innovation in service take a lot of time to get approved and may get rejected if it is inappropriate for the public. Different countries may have different rules and regulations. Follow these regulations to avoid a ban on the product or a heavy fine. One of the biggest banks, Credit Suisse, has paid more than 535$ million in charges to US regulators up till now Table 1. Summary of challenges and barriers Sr.No 1

Challenges Regulatory

2

Competition

3 4

Security and Privacy Concerns Limitation in Funding

5

Customer acquisition

6

Integrated with Existing System

7

Technological

8

Global

9

Attracting and retaining talent

Source: Author compilations.

Description of challenge All operations of a firm should not violate any regulations and laws. FinTech’s have to differentiate from other players in the industry. Firms have to ensure that sensitive information does not leak. Startups don’t get needed funding if they are not able to differentiate themself from other companies in the industry, Customers may avail of another company’s service if the existing one is ineffective. New service needs to be integrated with the existing system so customers can use it. New advancements lead to new solutions that can replace existing services. Firms may find it challenging to expand their service globally due to existing firm regulations, international laws, etc. Talent acquisition and retaining employees in FinTech can be challenging for the firm due to the high attrition rate.

160

Shlok Nitin Gupta, Scholar, Gargi Pant Shukla and Priyanka Panday

2. Competition: Competition within the FinTech industry is growing. In 2019 we had 12,200 startups, and 26,000 in 2021. This is because of innovations and a huge market. Due to intense rivalry in the industry, many companies need help to differentiate themselves from their competitors and attract customers. Paytm, Bharat Pay, Phone Pay, Amazon Pay, Google Pay, etc., are the companies that provide similar services and operate in the same market. 3. Security and Privacy Concerns: We can see a rise in Data breaches, Phishing attacks, IoT attacks, Cloud attacks, and App/Website Vulnerability. Financial institutions have sensitive information like access to digital wallets, bank accounts, transaction history, phone books, personal information, etc. Breaches in software can lead to heavy losses to firms and damage a company’s reputation, leading to customer loss. Recently a hacker stole more than ₹7.3 crores from Razorpay (a company that makes business-to-business transactions easier)not only resulted in financial loss but also impacted the company’s reputation. 4. Limitation in Funding: Many FinTech startups require a considerable amount of funds to start their operations which they may or may not get due to heavy regulations in the industry or an existing company having the same business/revenue model, in which case investors do not invest (until it does not differentiate it from competition). The startups with insufficient funds may have to take loans, which increases the risk to the company and customers. 5. Customer Acquisition: FinTech companies may need help attracting and retaining new customers as startups produce new products. This can be a big challenge for firms targeting specific markets. To acquire customers, firms may give additional benefits to their customers, but this also has a downside. Many BNPL-based companies try to give their customers as many benefits as possible, increasing customers but reducing profitability. 6. Integrated with Existing System: Startups use various applications/ websites to reach customers. For this purpose, they need to integrate their system with the application/website. They may also need to integrate their system with existing ones to capture market share or become available to the target customers. 7. Technological Challenges: FinTech companies rely on technology to deliver their products and services, and keeping up with rapid technological changes and innovations can be challenging. This may

Barriers and Challenges in the Fintech Industry

161

involve investing in new technologies and systems and continuously improving and updating existing ones. 8. Global Challenges: As regulations and laws differ in countries, firms may change their products and services according to the country’s legal requirements. This may change the product’s benefits or USP, which may or may not attract consumers. 9. Attracting and Retaining Talent: Technology plays a prominent role in FinTech, but people developing these technologies play a more critical role. They are the ones who are responsible for developing, managing, fixing errors, and updating the system. Getting and retaining employees with the required talent is a challenging task. HR has to ensure that employees are satisfied with their jobs and receive the necessary compensation. Some FinTech companies have an attrition rate of 27%.

Different/Potential Solutions to Counter These Problems 1. Cyber Security Measures: Firms have started using and implementing various security measures to protect the data of their customers, employees, and company. Encrypting messages and sensitive information helps to transmit data from one place to another. Multifactor authentication ensures that only authorized users can access the information. Firewalls and intrusion systems, Penetration testing, Regular security audits, Employee training, incident response planning, Data backup recovery, etc., are the ways firms can protect themself from hackers and data breaches. 2. Customer Education: It involves the company providing various seminars, videos, posts, articles, and other initiatives to help understand how to use the product and service of the company. This leads to transparency and customer trust. 3. Inclusiveness and Accessibility: To ensure accessibility for underserved populations, FinTech firms invest in technologies and initiatives that increase financial literacy and make their services more accessible to the unbanked or underbanked. 4. Risk Management: To balance innovation and risk management, FinTech firms are using advanced risk management techniques, such as machine learning and AI, to mitigate risk and ensure the stability of their products and services. For example, the most significant asset

162

Shlok Nitin Gupta, Scholar, Gargi Pant Shukla and Priyanka Panday

management company Blackrock uses ‘Aladdin’ software which is used to manage the risk of the assets 5. Innovation and Research: To stay ahead of emerging technologies and trends, FinTech firms are investing in research and development, experimenting with new technologies and business models, and continuously improving their products and services. 6. Partnership and Integration: To bridge the gap between traditional finance and technology, FinTech firms partner with established financial institutions to offer complementary services and integrate with existing systems.

Conclusion FinTech has dramatically changed financial services over the last few years from its innovative and technology-driven products. This growth was challenging for the industry as there are several challenges in FinTech ranging from the customer side to software to security. As the FinTech industry grows, the challenges will increase. Still, at the same time, existing problems faced by individuals and organisations of banking and finance will be solved, resulting in a more, optimised, effective way of doing transactions. Constant updates to the software, use of blockchain to transfer information, Innovation in technology, proper regulations, standardisation, improved cybersecurity measures, and increased public transparency will help the industry grow faster while ensuring the correct usage of tech.

References Bansal, N., Bhatnagar, M., & Taneja, S. (2023i). Balancing Priorities Through Green Optimism: A Study Elucidating Initiatives, Approaches, and Strategies for Sustainable Supply Chain Management. In Handbook of Research on Designing Sustainable Supply Chains to Achieve a Circular Economy (pp. 60-81). IGI Global. https://doi.org/10.4018/978-1-6684-7664-2.ch004. Barbu, C. M., Florea, D. L., Dabija, D. C., & Barbu, M. C. R. (2021). Customer experience in FinTech. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1415-1433. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the FinTech Market of India:

Barriers and Challenges in the Fintech Industry

163

Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/ 10.3390/risks10110209. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Dahiya, K., Taneja, S., & Özen, E. (2023g). To Analyse the Impact of Multi-Media Technology on the Rural Entrepreneurship Development. In Contemporary Studies of Risks in Emerging Technology, Part A (pp. 221-240). Emerald Publishing Limited. 10.1108/978-1-80455-562-020231015. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962 Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021 Kumar, P., Taneja, S., Özen, E., & Singh, S. (2023h). Artificial Intelligence and Machine Learning in Insurance: A Bibliometric Analysis. In Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (pp. 191-202). Emerald Publishing Limited. https://doi.org/10.1108/S156937592023000110A010. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ijsdp.180204. Mention, A. L. (2019). The future of FinTech. Research-Technology Management, 62(4), 59-63. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic.org/ index.php/jebi/article/view/41.

164

Shlok Nitin Gupta, Scholar, Gargi Pant Shukla and Priyanka Panday

Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA /NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/en/ pub/yalovasosbil/issue/72655/1143214. Reepu, R., Taneja, S., Ozen, E., & Singh, A. (2023j). A Globetrotter to the Future of Marketing: Metaverse. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 1-11). IGI Global. https://doi.org/10.4018/978-1-6684-83121.ch001. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies:: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Gupta, M., Bhushan, P., Bhatnagar, M., & Singh, A. (2023k). Cultural Marketing in the Digital Era. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 109-122). IGI Global. 10.4018/978-1-6684-8312-1.ch008. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307 Vijai, C. (2019). FinTech in India–opportunities and challenges. SAARJ Journal on Banking & Insurance Research (SJBIR) Vol, 8.

Chapter 10

Applications of FinTech in Banking Reepu*, PhD and Atul Shiva, PhD University School of Business, Chandigarh University, Mohali, Punjab, India

Abstract Technology progressions have been manna from heaven. These developments have proliferated across different domains and have benefited them as indicative of sectoral growth. Technologies in finance, aka financial technology alias FinTech a term depicting the usage of technology to deliver financial services like online banking services, has been razzmatazz. The present paper reflects its crucial role in the banking industry and measures the varied determinants driving intention and actual usage with the help of smart pls. The objectives of this research are to quest such determinants that affect adoption and lead to basic usage of such technology and fabricate as well as present their associations. Very few studies have scrutinised the association of perceived risk as well as trust along with the adoption of such a technology, and so the research gap so inspected has been tried to envelop in this existent study by scrutinising the associations of perceived risk, trust, UTAUT 3 as well as intention to adopt.

Keywords: banking, financial services, intention, PLS-SEM, UTAUT3

*

Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

166

Reepu and Atul Shiva

Introduction Technology advancements have altogether changed the way our routinized tasks are handled (Panetta F, 2018). These technologies have been creating impact over the past years across all industries. A feather on the cap of such development is the proliferation of such technological progressions in financial markets. (Shin, 2009) These progressions have introduced changes in banking models, strengthening the link between financial markets and institutions (Shin, 2009). Consequently, FinTech hyped and has been constantly expanding across financial markets (Navaretti B et al, 2017) FinTech is an association of two words i.e., finance as well as technology (Hu, et al, 2019). On that account, FinTech employs the usage of technology. (Gomber, et al, 2017). FinTech plays a crucial role towards banking industry development. Traditional banking models have been busted and now bank offerings have increased as customers may choose from a wide array of products and that too, at low cost (Berger A.N., 2003). FinTech has been used widely across the globe as countries like UK, China, India and several others are deploying its usage (Kim, Choi, Park & Yeon 2016). FinTech market has grown to $111,240.5 million in 2019, so the CAGR is 7.9% from the year 2015. FinTech has seen immense growth during 2020 as users preferred to preserve social distancing (The Hindu Business Line, 2020). The Market is further anticipated to grow up $191,840.2 million, 2025 with CAGR of 10.2% (The Business Research Company, 2020). FinTech adoption has been extensively examined across various nations Hu Z et al., (2019) examined FinTech adoption of a rural bank by employing the extended Technology Acceptance Model (TAM) which analysed factors in addition to TAM government support, innovativeness, brand image and perceived risk. They employed a structural equation model. Similarly, studies conducted by Jin C. C. et al., (2018), Huei C.T. et al., (2018) employed TAM to understand factors affecting intention in regards to adoption of FinTech. TRA, as well as UTAUT were also used in some studies (Ryu H.S. 2018; Chong T.P. 2019). In India, Financial inclusion, a drive by the Indian government although it provided mixed consequences thrust several Indians to go online. Demonetization further, made several users to use FinTech services. A fear of cyber frauds also existed, bringing several bankers and regulators to work on the same page (Kandpal V. et al, 2019). Appraising FinTech accession, some studies have analysed the Indian industry orientation towards FinTech, their

Applications of FinTech in Banking

167

structure, startups, trend analysis, as the integration of FinTech with IoT (Rajeswari P. et al., 2021; Suseendran G. et al., 2020). However empirical evidence advocates scarce research on FinTech adoption in India during the post lockdown period. This scanty literature designated a gap in understanding the FinTech attributes contributing to FinTech adoption among Indian banking consumers. This accords an opportunity to study about such FinTech attributes. The present research would subsidise the existing literature where more of FinTech structure has been examined. Policymakers may employ this study’s findings to enhance knowledge and attitude that leads to efficacious adoption of FinTech. (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al., 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2023g, 2023h, 2023i, 2023j, 2023k). The academic orientation of the present study has been entrenched from UTAUT-3 advanced by Farooq et al., (2017). Limited research indicates that the model is not ratified for FinTech adoption by Indian banking consumers. Considering the significance of attributes for adoption by corroborating UTAUT-3 regard, the mentioned research questions have been articulated: RQ1: Which aspect of FinTech attributes deals with behavioural intention and actual use by banking consumers? RQ2: Whether actual usage behaviour of banks considered in Indian Banking can be predicted? By responding to the above questions, this study attempts to recognise FinTech attributes and predict the usage of banking consumers by ratifying the conceptual framework based on UTAUT-3. Research Objectives of this study: 1. How FinTech attributes, risk, as trust lead to espousal and actual usage of bank users 2. To predict the behaviour of bank user in the light of important and performance variables in the study. The paper is structured as follows: Section 1 is about the introduction. Section 2 enlightens about the literature review where the conceptual model as well as hypothesis are discussed. Section 3 is about methodology.

168

Reepu and Atul Shiva

Review of Literature Theoretical Framework Diffusion of Innovation Theory expresses that the spread of innovative ideas requires four components and gives an epidemic model of adoption. Theory of Reasoned Action emanated constructs such as behavioural intention, attitude and subjective norm. TAM is regarded as one of the well-built models due to its validated outcomes (Staats H. 2004). Venkatesh in 2003, developed a comprehensive model which has a wide array of applications i.e., UTAUT, which further was synthesised by Venkatesh et al., (2012). Farooq et al.in 2017, introduced UTAUT-3 which constitutes eight determinants. Authors of UTAUT 3 claimed that this model has 66% explanatory power to predict the espousal of technology. The socio-cognitive theory of trust considers trust as a cognitive phenomenon. The theory set aside the psychological, implicit, and explicit trust aspects. It is rooted in the Belief, Desire and Intention approach. Competence, willingness, and dependence are the elementary beliefs as per this theory. Risk is prevalent in all everyday jobs. Risk is assessed in accordance with the returns that are to be received (Markowitz 1959; Tobin 1958). Risk is a function of utility, too (Pratt 1964). The theory of risk in context to consumers states that there is a fear of unwanted consequences due to current actions (Bauer 1960). This paper investigated the FinTech adoption during the post lockdown time with the help of UTAUT 3 as well as with the use of two additional rudiments, perceived trust as well as perceived risk. It is considered that they influence human behaviour. There are some studies that have examined the relationship of perceived risk as well as trust with the adoption of financebased technology but very few have deliberated on relationships of perceived risk, trust, UTAUT 3 and intention to adopt FinTech.

Conceptual Model and Hypothesis Attributes System attributes are also recognised to affect the use or adopt any new information system. Previous studies also emphasized design attributes

Applications of FinTech in Banking

169

employ instantaneous effect on usage (Davis 1989) due to which subsequently the hypothesis: H1: FinTech attributes positively influence intention to use financial technology.

Behavioural Intention Behavioural Intention refers to the willingness to accept or use technology (Davis 1989) due to which subsequent is the hypothesis: H2: Behavioral Intention positively influences actual usage of FinTech

Perceived Risk Perceived Risk is one of the main encumbrances towards FinTech adoption. Users fear with adverse consequences of using such services (Slade, E. L. et al., 2015). It is contemplated as a negative factor of adoption (Namahoot, K.S.; Laohavichien, T 2018). Privacy and security are always the pain areas of a user when opting for such services (Wang, E.S.-T et al., 2017). Now there are some studies conducted nowadays, that undertake multicomponent notions like performance, physical, time, financial, social, psychological, time etc. (Grob, M 2016; Luo, X et al., 2010). However, for such a study, perceived risk can be defined as the user’s belief that indeterminate circumstances may lead to negative outcomes while adopting FinTech shown in Figure 1.

Figure 1. Proposed Conceptual Model.

Different studies have concluded that perceived risk bears a negative association with intentions to use (Ryu, H 2018; Stewart, H., et al., 2018) and

170

Reepu and Atul Shiva

perceived risk, alongwith perceived trust have a reciprocal influence on user’s intention to use services (Slade, E.L. et al., 2015). Therefore, below mentioned are the hypothesis: H3a: Perceived Risk bears a negative influence towards FinTech usage. H3b: Perceived Risk bears a negative influence on users’ trust towards FinTech usage.

Perceived Trust Perceived trust plays a pivotal role in FinTech as there is inherent uncertainty as well as risk while conducting such transactions. Previous research studies outlined that there are two vital roles of trust i.e., first and foremost trust plays a significant role in confining user behavior. Trust enables higher expectations towards successful transactions (Kim et al., 2008). Trust creates a positive inclination of customers towards different digital services in case of ecommerce (Kim et al., 2008), mobile banking (Sharma S.K. et al, 2019, Zhou, T 2012), internet banking (Namahoot, K.S.; Laohavichien, T 2018) etc. Henceforth, if users feel that they are surrounded by an upright environment. Trust enables user’s confidence in FinTech’s ability, integrity etc. (Stewart, H.; Jürjens, J 2018; Lu, Y.; et al., 2011) Furthermore, switching costs for traditional financial systems is comparatively higher, therefore trust serves as a crucial aspect in relation to financial service offerors (Yang, Q. et al., 2015). Trust, therefore, may lead to reduced risks and rather positive intention towards using such technology (Stewart, H.; Jürjens, J 2018; LiébanaCabanillas, F. et al., 2018). Thus, below mentioned are hypotheses: H4a: Perceived trust positively influences towards intention to FinTech usage H4b: Perceived trust positively influences towards facilitating conditions.

Facilitating Conditions Facilitating conditions requires infrastructure and institutional backing to utilize technology (Venkatesh et al., 2012). Technical backing and substructure are clustered under facilitating conditions (Venkatesh et al., 2012). These affect actual usage as well as the intention to use such services (Venkatesh et al., 2003), thus the following hypothesis are proposed:

Applications of FinTech in Banking

171

H5a: Facilitating conditions positively influence intention to use FinTech H5b: Facilitating conditions positively influence actual usage of FinTech.

Research Methodology This inquest was administered across India. Data was collected from the North, South, East and West zones of India through an online questionnaire during the post-lockdown from Sep to Dec 2020. Non-probability method is purposive sampling is applied for data collection purposes with the help of a questionnaire as attached through Appendix-1. The model constitutes a construct of FinTech attributes which is second-order construct appraised through formative modelling. The PLS-SEM in SmartPLS is reckoned to be supple enough for such intricately designed models; henceforth, it is an extensively acknowledged multivariate technique (Hair et al., 2020). The research was conducted through: (1) prescribed model and hypothesis testing (2) collection of data (3) validation (4) appraising the results. The existing study is cross-sectional. A power of 0.95 has been attained via G*Power Software through the least sample size of 89 respondents (as reported in the figure below) but the study employed a sample size of 265 so it sufficed the minimum set of respondents required. Further, Harman’s single factor analysis is appertained to scrutinize if any Common Method Bias prevails. So, all the undertaken statements were chock-a-block to sole factor and it disclosed a 34.007% variance, which is lesser than the 50% variance upper limit suggestive in itself that there prevails no common method bias (Babin et al., 2016).

Results Descriptive information about this research is presented in the following Table 1. The FinTech users were enthusiastically embracing its use. Male respondents were more than that of females as they constituted 61 per cent of the sample. The sample constituted 11 per cent of professionals, 55 per cent of postgraduate and 32 per cent of graduates. Further sample had responses from all the zones of the country. Demographics suggested that well-educated

172

Reepu and Atul Shiva

(55.8%), and young (70.5%) were intending FinTech employment. Thus, the sample had a true picture of the population. Table 1. Sample Size: 265 Age

Frequency

%

< 30 30-39 > 40

187 49 29 265

70.5 18.4 10.9 100

161 104 265

60.7 39.2 100

100 120 45

37.7 45.2 16.9

187 78 265 Source: Author’s Calculations.

70.5 29.4 100

Gender Male Female

Income Below 30000 p.m. 30000 – 50000 p.m. Above 50000 p.m. Marital Status Married Unmarried

Educational Qualification Graduate Post Graduate Professional Zone North East South West

Frequency

%

87 148 30 265

32.8 55.8 11.32 100

93 27 40 105 265

35 10 15 40 100

MM Assessments The indicator loadings evaluations are reported in below Table 2. Variables possessing loadings above 0.50 are advocated. Convergent validity was entrenched through AVE which was found to be greater than 0.50 as shown in Table 2. The value of Composite Reliability outdoes the criteria of 0.5. Henceforth, there was no overlay between the measures employed (Hair et al., 2016). An examination of discriminant validity was conducted through Fornell and Larcker’s norm (1981). The Table 3 suggests that each existing construct is distinct from the other and therefore, the study is fit in order to pursue the final analysis. Along with the conventional method of examining discriminant validity a contemporary yardstick named the Heterotrait-Monotrait ratio of correlations is also employed. The Table 4 stated benchmark is that all HTMT values need to be less than 1, a recommendation of the HTMTinterface technique,

Applications of FinTech in Banking

173

nevertheless onto a firm note HTMT ratio of correlations with a max of 0.85 (Henseler et al., 2015; Voorhees et al., 2016) and 0.9 admissible value (Gold et al., 2001). Table 2. Cross Loading

Cross Loading

Factor Loading

AU1 0.844 AU2 0.899 AU3 0.84 BI1 0.804 Behavioural BI2 0.642 Intention BI3 0.811 DS1 0.771 DS2 0.569 DS3 0.794 Data Security DS4 0.772 DS5 0.681 DS6 0.765 FC1 0.881 Facilitating Conditions FC2 0.871 Risk1 0.809 Perceived Risk2 0.885 Risk Risk3 0.75 Trust 1 0.757 Trust 2 0.782 Perceived Trust 3 0.849 Trust Trust 4 0.757 Trust 5 0.756 UD1 0.755 UD2 0.476 UD3 0.761 User Design UD4 0.737 UD5 0.754 UD6 0.622 VA2 0.789 Value Addition VD1 0.875 Source: Author’s Calculations. Actual Usage

Average Variance Extracted (AVE)

Composite Reliability

rho_A

Cronbach’s Alpha

0.742

0.896

0.832

0.826

0.572

0.799

0.651

0.626

0.533

0.871

0.832

0.821

0.767

0.868

0.698

0.697

0.666

0.856

0.787

0.755

0.61

0.886

0.841

0.84

0.53

0.849

0.782

0.776

0.694

0.819

0.585

0.565

Table 3. Discriminant Validity (Fornell and Larcker, 1981) Cross Loading Fornell and Larcker Criterion Actual Use _of FinTech Beh_Int Data _Security Facilitating_Conditions Per_Risk Per_Trust User_Design Value_Addition Source: Author’s Calculations.

Actual Use _of FinTech 0.861 0.515 0.500 0.528 0.222 0.575 0.438 0.327

Beh_Int

0.756 0.683 0.597 0.226 0.529 0.556 0.382

Data _ Security

Facilitating_ Conditions

0.730 0.554 0.406 0.582 0.590 0.397

0.876 0.201 0.488 0.570 0.358

Per_Risk

Per_Trust

User_Design

Value_Addition

0.816 0.454 0.253 0.198

0.781 0.578 0.394

0.728 0.608

0.833

Table 4. HTMT Ratio of Correlations for Discriminant Validity Assessments HTMT Ratio Beh_Int Data _Security Facilitating_Conditions Per_Risk Per_Trust User_Design Value_Addition Source: Author’s Calculations.

Actual Use _of FinTech 0.708 0.611 0.693 0.272 0.689 0.547 0.474

Beh_Int

Data _Security

Facilitating_Conditions

Per_Risk

Per_Trust

User_Design

0.927 0.900 0.334 0.722 0.79 0.603

0.731 0.472 0.702 0.732 0.568

0.252 0.635 0.770 0.568

0.555 0.315 0.280

0.716 0.570

0.909

Figure 2. Structural Model Assessments.

Table 5. Hypothesis Testing Hypothesis Testing Beh_Int -> Actual Use _of FinTech FINTECH_Attributes ->Beh_Int Facilitating_Conditions -> Actual Use _of FinTech Facilitating_Conditions ->Beh_Int Per_Risk ->Beh_Int Per_Risk ->Per_Trust Per_Trust ->Beh_Int Per_Trust ->Facilitating_Conditions Source: Author’s Calculations.

O 0.311 0.526

M 0.312 0.527

STDEV 0.069 0.068

|O/STDEV| 4.496*** 7.763***

P-Values 0.000 0.000

2.50% 0.174 0.383

97.50% 0.446 0.653

0.342

0.34

0.066

5.21***

0.000

0.205

0.464

0.001 0.084 0.000 0.081 0.000

0.106 -0.181 0.34 -0.012 0.338

0.378 0.005 0.551 0.253 0.609

0.234 -0.082 0.454 0.116 0.488

0.233 -0.079 0.456 0.116 0.488

0.07 0.048 0.054 0.067 0.069

***

3.343 1.731* 8.473*** 1.743* 7.074***

Applications of FinTech in Banking

177

Structural Model Assessments Subsequent is the assessment of model coefficients. In our study, values range from 1 to 2.197, indicating no collinearity issues prevail. The coefficient of determination, R2 value was also investigated. The R2 value assesses the variance of each endogenous construct, enlightened through explanatory variables. Values in the circles above depict the coefficient of determination. The value of R2 is 0.341 for actual use of FinTech and 0.533 for behavioural intention to use FinTech. The Figure 2 above arrow lines are partial regression coefficients amid constructs and amid constructs and the calculated qualitative as well as quantitative variables. Hypothesis Testing Figure 2 is a description of hypothesized outcomes from the study along with the study variables as well as their relationships. Table 5 shows a direct relationship among the variables. Behavioural intention positively influences actual usage of FinTech as O = 0.311, p < 0.001 thus proving H2. It is also disclosed that FinTech attributes may lead to behavioural intention towards FinTech use as O = 0.526, p < 0.001, thereby proving H1. Facilitating conditions positively influences actual use of FinTech as O = 0.342, p < 0.001thus proving the H5b. Facilitating conditions bear a positive relationship with behavioural intention towards usage FinTech as O = 0.234, p < 0.01, thus proving H5a.

Conclusion In regard to the rising adoption of technology in banking, the present paper presents FinTech adoption. This existing research empirically addresses the literature gap. On the basis of extant literature, a unified conceptual framework was proposed and tested which incorporated perceived risk, perceived trust as well as FinTech attributes. The findings revealed that six associations of the proposed model tend to determine technology adoption in the existing milieu. Overall, the structural model has R square 34 per cent to investigate influencers. Results confirmed that intention encourages actual usage, and likely even the FinTech attributes appeared as another key driver that stimulated actual use. The behavioural intention towards adoption is not influenced by perceived risk as well as perceived trust. Moreover, the system

178

Reepu and Atul Shiva

designers may work towards enhancing the usage by taking into consideration the factors investigated.

Disclaimer None.

References Bansal, N., Bhatnagar, M., & Taneja, S. (2023i). Balancing Priorities Through Green Optimism: A Study Elucidating Initiatives, Approaches, and Strategies for Sustainable Supply Chain Management. In Handbook of Research on Designing Sustainable Supply Chains to Achieve a Circular Economy (pp. 60-81). IGI Global. https://doi.org/10.4018/978-1-6684-7664-2.ch004. Berger, A. N. (2003). The economic effects of technological progress: evidence from the banking industry. Journal of Money, Credit and Banking, 35(2), 141-176. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/10. 3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Chong T. P., Yen-San, Yip, Pui-Yee, Chan 92019). An adoption of FinTech service in Malaysia, South East Asia Journal of Contemporary Business, Economics and Law, 18(5), 25-35. https://seajbel.com/wp-content/uploads/2019/05/seajbel5-VOL18_241. pdf. Dahiya, K., Taneja, S., & Özen, E. (2023g). To Analyse the Impact of Multi-Media Technology on the Rural Entrepreneurship Development. In Contemporary Studies of Risks in Emerging Technology, Part A (pp. 221-240). Emerald Publishing Limited. 10.1108/978-1-80455-562-020231015. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962.

Applications of FinTech in Banking

179

Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/10. 18280/ijsdp.170802. Davis, F. D. (1986) Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly. 13 (3), 318 – 330. Gomber, P., Koch, J. A., & Siering, M. (2017). Digital Finance and FinTech: Current research and future research directions. Journal of Business Economics, 87(5), 537580. https://doi.org/10.1007/s11573-017-0852-x. Grob, M. Impediments to mobile shopping continued usage intention: A trust-riskrelationship. J. Retail. Consum. Serv. 2016, 33, 109–119. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Hu, Z., Ding, Z., Li, S., Chen, L., & Yang, S. (2019). Adoption Intention of FinTech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model. Symmetry, 11(3), 1-16. https://doi.org/10.3390/sym11030340. Jangir, K., Sharma, V., Taneja, S., & Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Jin C. C. (2018), Factors affecting the consumer acceptance towards FinTech products and services in Malaysia, International Journal of Asian Social Science, Vol. 9, No. 1, 5965. Kandpal V. and Mehrotra R. Financial Inclusion: The role of FinTech and digital financial services in India, Indian Journal of Economics & Business, Vol. 19, No.1 (2019) : 8593. Kim, D. J.; Ferrin, D. L.; Rao, H. R. A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Dec. Supp. Syst. 2008,44, 544–564. Kim, Y., Choi, J., Park, Y., & Yeon, J. (2016). The adoption of mobile payment services for “FinTech.” International Journal of Applied Engineering Research, 11(2), 10581061. Kumar, P., Taneja, S., Özen, E., & Singh, S. (2023h). Artificial Intelligence and Machine Learning in Insurance: A Bibliometric Analysis. In Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (pp. 191-202). Emerald Publishing Limited. https://doi.org/10.1108/S156937592023000110A010. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ijsdp.180204. Liébana-Cabanillas, F.; Muñoz-Leiva, F.; Sánchez-Fernández, J. A global approach to the analysis of user behavior in mobile payment systems in the new electronic environment. Serv. Bus. 2018,12,25–64.

180

Reepu and Atul Shiva

Lu, Y.; Yang, S.; Chau, P. Y. K.; Cao, Y. Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Inf. Manag. 2011, 48, 393–403. Luo, X.; Li, H.; Zhang, J.; Shim, J. P. Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Dec. Supp. Syst. 2010, 49, 222–234. Namahoot, K. S.; Laohavichien, T. Assessing the intentions to use internet banking: The role of perceived risk and trust as mediating factors. Int. J. Bank Mark. 2018, 36, 256– 276. Navaretti G. B., Calzolari, G. & Pozzolo, A. F. FinTech and Banks: Friends or Foes?, http://european-economy.eu/wp-content/uploads/2017/07/EE_2.2017.pdf#page=11. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic.org/ index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/en/pub/ yalovasosbil/issue/72655/1143214. Panetta F, 2018, FinTech and banking: today and tomorrow, https://www.bancaditalia.it/ pubblicazioni/interventi-direttorio/int-dir-2018/panetta-120518.pdf. Rajeswari P. & Vijai, C. FinTech Industry In India: The Revolutionized Finance Sector, European Journal of Molecular & Clinical Medicine, ISSN 2515-8260, Volume 7, Issue 11, 2020. Reepu, R., Taneja, S., Ozen, E., & Singh, A. (2023j). A Globetrotter to the Future of Marketing: Metaverse. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 1-11). IGI Global. https://doi.org/10.4018/978-1-6684-8312-1. ch001. Ryu H. S. Understanding Benefit and Risk Framework of FinTech Adoption: Comparison of Early Adopters and Late Adopters, Proceedings of the 51st Hawaii International Conference on System Sciences 2018. Ryu, H. What makes users willing or hesitant to use FinTech? The moderating effect of user type. Ind. Manag. Data Syst. 2018, 118, 541–569. Sharma, S. K.; Sharma, M. Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. Int. J. Inf. Manag. 2019, 44, 65–75. Shin, H. S. (2009). Reflections on Northern Rock: The bank run that heralded the global financial crisis. Journal of Economic Perspectives, 23 (1), 101-119. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies:: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Slade, E. L.; Dwivedi, Y. K.; Piercy, N. C.; Williams, M. D. Modeling consumers’ adoption intentions of remote mobile payments in the United Kingdom: Extending UTAUT with innovativeness, risk, and trust. Psychol. Mark. 2015, 32, 860–873.

Applications of FinTech in Banking

181

Staats H. Pro-environmental attitudes and behavioral change. Encyclopedia of Applied Psychology. 2004, pp 127 – 135. Stewart, H.; Jürjens, J. Data security and consumer trust in FinTech innovation in Germany. Inf. Comput. Secur. 2018, 26, 109–128. Suseendran G. Banking and FinTech (Financial Technology) Embraced with IoT Device, Springer Nature Singapore Pte Ltd. 2020. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Gupta, M., Bhushan, P., Bhatnagar, M., & Singh, A. (2023k). Cultural Marketing in the Digital Era. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 109-122). IGI Global. 10.4018/978-1-6684-8312-1.ch008. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/10. 18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307. Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. (2003), “User acceptance of information technology: toward a unified view,” MIS Quarterly, Vol.27No.3, pp.425478. Venkatesh, V., Thong, J. Y. L. and Xu, X. (2012), “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology,” MIS Quarterly, Vol. 12No.1,pp.157-178. Wang, E. S.-T.; Lin, R. L. Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behav. Inf. Tech. 2017, 36, 2– 10. Yang, Q.; Pang, C.; Liu, L.; Yen, D.C.; Tarn, J.M. Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Comput. Hum. Behav. 2015, 50, 9–24. Zhou, T. Understanding users’ initial trust in mobile banking: An elaboration likelihood perspective. Comput. Hum. Behav. 2012, 28, 1518–1525.

Chapter 11

Machine Learning Algorithms to Accelerate the Development of Business Analytics Renu Vij* Associate Professor, USB, AIT-Management, Chandigarh University, Mohali, Punjab, India

Abstract Nowadays, AIML-tool is one of the industry’s best and fastest growing. And AIML is one of the best competitors in the industry also. So many developments are implemented through the AIML. Analytics experts are needed. Accessible machine learning frameworks may help the public learn. AutoML provides completely automated model selection and hyperparameter tweaking solutions. This research assessed AutoML’s business analytics capabilities. This application might increase machine learning use across many sectors. We compared H2O AutoML’s performance, robustness, and reliability on three real-world datasets to a manually customised stacking machine learning model. The manually tweaked machine-learning model outperformed all three case studies. H2O AutoML still did well. It’s quick, simple, and accurate, like a customised machine learning model. H2O AutoML’s present configuration may accelerate development and deployment. It might reduce the machine learning talent gap and automate business analytics choices. Significance: The study highlights digital strategies for effective decision-making to boost growth and reduce the talent gap.

Keywords: auto machine learning, performance, decision-making



Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

184

Renu Vij

Introduction We compare the outcomes to determine whether Auto ML outperforms manually trained ML models. All organizations are considering the recent ML models, which are valid or training datasets, but these training data sets still need to be improved in organizations. Auto ML and other business analytics technologies may become strong decision engines. It might boost growth and reduce the talent gap. It is a standard process compared to the existing manually constructed machine-learning model to assess its prediction accuracy, robustness, and ease. This data will also be tested for digital strategy management implications. Finally, research will be directed. The goal is to extend the discourse to spark new conversations and encourage more academics to apply ML models in business processes.

Methodology Implemented Auto Machine Learning Auto ML automates predictive analytics. The Auto ML response may need preprocessing, feature engineering, and model adjustments. Auto ML systems must efficiently preprocess data. Here we optimize hyperparameters and choose models. H2O AutoML is a leading AutoML utility. Classification and regression benchmark tests have shown good results. Figure 1. In the initial stage, the H2o Auto ML is the well-generated structure for the various models like the Random forecasting technique, Gradient boosting method, and Deep learning tool. These models are well structured by the two different training and tested data sets. Important parameters for the Auto ML solution are training and testing

dataset frame. Organization needs more time to optimize the available data sets for choosing the training datasets and optimizing the time for the accuracy of the result. This work refers to the random search optimization method. Algorithm:1- Meta-machine learning algorithm- Linear regression, Random forecast, Deep free forward neural network. Input: Upload the different data sets. Step-1: Train all the datasets

Machine Learning Algorithms …

185

Step 2: Consider all pre-data sets to develop the best classifier 1 Step 3: Consider all pre-data sets to build the best classifier 1 Step 4: Repeat the 1,2,3 step until the higher models reach Output: runtime, in decreasing order, depending on the accuracy of their predictions on the test dataset.

Figure 1. The H2O AutoML system later combines many basic learners with two super learners.

Implementation Process This empirical study’s main objective is to evaluate, using real-time developed datasets from the industries’ credit risk factors, insurance-related claims, and

186

Renu Vij

marketing issues, the performance, robustness, and reliability of the H2O AutoML model and that of manually trained learning datasets.

Data Set Description This experiment uses three datasets that are freely accessible to the public to make it easier to reproduce and compare the results. The prepared datasets are available on a website to support the machine learning algorithm. All the data sets are compared concerning the characteristics of the fast data analysis features like 16, 23 and 57. The features predict variables to classify the data sets according to their category. Observe the categorized datasets to find the feature prediction. Every dataset column is named a binary reaction; it indicates the claims of the customers. Whether customer payment is made, if the customer claimed any insurance or whether the market can lead this much effort to sake the product. Table 1 is a rundown of the key takeaways from the various datasets and case studies utilised in this research. Table 1. Data set

Credit Claims Marketing

Total 34000 602592 51282

Y=0 25578 599325 41256

Y=1 8422 3267 10026

Remaining 8422/8422 3267/3267 10026/10026

Feature 32 64 18

Credit Risk: The initial dataset comes from the credit risk factor-related domain and comprises payment corresponding issues details provided by customers in Asia who use credit cards. There are 34,000 observations included in the whole dataset. Positive instances account for 23,364 of them, whereas negative cases, in which the customer failed to meet the terms of the agreement, account for 8422 of them. The observations include 35 characteristics, one of which is a response column that stores information on whether the binary value is default or non-default. This dataset contains a variety of attributes, some of which include previous payment information and basic information such as the age and gender of the person, marital status of the concerned person, and education qualification. (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al., 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a,

Machine Learning Algorithms …

187

2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2023g, 2023h, 2023i, 2023j, 2023k). The second data collection comes from the realm of insurance and contains information about people with motor insurance policies. This data was used to make predictions regarding claims. It is comprised of a total of 602592 observations. Of those observations, 599325 do not represent claims that have been filed, while 3267 represent claims that have been filed. The statements include 64 attributes, one of which is a response column detailing each policyholder’s claim process. Data about marketing and sales are included in the third set of data. It gives customer-related information to support the marketing campaign in the financial-related service industry. The dataset contains an entire number of 51282 observations taken from various sources. Thirty-nine thousand nine hundred twenty-two attempts failed, and 10026 attempts were successful. To be successful, anything must lead to a conversion or a completed sale. The observations include a response column indicating whether or not the marketing campaign successfully resulted in a final conversion or sale. Each comment also includes a total of 18 attributes.

Data Processing Random under-sampling: It needs to balance positive and negative observations; otherwise, it will need to pull or push compliance towards the comments. The training data sets are with a ratio of 85:15. Random undersampling: - All the results are developed from the basic theory, that is, prediction accuracy; it may not be the first point of the classifier, but it will affect all other data sets. Under-sampling at random is used to accomplish this goal. Eliminating observations from classes in which most members are present is one method for recalibrating the datasets and bringing them into a equilibrium condition. The loss of some information that occurs due to undersampling may be disregarded for this comparison since the primary objective is to evaluate AutoML compared to the manually configured super learner ensemble. An additional option would be to conduct a larger sample size of the minority group; however, doing so would cause the dataset to become unmanageably large and lengthen the time required for training. For large datasets, this would be a severe issue. It must also encode the categorical data, a step in the preprocessing phase necessary for AI and ML models. Models in AI/ML need numeric input

188

Renu Vij

variables. Before model fitting and assessment can begin, convert the categorical data need into a numerical representation of themselves, often accomplished via one-hot encoding (or ordinal encoding if a rank already exists). One hot explicit is the name of a parameter setting that can be found in H2O. This setting generates N+1 additional columns for categorical features that have N levels. Training/Test split: The 75:25 division was selected, which indicates that 80% of the used dataset will be in the training phase, and the remaining 25% of the dataset will be used to test the trained classifier’s capacity to generalise its findings. When it comes time to combine the essential learners into the meta-learner subsequently, you will need to use the same cross-validation configuration. As a result, eighty per cent of the dataset will be segmented into several training and validation sets to facilitate the process of cross-validation while the model is being trained.

Data Evaluation Process The basic Linear Model (LR), Random Forest technique, Gradient Boosting Model, and Deep Learning in Feedforward Neural Networks are some models that H2O’s AutoML solution trains internally. The stacking ensemble method, which merges all pre-trained candidate models into a single super learner, is used in the second stage. The highest-performing model is chosen automatically after a series of tests are run on various alternatives. I could assess the configuration’s effectiveness by reproducing the H2O AutoML solution’s inner workings. I did this by training the basic models by hand and stacking them to form a super learner. We’re looking at two super learners, each with its own parameters and contrasting them. One is modified and adapted by humans, while the other is generated mechanically using H2O AutoML solutions.

Tools Used R-Studio, the IDE for the statistical programming language R, is utilised throughout the data analysis process, from preparation through model fitting and evaluation. R is one of the most critical languages for research and development in computational statistics, a subfield of data science and machine learning. It’s no secret that R is a significant player in the

Machine Learning Algorithms …

189

programming world. By helping the R programming H2O, we will develop the AutoML structure, and all of the models are experimental, including RF, GBM and Deep learning. A free source of Java’s H2O version in machine learning is free. Assorted prediction models are among those it can accommodate. H2O’s speed comes from its ability to quickly scale from a small, desktop-based setup to a much larger one. This greatly facilitates the processing of large data sets and increases productivity. A REST API allows R-studio to communicate with H2O.

Result The experiment’s findings are discussed in this article. Here, we examine the differences between a stacked ensemble learner and H2O’s AutoML solution for a single dataset. When using H2O AutoML, many independent base classifiers must be trained and included in a stacked ensemble model. The H2O AutoML technique is a stacking simulator. Our theories were evaluated across three real-world applications: credit risk, insurance claims, and advertising. Evaluate the H2O AutoML solution compared to a manually constructed super learner using the four above criteria. Accuracy and F-scores must exceed 0.5 to be reported. To verify this theory: Training for RF, GBM, and deep learning was initially quite rigorous. During training, tweaked the default hyperparameter values of the primary models via tried-and-true methods, including hand-tweaking, grid searching, and random searching across a fixed range. Table 2 displays the numerical results of the base classifiers for each dataset. Determined that Gradient Boosting (GB) has the highest overall performance, followed closely by Random Forest (RF). No matter whether the dataset is examined, the performance results for Deep Learning are the poorest. The second step included stacking candidate models with a super learner. This strategy improves base classifiers asymptotically optimally. Every case study’s “super learner” combines RF, GBM, and DL features. Each case study might achieve the best performance by employing RF, GBM, and DL as inputs for the super learner, as determined by evaluating the three feasible baseline model combinations for the stacked ensemble. That is only sometimes true.

190

Renu Vij

Table 2. Result- primary classifier to each data set Case Credit risk

Method RF

Claim prediction

GB

Marketing

Deep learning

AUC 0.642 0.754 0.962 0.732 0.632 0.932 0.732 0.922 0.823

Accuracy 0.719 0.621 0.873 0.723 0.586 0.879 0.723 0.832 0.853

F score 0.692 0.573 0.774 0.553 0.589 0.529 0.686 0.821 0.862

Log loss 0.582 0.632 0.232 0.532 0.669 0.321 0.529 0.212 0.311

The entire developed structure employs the learning datasets in step two as a benchmark; the benchmark evaluates the system’s overall reliability and robust performance. Table 3 represents the results of the developed system H2O AutoML. Table 3. Compare the super learner benchmark model and AutoML Case

Method

AUC

Accuracy

F score

Credit risk

Stacked Ensemble Auto ML Stacked Ensemble Auto ML Stacked Ensemble Auto ML

0.759

0.623

0.578

Log loss 0.626

0.784 0.635

0.618 0.596

0.575 0.596

0.628 0.667

0.640 0.927

0.588 0.834

0.594 0.825

0.669 0.212

0.942

0.539

0.827

0.210

Claimprediction

Marketing

The findings are remarkably similar across the board, as the stacked Super Learner beat the AutoML model on each of the three datasets by a margin of 0.003 in terms of the area under the curve (AUC). Although presentation deltas for the other related matrices are different, the stacked ensemble did a better job than the AutoML solution in most of these situations. The difference is 0.004 for the Accuracy metric, 0.004 for the F-score, and 0.003 for the Log Loss metric when considering the credit risk

Machine Learning Algorithms …

191

case study. In the instance of the insurance dataset, the difference in performance for The exactness is 0.005, the difference for F- score is 0.003, and the difference for LogLoss is 0.002. The differences in performance between the two versions of the marketing case study are as follows: • • •

The Accuracy is 0.002 points lower. The F-score is 0.003 points lower. The log loss is 0.002 points lower.

Regarding Accuracy and F-score, AutoML had a slightly better performance than the stacked ensemble regarding the marketing case study. The manually adjusted stacked ensemble performs more effectively in all three case studies than in the AutoML approach, regardless of whatever case study you look at.

Discussion This paper’s experimental research compared the H2O AutoML framework to a manually created ML model using the four evaluation criteria of AUC value, Accuracy prediction, F-score value, and Log Loss. These are the three elements that make up this section: First, we’ll review the study’s findings to evaluate the effectiveness of the tried-and-true AutoML method. Second, will examine the significance of the results for managers, practitioners, and researchers in business analytics. Finally, a plan is laid up for ongoing investigations. At the end of the empirical results based on real data sets from the credit risk factor, insurance factor and marketing-related issues are represented in H2O AutoML models; these cannot reach the trained data sets. It has problems attaining the quality of a manual\setup in two ways: There was a discrepancy between the prediction accuracy of the underlying models (based classifiers) and the manually modified versions. Running it longer didn’t make much of a difference in the end outcome, and it didn’t get much better. The H2O AutoML program selects two different stacked ensembles. Both use the best-trained model for each category, but one uses all of the models. It needs to check whether a different collection of candidate models (for example, a narrower one) will provide better results, which is crucial since the

192

Renu Vij

performance of the stacked ensemble could improve when weaker models are added to the overall pool of models. Super learners should only use the most accurate baseline models available since adding more classifiers might dilute performance by including less relevant data. Despite this little performance gap, H2O’s AutoML solution is a powerful model-tuning engine that can significantly speed up prototyping and enable practitioners unfamiliar with ML ideas to create a robust model. Nonetheless, a data scientist’s meticulous model tuning and tweaks to hyperparameters provide optimal performance, allowing for the highest prediction accuracy. Although the three case studies show that manual modification provides a minor advantage, it is still being determined whether this benefit justifies the time-consuming model development approach. The answer to this issue is very context-specific and hinges on whether or not the marginal gain in performance is worth the effort put into manual model tweaking. The outstanding performance of the AutoML solution created by H2O demonstrates that with further research, prediction accuracy levels will match those of models modified by ML experts. All in all, AutoML is a significant first step toward comprehensive endto-end decision-making. AutoML has great potential to help human engineers in the long run because of its relatively excellent performance and reliable outcomes. This would be a massive boon to the accessibility of ML for Business Analytics operations, particularly for SMEs that struggle to find and retain qualified personnel. Data has long been a tool of choice for managers seeking new perspectives and understanding. Specifically, by use of computerised B.I.S. Obviously, this is nothing new. Evidence-based or data-driven decision-making has replaced the more intuitive corporate approach of the past. An A.I./ML-enabled setting is essential for this novel mode of decision-making. Although AutoML is a significant first step, further development and expansion may lead to a completely automated decision engine. By making ML solutions more accessible, it might level the playing field for businesses of all sizes and in all sectors. While this study’s results show that manual engineering is still superior for model tweaking, AutoML may assist in addressing the skill gap and encourage the widespread use of ML solutions. Additionally, it offers qualified data scientists quick benchmarking and prototyping, which might save development times and result in early deployment. The findings of this study give hopeful evidence that, as a consequence of continued innovation in the area and improvements in

Machine Learning Algorithms …

193

hardware, software, APIs, and user interfaces, AI/ML systems will eventually become less costly and more user-friendly. As these trends continue, competence in machine learning may become less crucial for developing and implementing end-to-end AI solutions than domain knowledge and subject matter expertise. While it is impossible to commoditize domain knowledge, machine learning (ML) as a decisionmaking tool, in general, may and will be commoditised. Although the rate at which AI/ML solutions become commodities is hard to foretell, this trend is already seen in the real world. Cloud computing giants like Amazon Web Services (AWS), Google Cloud, and Microsoft Azure are hard at perfecting their artificial intelligence and machine learning (AI/ML) systems, which can be easily acquired via the SaaS model. Unfortunately, one of the most labor-intensive aspects of data science— preprocessing large datasets—cannot yet be accomplished automatically using AutoML. Prescriptive analytics, which provides actual, doable next moves based on predicted results, is similarly necessary. Companies now rely on data science specialists or external consultants to assist in driving their digital transformation activities until the last stages towards a comprehensive end-toend process are addressed.

Conclusion Machine learning and artificial intelligence experts are in high demand due to the ongoing digitization of our global economy. However, despite their apparent benefits, more qualified workers have been needed for the widespread use of AI and ML techniques in corporate analytics. To help alleviate this shortage of manpower and speed up the predictive analytics process, AutoML frameworks are being developed. In its present form, the H2O AutoML system needs to improve the prediction accuracy that can be achieved with human intervention. Despite such results, the research demonstrated that AutoML is a valuable tool. For one, it may serve as a prototype for specialists in machine learning, speeding up the iteration and rollout phases of ML initiatives. There are two main ways this helps make ML models more approachable for those who aren’t experts in the field: it improves usability and enhances the degree of abstraction. The number three is a critical milestone in creating a complete business analytics decision engine.

194

Renu Vij

References Ajah, I. A.; Nweke, H. F. Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications. Big Data Cogn. Comput. 2019, 3, 32. https://doi.org/ 10.3390/bdcc3020032. Arafat Salih Aydiner, Ekrem Tatoglu, Erkan Bayraktar, Selim Zaim, Dursun Delen, Business analytics and firm performance: The mediating role of business process performance, Journal of Business Research, Volume 96, 2019, Pages 228-237, ISSN 0148-2963, https://doi.org/10.1016/j.jbusres.2018.11.028.(https://www. Science direct.com/science/article/pii/S0148296318305800). Bansal, N., Bhatnagar, M., & Taneja, S. (2023i). Balancing Priorities Through Green Optimism: A Study Elucidating Initiatives, Approaches, and Strategies for Sustainable Supply Chain Management. In Handbook of Research on Designing Sustainable Supply Chains to Achieve a Circular Economy (pp. 60-81). IGI Global. https://doi.org/10.4018/978-1-6684-7664-2.ch004. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/ 10.3390/risks10110209. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Cadden, Trevor, Denis Dennehy, Matti Mantymaki, Raymond Treacy. (2022) Understanding the influential and mediating role of cultural enablers of AI integration to supply chain. International Journal of Production Research 60:14, pages 45924620. Dahiya, K., Taneja, S., & Özen, E. (2023g). To Analyse the Impact of Multi-Media Technology on the Rural Entrepreneurship Development. In Contemporary Studies of Risks in Emerging Technology, Part A (pp. 221-240). Emerald Publishing Limited. 10.1108/978-1-80455-562-020231015. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962 Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi.org/ 10.18280/ijsdp.170802.

Machine Learning Algorithms …

195

Dash, S., Shakyawar, S.K., Sharma, M. & Kaushik, S. Big data in healthcare: management, analysis and future prospects. J Big Data 6, 54 (2019). https://doi.org/10.1186/s40537019-0217-0 Ghimire, A., S. Thapa, A. K. Jha, S. Adhikari and A. Kumar, “Accelerating Business Growth with Big Data and Artificial Intelligence,” 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2020, pp. 441-448, https://doi.org/10.1109/I-SMAC49090. 2020. 9243318. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)? Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Kumar, P., Taneja, S., Özen, E., & Singh, S. (2023h). Artificial Intelligence and Machine Learning in Insurance: A Bibliometric Analysis. In Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (pp. 191-202). Emerald Publishing Limited. https://doi.org/10.1108/S156937592023000110A010. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ijsdp.180204 Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R. & Muharemagic, E. Deep learning applications and challenges in big data analytics. Journal of Big Data 2, 1 (2015). https://doi.org/10.1186/s40537-014-0007-7. Özen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebi-academic.org/ index.php/jebi/article/view/41. Özen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies : a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/ en/pub/yalovasosbil/issue/72655/1143214. Pappas, Ilias O. “Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies.” Information Systems and e-Business Management 16 (2018): 479-491. Power, Daniel J., Ciara Heavin, Yvonne O’Connor. (2021) Balancing privacy rights and surveillance analytics: a decision process guide. Journal of Business Analytics 4:2, pages 155-170. Reepu, R., Taneja, S., Ozen, E., & Singh, A. (2023j). A Globetrotter to the Future of Marketing: Metaverse. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 1-11). IGI Global. https://doi.org/10.4018/978-1-6684-83121.ch001.

196

Renu Vij

Sarker, I. H. Machine Learning: Algorithms, Real-World Applications and Research Directions. SN COMPUT. SCI. 2, 160 (2021). https://doi.org/10.1007/s42979-02100592-x. Schmitt, Marc, Deep learning in business analytics: A clash of expectations and reality, International Journal of Information Management Data Insights, Volume 3, Issue 1, 2023, 100146, ISSN 2667-0968, https://doi.org/10.1016/j.jjimei.2022.100146. (https://www.sciencedirect.com/science/article/pii/S2667096822000891). Sincorá, L. A., Oliveira, M. P. V. d., Zanquetto-Filho, H. and Ladeira, M. B. (2018), “Business analytics leveraging resilience in organizational processes,” RAUSP Management Journal, Vol. 53 No. 3, pp. 385-403. https://doi.org/10.1108/RAUSP04-2018-002. Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies:: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Soldic Aleksic, Jasna & Chroneos Krasavac, Biljana & Karamata, Ema. (2019). Business analytics: new concepts and trends. Management: Journal of Sustainable Business and Management Solutions in Emerging Economies. 25. 10.7595/management. fon. 2019.0013. Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India ‘ s Total Natural Resource Rents (NRR) and GDP : An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Gupta, M., Bhushan, P., Bhatnagar, M., & Singh, A. (2023k). Cultural Marketing in the Digital Era. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 109-122). IGI Global. 10.4018/978-1-6684-8312-1.ch008. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10.18280/ijsdp.180307.

Machine Learning Algorithms …

197

World Bank Group. 2016. Business Analytics Toolkit for Tech Hubs : Lessons Learned from infoDev’s mLabs and mHubs. World Bank, Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/24778 License: CC BY 3.0 IGO. Zhao, Yifan. (2021). Transformation of Business Analytics from Business Intelligence. E3S Web of Conferences. 253. 03013. 10.1051/e3sconf/202125303013.

Chapter 12

The Role of Business Intelligence in the Financial Sector Sanjay Taneja1,* Neha Bansal2 and Ercan Ozen3,† 1Department

of Management Studies, Graphic Era University, Dehradun(Uttarakhand), India 2University School of Business, Chandigarh University, Mohali (Punjab), India 3Department of Finance and Banking, Faculty of Applied Sciences, Usak University, Turkey

Abstract The financial sector is a data-intensive industry that regularly produces enormous volumes of data. Financial institutions now rely heavily on business intelligence (BI) to make data-driven decisions, increase operational effectiveness, and accomplish their corporate objectives. The development of BI in the financial industry has been influenced by technological advancements, shifting business requirements, and escalating rivalry. This chapter has discussed the role of BI in the financial sector, including the advantages of BI for financial institutions, the challenges of implementing BI in the financial sector, and the steps for successful BI implementation. The evolution of BI in the financial industry has also been covered, from essential reporting tools to today’s advanced analytics capabilities. To clarify the function of BI in the financial industry, certain real-world case studies have been incorporated  †

Corresponding Author’s Email: [email protected]. Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

200

Sanjay Taneja, Neha Bansal and Ercan Ozen in this chapter. In conclusion, this chapter offers insights into how BI may support financial organisations in gaining a competitive edge and stimulating innovation via data-driven decision-making.

Keywords: financial sector, business intelligence, BI

Introduction There is a rising demand for tools and techniques that may assist organisations in making sense of the vast quantity of data accessible to them in the financial industry and using it to make better choices. BI can help in this situation. Financial companies may use BI to analyse data from many sources and acquire insights into market trends, client behaviour, investment possibilities, and risks (Cockcroft & Russell, 2018; Huttunen et al., 2019). Financial institutions may improve their own and their customers’ results by utilising BI to make data-driven choices (Vassakis et al., 2018). Businesses may gather, analyse, and display data in a manner that supports business choices using a collection of tools, technologies, and practices called business intelligence (Chaudhuri et al., 2011; Srivastava et al., 2022). By combining data from several sources, including internal systems, external databases, and third-party suppliers, BI offers a holistic perspective of an organisation’s data (Davenport, 2000). By offering insights into corporate processes, BI’s main objective is to assist organisations in making better choices (Watson & Wixom, 2007). Data on a variety of subjects, including customer relationships, market trends, financial performance, and operational efficiency, may be analysed using BI (Bhatnagar et al., 2022; Lim et al., 2013). The capacity to combine data from many sources is one of the essential elements of BI in the financial industry (Reddy et al., 2019). Financial organisations get information from many different places, such as internal sources, external databases, and outside suppliers. Data from these many sources may be gathered by BI technologies and combined to provide a solitary, comprehensive perspective of the company. Financial companies often deal with enormous volumes of data (Bhatnagar et al., 2023; Hussain & Prieto, 2016), and BI technologies can analyse this data quickly and provide real-time insights (Trigo et al., 2014). In order to do this, financial institutions need to have strong data processing skills as well as strong analytical tools that can help them spot trends, patterns, and abnormalities in the data. Financial

The Role of Business Intelligence in the Financial Sector

201

institutions may leverage BI technologies’ actionable information to enhance their business processes (Ibrahim et al., 2014). This requires both strong analytical skills and the capacity to convey facts in a form that is simple to comprehend and use. Financial institutions may rapidly and easily analyse the data using BI technologies’ visualisations, dashboards, and other tools and then act on the knowledge they obtain (Suprata, 2021). In the financial industry, BI tools and methods may be used in a number of different contexts. Banks and other financial companies, for instance, may use BI to analyse consumer data and learn more about the preferences and behaviour of their clients (Moro et al., 2015). They may use this information to build goods and services that more effectively satisfy their clients’ wants. The analysis of financial data using BI may also be used to spot trends and patterns that might help investors make better choices (Cheng et al., 2009). For instance, BI may be applied to analyse stock prices and economic indicators to spot potential investment opportunities and dangers. To help with investment choices, it may also be utilised to analyse the financial performance of certain businesses and sectors (Rajnoha et al., 2016; Taneja et al., 2023). Financial institutions will need BI more and more as the quantity of data at their disposal increases since it will help them understand the data and utilise it to their advantage. The authors will explore the role of BI in the financial industry, which has changed over time and is used to analyse consumer data, discover investment possibilities, and manage risk. The advantages and difficulties of BI adoption in the financial industry will also be covered, along with factors influencing their use. Additionally, real-world case studies from banks, FinTech companies, and financial services companies are covered to demonstrate how financial institutions are using BI to gain a competitive edge. In order to get even more profound insights into financial data and promote company success, the authors will discuss the future of BI in the financial industry, including the use of advanced analytics, artificial intelligence, and machine learning. (Bhatnagar et al., 2022a, 2022b,2023a, 2023b; Dangwal et al., 2022a, 2022b; Jangir et al. 2023, Kumar et al., 2023; Özen et al., 2022a 2022b; Singh et al., 2021; Taneja et al., 2022a, 2022b; Taneja et al., 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2023g, 2023h, 2023i, 2023j, 2023k). Readers will have a thorough grasp of the importance of BI in the financial industry by the conclusion of this chapter, as well as the methods, strategies, and best practices used by financial institutions to maximise the value of their data and promote business success.

202

Sanjay Taneja, Neha Bansal and Ercan Ozen

Evolution of Business Intelligence in Financial Sector The growth of BI in the financial industry can be traced back to the 1970s when financial institutions began employing computer-based systems to manage their operations (Power, 2007). BI has significantly changed over time due to technological improvements, shifting business requirements, and the amount and complexity of data increasing. The following are some significant phases in the development of BI in the financial sector: •

•

•

•

•

Initial stage: In the early phases of BI implementation in the financial industry, most institutions depended on fundamental reporting technologies to create static reports and dashboards (Audzeyeva & Hudson, 2016). These technologies lacked sophisticated analytics capabilities and could not combine data from various sources. Data Warehousing: The development of data warehousing technologies in the 1990s allowed financial firms to combine and store significant amounts of data from many sources (Roth et al., 2002). This made it possible to do multidimensional analysis, data mining, and more sophisticated reporting and analytics (Khan & Quadri, 2012). Self-Service BI: In the 2000s, self-service BI solutions were made available, allowing business users to build their reports and dashboards without the assistance of IT (Schlesinger & Rahman, 2015). This reduced the workload on IT departments while enabling more flexibility and agility in generating insights (Johansson et al., 2015). Big Data Analytics: In the 2010s, the introduction of sophisticated analytics tools and the growth of big data made it possible for financial institutions to analyse enormous volumes of data in realtime (Chen et al., 2012). This made it possible to use machine learning and artificial intelligence to increase the predictive and prescriptive analytics capabilities (Shi-Nash & Hardoon, 2017). Cloud-based BI: Financial institutions are now embracing cloudbased BI solution at a faster rate, as it provides scalability, flexibility, and cost advantages (Dr. Sunil Joshi, 2021; Xue et al., 2021). Because users can access insights from anywhere, at any time, cloud-based BI also facilitates more distributed and collaborative decision-making.

The Role of Business Intelligence in the Financial Sector

203

The development of BI in the financial industry has given institutions the ability to provide more complex insights, increase operational effectiveness, and remain competitive. BI will keep developing and play a crucial role in the financial industry as the value of data-driven decision-making increases.

Role of Business Intelligence in Financial Sector In the financial industry, BI plays a critical role in driving informed business decisions, boosting operational effectiveness, controlling risk, and improving customer experience. Here are some roles that BI plays specifically in the financial industry: 1. Data management: BI systems gather, examine, and analyse massive amounts of data from many sources, including transactional data, customer data, and market data (Ćurko et al., 2007). After that, the data are processed in order to get actionable insights that may be used for making well-informed business decisions. 2. Risk management: By examining market data, credit risk, and operational risk, BI technologies assist financial firms in identifying and managing risk (Mukul & Kumar, 2020; Wu et al., 2014). Financial firms may use this to build methods to reduce risk and prevent losses. 3. Client analytics: BI technologies assist financial institutions in analysing client data and behaviour to spot trends and possibilities for cross-selling, up-selling, and retention (Dass, 2006). These aids financial companies in enhancing client satisfaction and stimulating revenue development. 4. Fraud detection: By monitoring transactions and seeing patterns of questionable behaviour, financial institutions may use BI technologies to find and stop fraud (Minelli et al., 2013). 5. Performance management: BI technologies assist financial firms in tracking and evaluating the effectiveness of their staff, branches, and products (Gessner & Volonino, 2005). Financial firms may use this to identify problem areas and make data-driven choices to boost performance. 6. Regulatory compliance: By automating compliance reporting and ensuring that regulations are followed, BI technologies assist

204

Sanjay Taneja, Neha Bansal and Ercan Ozen

financial firms in ensuring compliance with regulatory standards (Becker & Buchkremer, 2018). The role of BI in the financial industry is to provide financial institutions with the data insights they need to increase operational effectiveness, manage risk, and improve customer experience. Financial organisations may obtain a market edge and promote long-term success with the use of BI technologies.

Factors Driving Business Intelligence in Financial Sector The financial industry is adopting BI due to a number of factors, such as: •

•

•

•

•

Data complexity and volume: Financial organisations produce a lot of data from various sources, including client transactions, market data, and regulatory filings (Cao et al., 2015). In order to find patterns and insights that might lead to improved decision-making, BI tools assist in processing and analysing this data (Mukul & Pathak, 2021; Pillay & van der Merwe, 2021). Regulatory adherence: Financial institutions are required to abide by a number of rules that call for the reporting and oversight of data (Arner et al., 2017). This procedure may be automated using BI technologies, lowering the danger of regulatory fines and noncompliance. Competition: Because the financial industry is so fiercely competitive, institutions must utilise data to understand consumer behaviour, market trends, and competitor plans. Real-time data and analytics from BI technologies may help with strategic decisionmaking and performance enhancement (Niu et al., 2021). Risk management: The financial industry is subject to several operational, market, and credit risks. The danger of losses and reputational harm may be decreased with the use of BI tools that can assist institutions in identifying possible risks and creating measures to minimise them (Siddiqi, 2006). Client experience: In order to keep customers and boost sales, financial institutions must provide outstanding customer service. Institutions may enhance the client experience by personalising their

The Role of Business Intelligence in the Financial Sector

•

205

goods and services with the use of BI technologies, which can assist them in gaining insights into consumer behaviour and preferences. Operational effectiveness: Financial institutions must function effectively to save expenses and increase profitability. Institutions may increase operational efficiency by using BI technologies to streamline procedures, automate workflows, and reduce human labour.

A variety of interrelated issues are influencing the adoption of BI in the financial industry. Financial institutions can compete, adhere to rules, manage risks, enhance the client experience, and streamline operations with the help of BI.

Benefits of Business Intelligence in the Financial Sector The financial industry benefits greatly from BI, including the following:

Increased Operational Efficiencies Financial institutions must be as lean and effective as possible in today’s fiercely competitive environment. Organisations may save on continuing expenditures and make the most of already available resources and skills using BI tools to analyse operational operations. For instance, organisations may find methods to enhance and improve the customer experience at the point of contact by examining the performance of staff members who interact with customers, such as salespeople, tellers, and account managers (Kumar et al., 2023).

Better Products and Services Businesses may monitor specific income streams with the help of BI tools to better understand which goods and services are lucrative and which are not. However, the advantages go further than that. Financial institutions may analyse enormous volumes of client data using BI solutions to learn more about their customers’ requirements and attitudes and improve their goods and

206

Sanjay Taneja, Neha Bansal and Ercan Ozen

services (Xu et al., 2017). For instance, it can be discovered that consumers desire a simpler, faster approach to monitor and analyse their spending and earning trends. Customers may get notifications from institutions more quickly. Alternatively, they desire a simpler and more straightforward application and funding process. With the use of these sorts of data, businesses may design new and enhanced financial goods and services that will better satisfy the demands of their clients and provide them with a competitive advantage.

Better Marketing By using BI, marketers may examine CRM data using a variety of criteria in order to identify the most lucrative client profile (Ngai et al., 2009). The client base may also be examined to find and create fresh cross-sell and up-sell possibilities and to run more focused internet marketing efforts. According to studies, this has significant benefits since selling financial goods and services to new clients is five times more expensive than doing it to current ones.

Better Customer Retention As was already said, BI tools may assist financial firms in identifying and pursuing the most lucrative customers. Additionally, BI is crucial for increasing customer loyalty and retention. Businesses may discover why consumers patronise rival businesses by using business analytics tools and methodologies. They may then put new procedures in place to minimise client attrition. Organisations may adjust their goods and services to suit demands, address issues, and foster customer loyalty and retention by keeping track of client habits, preferences, and behaviours.

Creating New Investment Strategies Asset managers create investment strategies using new data sources. Investors may generate trade signals and obtain specialised insight into sentiment by creating models based on social media. Leveraging analytics and BI applications has led to the emergence of completely new categories of investing (Ravi & Kamaruddin, 2017).

The Role of Business Intelligence in the Financial Sector

207

Avoid Data Overload Financial organisations produce a lot of data from several sources, such as transactional data, client data, and market data. Processing and analysing this data using BI tools may help in finding insights that will assist in making business choices.

Compliance By automating compliance reporting and monitoring, BI technologies make it easier for financial institutions to stay in compliance with legal requirements and stay out of trouble. Financial institutions may provide the reports and paperwork needed by regulators with the use of BI. BI solutions have the ability to automatically gather data from many sources and provide reports that adhere to legal criteria (Lee et al., 2020). This may cut down on time and error-prone work. Financial firms may benefit from BI to have accurate and trustworthy audit trails. Data changes may be tracked by BI tools, which also give an audit trail that can be used to show compliance.

Competitive Advantage By giving financial institutions insights into market trends, client demands, and rival tactics, BI technologies help them remain one step ahead of the competition. BI helps financial organisations track their rivals and identify niches where they may set themselves apart. BI technologies may help organisations keep one step ahead of the market by delivering insights about rival pricing, product offers, and customer satisfaction levels.

Risk Mitigation The financial world is unpredictable and undergoing continual change. Banks and other financial institutions must now more than ever utilise every instrument at their disposal to lower risk. Businesses may utilise the actionable information provided by today’s BI tools to reduce risk in a variety of contexts. Instances of fraud, most notably credit card fraud, may be swiftly identified

208

Sanjay Taneja, Neha Bansal and Ercan Ozen

and decreased because of institutions’ capacity to trace consumer transaction histories. The capacity to monitor internal staff members’ conversations and actions while trading securities aids organisations in adhering to the new regulatory frameworks created by the 2008 financial crisis and current insider trading instances. Global banks may be able to estimate credit risk for counterparties across all asset classes with the use of data that has been locked away in segregated asset class systems. Financial organisations can make better choices, run more effectively, manage risk, and spur development with the unique insights that BI offers. The use of BI is essential for success in today’s data-driven world since it has so many advantages for the financial industry.

Challenges in the Implementation of Business Intelligence in the Financial Sector Several factors, such as the following, can make BI implementation in the financial sector difficult. 1. Data quality: BI depends on high-quality data to give accurate insights. Data input mistakes, data duplication, or data discrepancies across systems may all impair data quality in the financial industry (Chu et al., 2016). 2. Data security: Because financial data is so sensitive, BI systems are required to adhere to stringent security standards (Bhana & Ophoff, 2022). Data sharing across several departments or organisations may be difficult due to security constraints. 3. Integration of a variety of data sources: Financial institutions often employ a variety of data sources that are difficult to combine, making it challenging to get a full picture of the client or the organisation. 4. Scalability: BI systems must be scalable to accommodate the growing amount of data and the complexity of new goods and services as financial institutions expand and diversify their offerings. 5. Cost: Implementing BI solutions may be costly owing to the requirement for specialised expertise, technology, and software. As a result, small financial organisations may find it difficult to deploy BI solutions.

The Role of Business Intelligence in the Financial Sector

209

6. Change management: Putting BI solutions into practice often necessitates organisational procedures and culture changes. Employee opposition to the adoption of BI solutions might come from those who are not used to making decisions based on data. The financial industry demands careful planning, administration, and investment for the adoption of BI solutions. To guarantee the success of BI initiatives, financial institutions must address issues with data quality, security, integration, scalability, cost, and change management.

Steps to Implement BI in the Financial Sector Here are the steps to implement BI in the financial sector which are shown in Figure 1:

Source: Author’s Compilation. Figure 1. Steps to Implement BI in the Financial Sector.

1. Determine Business Requirements and Goals: Financial institutions should determine their unique business needs and goals prior to adopting BI. This involves establishing key performance indicators (KPIs) and measures that are consistent with their corporate objectives. 2. Define Data Sources: Financial institutions should determine the data sources necessary to provide insights after establishing business goals and objectives. This comprises both internal and external data

210

Sanjay Taneja, Neha Bansal and Ercan Ozen

3.

4.

5.

6.

7.

sources, such as market and social media data as well as internal data sources, including transaction, customer, and financial data. Choose the Best BI Tools: Financial institutions should use solutions that suit their unique requirements and goals. This entails selecting tools that can manage the complexity and volume of their data sources and provide the necessary capability for reporting, data analysis, and visualisation. Create a Data Model: To integrate and convert data from many sources, financial institutions should create a data model. This entails generating data transformations to clean, enhance, and format the data, as well as establishing a schema that specifies the connections between various data items. Create Dashboards and Reports: Financial institutions must create KPI and metric-focused dashboards and reports. This entails creating visualisations that clearly convey the insights and provide people with the tools to engage with the data. Test and Validate: Financial institutions should test and verify the BI solution before deploying it to make sure it fulfils their business demands and objectives. This involves evaluating the functioning of the BI tools and dashboards and confirming the data’s correctness and dependability. Implement and Maintain: Financial institutions should implement and sustain the BI solution when it has been verified. This entails keeping an eye on how well the BI solution is doing, fixing any problems that crop up, and constantly enhancing the programme based on user input.

Financial institutions may deploy BI effectively and provide insights that help them make data-driven choices, increase operational effectiveness, and accomplish their business objectives by following these steps.

Suggestions to Improve BI Implementation in the Financial Sector Here are some ideas for enhancing the financial sector’s use of BI:

The Role of Business Intelligence in the Financial Sector

•

•

•

•

•

•

211

Identify business needs: Financial institutions should determine their unique business requirements and objectives before deploying BI. They will be able to choose the appropriate BI tools and data sources as a result, and they can make sure that the deployment of BI is in line with their business plan. Establish a strong data foundation: Since BI depends on accurate and trustworthy data. Financial institutions should invest in data management and governance to ensure that their data is accurate, consistent, and up-to-date. They will be able to provide reliable and useful insights as a result. Promote a data-driven culture: BI involves a change in organisational culture; it is not only a technical solution. By educating staff members on the value of data, promoting data sharing, and rewarding datadriven decision-making, financial institutions may help to create a culture where data is used to drive decisions. Invest in user assistance and training: Since BI systems may be complicated and need specialised knowledge. Financial institutions should invest in user training and support to guarantee that staff can efficiently utilise the BI tools and provide insights that suit their requirements. Constantly assess and improve: BI is an ongoing process that calls for constant review and tweaking. Financial institutions should routinely assess the BI implementation they have in place to make sure it is fulfilling their requirements and find room for improvement. Assure compliance: Financial institutions must ensure that the BI implementation they use conforms with all applicable rules and laws. This involves ensuring that data is utilised according to relevant rules and regulations and that it is secure and secret.

Financial institutions may enhance their BI deployment and provide insights that help them make data-driven choices, increase operational efficiency, and accomplish their business objectives by implementing these recommendations.

212

Sanjay Taneja, Neha Bansal and Ercan Ozen

Future of the BI in the Financial Sector BI has a bright future in the financial industry, with more development and innovation anticipated in the years to come. Business intelligence (BI) in the financial sector is predicted to be driven by technological advancements, more automation, and the financial sector’s increasing emphasis on data analytics. The following are some major themes that will probably influence BI in the financial industry in the future: 1. Application of artificial intelligence (AI) and machine learning: The financial industry already makes use of AI and machine learning technologies to enhance fraud detection, risk management, and customer service (Satheesh & Nagaraj, 2021). These technologies are anticipated to play a greater role in business intelligence (BI) as they advance, helping financial firms to make more precise forecasts and get deeper insights into their data. Financial firms may use these technologies to find patterns and trends in their data that might not be immediately obvious when using conventional BI approaches. 2. Increased automation: As more routine tasks are automated, analysts will have more time to devote to higher-level analysis and strategic decision-making, which is expected to play a larger role in BI in the financial sector. 3. The importance of data privacy and security: As financial institutions struggle to safeguard sensitive consumer data from online dangers, data privacy and security will remain a top priority. BI solutions will need to include cutting-edge security measures and adhere to stringent data privacy laws. 4. Integration with cloud computing: As it offers a scalable and affordable solution to store and analyse massive volumes of data, cloud computing is growing in popularity in the financial industry (El-Seoud et al., 2017). It is anticipated that BI tools that are connected to cloud computing platforms will spread more widely in the coming years. Financial companies can grow their operations more swiftly and easily thanks to cloud-based BI. 5. Emphasis on customer experience: By offering individualised insights and suggestions based on distinct consumer behaviour and preferences, BI technologies will increasingly be leveraged to enhance the customer experience. Users may build their own reports and dashboards using self-service BI tools without the assistance of

The Role of Business Intelligence in the Financial Sector

213

IT or data experts. Users may now independently examine data and learn more rapidly as a result. The financial sector’s BI future is promising, with further development and innovation anticipated. Financial institutions will increasingly rely on BI to support data-driven decision-making and promote business success as they seek to establish a competitive advantage.

Case Studies Several case studies of business intelligence implementation in the financial sector are listed below:

Case Study: American Express An international financial services company called American Express (Amex) provides a variety of credit cards, traveller’s checks, and other financial goods and services. Amex has used business intelligence to enhance the customer experience, lower risk, and spur expansion.

Challenge To enhance the customer experience and spur revenue development, Amex sought to better understand the behaviour and preferences of its consumers. However, the company was having trouble getting a holistic view of the customer because of data silos. Solution Amex put in place a business intelligence (BI) system that combined information from several sources, including transactional data, customer data, and external data sources like social media. In order to deliver individualised product suggestions and marketing offers, the BI solution analysed consumer behaviour and preferences using machine learning algorithms. Results Amex was able to increase revenue and enhance customer experience thanks to the BI solution. Customer satisfaction increased by 30%, and revenue

214

Sanjay Taneja, Neha Bansal and Ercan Ozen

increased by 10% for the business. The system also assisted Amex in lowering risk by allowing proactive risk management and real-time monitoring of fraudulent transactions. Amex has enhanced client experiences and increased income because of the usage of business intelligence, which has allowed the firm to better understand the behaviour and preferences of its consumers. Because it offered proactive risk management and real-time monitoring, the BI solution also assisted the business in lowering risk. Overall, Amex’s usage of BI shows how data analytics can be used to propel corporate success in the financial industry.

Case Study: Google Pay Users of Google Pay may shop online and in person, send and receive money, and manage their accounts. It is a digital wallet and payment system. Google Pay uses BI to make data-driven choices that boost growth and enhance the user experience. The company’s initiatives to promote app adoption among Indian customers serve as one case study illustrating Google Pay’s application of BI. In India, where Google Pay was introduced in 2017, the business was up against established rivals Paytm and PhonePe. Google Pay has to comprehend Indian customers’ demands and preferences in order to adjust its product and marketing tactics and win market dominance. BI was utilised by Google Pay to analyse user data and acquire an understanding of how Indian users interacted with the service. The business utilised this information to pinpoint areas where the user experience might be enhanced and engagement levels raised. For instance, Google Pay discovered that Indian users tended to make smaller, more frequent transactions as opposed to larger, less frequent ones. Google Pay created a feature called “Tez Shots” that rewarded users for doing several transactions in order to accommodate this behaviour. Indian customers embraced the function, and it helped the app become more popular. The Indian market’s trends and potential were also discovered by Google Pay using BI. For instance, the business discovered that there was a huge chance to build its user base in India, where the usage of digital payments was expanding quickly. Google Pay launched a marketing effort aimed at customers in Tier 2 and Tier 3 cities, where digital payments were still in their infancy due to this discovery. The advertising effort increased Google Pay’s user base and encouraged app downloads.

The Role of Business Intelligence in the Financial Sector

215

The success of Google Pay in the Indian market may be directly attributed to the company’s usage of BI. Google Pay has been able to modify its product and marketing tactics to match the demands of Indian customers and promote development in the industry by leveraging data to acquire insights into user behaviour and market trends.

Case Study: World Bank The World Bank is a global financial organisation that offers poor nations loans, grants, and other types of support. The World Bank depends on BI to make data-driven choices in order to efficiently run its operations and achieve its aim of eradicating extreme poverty and fostering shared prosperity. The World Bank’s attempts to increase the efficacy and efficiency of its lending operations serve as one example of how it uses BI. The loan procedure at the World Bank is complex and includes a large number of participants and data. The World Bank has expedited its loan process and increased customer responsiveness by using BI technologies and approaches. For instance, the World Bank utilises BI to examine loan performance statistics. The Bank uses predictive analytics to identify loans that are likely to default and to take preventative action. The World Bank has been able to lower the number of loan defaults and enhance the performance of its loan portfolio by utilising data to detect possible issues early. Analysing customer data is another way the World Bank uses business intelligence. The Bank gathers information on the financial stability of its customers, social variables, and other aspects that impact their capacity to repay loans. The World Bank can make better loan choices by analysing this data to find trends and patterns. For instance, if the Bank notices a pattern of slowing economic growth in a specific nation, it may change its lending priorities and regulations to better meet the demands of that nation’s development. Additionally, the World Bank makes use of BI to boost the effectiveness of its lending activities. The Bank uses data to monitor the status of loan applications and spot any delays in the approval process. The World Bank may find places to simplify its procedures and shorten the time it takes to approve loans by looking at this data. This enables the Bank to serve its customers more quickly and effectively. Utilising BI has been crucial in assisting the World Bank in achieving its goals of eradicating extreme poverty and fostering shared prosperity. The

216

Sanjay Taneja, Neha Bansal and Ercan Ozen

World Bank has been able to increase the efficiency and efficacy of its lending operations, decrease loan defaults, and better meet the development goals of its customers by utilising data to make educated choices.

Conclusion To provide insights that support data-driven decision-making, boost operational efficiency, and accomplish corporate objectives, BI has emerged as a crucial tool in the financial industry. The development of BI in the financial industry has been influenced by technological advancements, shifting business requirements, and escalating rivalry. Financial organisations can now analyse enormous volumes of data in real-time and provide complex insights that were previously impossible thanks to BI, which has evolved from early reporting tools to powerful big data analytics and cloud-based solutions. Financial institutions have gone a long way from simple reporting tools to the comprehensive analytics capabilities available today, including machine learning and artificial intelligence. Selfservice BI and cloud-based BI systems have made it possible to generate insights with greater flexibility, agility, and collaboration. Establishing a strong data foundation, fostering a data-driven culture, investing in user training and support, continuously evaluating, improving, and ensuring compliance with pertinent rules and regulations are all necessary for institutions to successfully implement BI in the financial sector. As institutions try to use data to their advantage and spur innovation, BI will likely continue to be important in the financial sector in the future. Financial institutions must be ready to adapt and expand their BI systems to suit their changing demands as data sources continue to increase in quantity and complexity.

References Arner, D. W., Barberis, J., & Buckley, R. P. (2017). FinTech, regTech, and the reconceptualization of financial regulation. Northwestern Journal of International Law and Business, 37(3). Audzeyeva, A., & Hudson, R. (2016). How to get the most from a business intelligence application during the post implementation phase? Deep structure transformation at a

The Role of Business Intelligence in the Financial Sector

217

U.K. retail bank. European Journal of Information Systems, 25(1). https://doi.org/ 10.1057/ejis.2014.44. Becker, M., & Buchkremer, R. (2018). Ranking of current information technologies by risk and regulatory compliance officers at financial institutions - a german perspective. The Review of Finance and Banking, 10(1). Bansal, N., Bhatnagar, M., & Taneja, S. (2023i). Balancing Priorities Through Green Optimism: A Study Elucidating Initiatives, Approaches, and Strategies for Sustainable Supply Chain Management. In Handbook of Research on Designing Sustainable Supply Chains to Achieve a Circular Economy (pp. 60-81). IGI Global. https://doi.org/10.4018/978-1-6684-7664-2.ch004. Bhana, A., & Ophoff, J. (2022). Security Fatigue: A Case Study of Data Specialists. IFIP Advances in Information and Communication Technology, 658 IFIP. https://doi.org/ 10.1007/978-3-031-12172-2_22. Bhatnagar, M., Taneja, S. Kumar, P., & Özen, E., (2023a). Does Financial Education Act as a Catalyst for SME Competitiveness. International Journal of Education Economics and Development, 1(1), 1. https://doi.org/10.1504/ijeed.2023.10053629. Bhatnagar, M., Özen, E., Taneja, S., Grima, S., & Rupeika-Apoga, R. (2022a). The Dynamic Connectedness between Risk and Return in the Fintech Market of India: Evidence Using the GARCH-M Approach. Risks, 10(11), 209. https://doi.org/10. 3390/risks10110209. Bhatnagar, M., Taneja, S., & Özen, E. (2022b). A wave of green start-ups in India—The study of green finance as a support system for sustainable entrepreneurship. Green Finance, 4(2), 253–273. https://doi.org/10.3934/gf.2022012. Bhatnagar, M., Taneja, S., & Rupeika-Apoga, R. (2023b). Demystifying the Effect of the News (Shocks) on Crypto Market Volatility. Journal of Risk and Financial Management, 16(2), 136. https://doi.org/10.3390/jrfm16020136. Cao, M., Chychyla, R., & Stewart, T. (2015). Big data analytics in financial statement audits. Accounting Horizons, 29(2). https://doi.org/10.2308/acch-51068. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. In Communications of the ACM (Vol. 54, Issue 8). https://doi.org/ 10.1145/1978542.1978562. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly: Management Information Systems, 36(4). https://doi.org/10.2307/41703503. Cheng, H., Lu, Y. C., & Sheu, C. (2009). An ontology-based business intelligence application in a financial knowledge management system. Expert Systems with Applications, 36(2 PART 2). https://doi.org/10.1016/j.eswa.2008.02.047. Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016). Data cleaning: Overview and emerging challenges. Proceedings of the ACM SIGMOD International Conference on Management of Data, 26-June-2016. https://doi.org/10.1145/2882903.2912574. Cockcroft, S., & Russell, M. (2018). Big Data Opportunities for Accounting and Finance Practice and Research. In Australian Accounting Review (Vol. 28, Issue 3). https://doi.org/10.1111/auar.12218.

218

Sanjay Taneja, Neha Bansal and Ercan Ozen

Ćurko, K., Bach, M. P., & Radonić, G. (2007). Business intelligence and business process management in banking operations. Proceedings of the International Conference on Information Technology Interfaces, ITI. https://doi.org/10.1109/ITI.2007.4283744. Dahiya, K., Taneja, S., & Özen, E. (2023g). To Analyse the Impact of Multi-Media Technology on the Rural Entrepreneurship Development. In Contemporary Studies of Risks in Emerging Technology, Part A (pp. 221-240). Emerald Publishing Limited. 10.1108/978-1-80455-562-020231015. Dangwal, A., Kaur, S., Taneja, S., & Taneja, S. (2022a). A Bibliometric Analysis of Green Tourism Based on the Scopus Platform. In J. Kaur, P. Jindal, & A. Singh (Eds.), Developing Relationships, Personalization, and Data Herald in Marketing 5.0: Vol. i (pp. 1–327). IGI Global. https://doi.org/10.4018/9781668444962. Dangwal, A., Taneja, S., Özen, E., Todorovic, I., & Grima, S. (2022b). Abridgement of Renewables: It’s Potential and Contribution to India’s GDP. International Journal of Sustainable Development and Planning, 17(8), 2357–2363. https://doi.org/doi. org/10.18280/ijsdp.170802. El Seoud, S. A., El-Sofany, H. F., Abdelfattah, M., & Mohamed, R. (2017). Big data and cloud computing: Trends and challenges. International Journal of Interactive Mobile Technologies, 11(2). https://doi.org/10.3991/ijim.v11i2.6561. Gessner, G. H., & Volonino, L. (2005). Quick response improves returns on business intelligence investments. Information Systems Management, 22(3). https://doi.org/ 10.1201/1078/45317.22.3.20050601/88746.8. Gupta, M., Taneja, S., Sharma, V., Singh, A., Rupeika-Apoga, R., & Jangir, K. (2023). Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?. Journal of Risk and Financial Management, 16(6), 286. https://doi.org/10.3390/jrfm16060286. Hussain, K., & Prieto, E. (2016). Big data in the finance and insurance sectors. In New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe. https://doi.org/10.1007/978-3-319-21569-3_12. Huttunen, J., Jauhiainen, J., Lehti, L., Nylund, A., Martikainen, M., & Lehner, O. M. (2019). Big Data, Cloud Computing and Data Science Applications in Finance and Accounting. ACRN Journal of Finance and Risk Perspectives, 8. Jangir, K., Sharma, V., Taneja, S., &Rupeika-Apoga, R. (2023). The Moderating Effect of Perceived Risk on Users’ Continuance Intention for FinTech Services. Journal of Risk and Financial Management, 16(1). https://doi.org/10.3390/jrfm16010021. Ibrahim, J., Bhatti, Z., & Waqas, A. (2014). Business Intelligence as a Knowledge Management Tool in Providing Financial Consultancy Services. American Journal of Information Systems, 2(2), 26–32. https://doi.org/10.12691/ajis-2-2-1. Johansson, B., Alkan, D., & Carlsson, R. (2015). Self-service BI does it change the rule of the game for BI systems designers. CEUR Workshop Proceedings, 1420. Joshi S., S. T. S. B. (2021). A Study of Impact of Cloud Computing and Artificial Intelligence on Banking Services, Profitability and Operational Benefits. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6). https://doi.org/10.17762/turcomat.v12i6.3198. Khan, R. A., & Quadri, S. (2012). Business intelligence: an integrated approach. Business Intelligence Journal, 5(1).

The Role of Business Intelligence in the Financial Sector

219

Kumar, P., Taneja, S., Özen, E., & Singh, S. (2023h). Artificial Intelligence and Machine Learning in Insurance: A Bibliometric Analysis. In Smart Analytics, Artificial Intelligence and Sustainable Performance Management in a Global Digitalised Economy (pp. 191-202). Emerald Publishing Limited. https://doi.org/10.1108/ S1569-37592023000110A010. Kumar, P., Verma, P., Bhatnagar, M., Taneja, S., Seychel, S., Todorović, I., & Grim, S. (2023). The financial performance and solvency status of the indian public sector banks: A CAMELS rating and Z index approach. International Journal of Sustainable Development and Planning, 18(2), 367-376. https://doi.org/10.18280/ ijsdp.180204. Lee, J., Wewege, L., & Thomsett, M. C. (2020). Disruptions and Digital Banking Trends. Journal of Applied Finance & Banking, 10(6). Lim, E. P., Chen, H., & Chen, G. (2013). Business intelligence and analytics: Research directions. In ACM Transactions on Management Information Systems (Vol. 3, Issue 4). https://doi.org/10.1145/2407740.2407741. Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. In Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. https://doi.org/10.1002/9781118562260. Moro, S., Cortez, P., & Rita, P. (2015). Business intelligence in banking: A literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Systems with Applications, 42(3). https://doi.org/10.1016/j.eswa.2014.09.024. Mukul, & Kumar, R. (2020). Research exhibiting the influence of demonetization on income tax return. Journal of Critical Reviews, 7(16), 1633–1638. https://doi.org/ 10.31838/jcr.07.16.204. Mukul, & Pathak, N. (2021). Are the financial inclusion schemes of India developing the nation sustainably? In T. M. (Ed.), E3S Web of Conferences (Vol. 296). EDP Sciences. https://doi.org/10.1051/e3sconf/202129606011. Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. In Expert Systems with Applications (Vol. 36, Issue 2 PART 2). https://doi.org/10. 1016/j.eswa.2008.02.021. Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing and Management, 58(6). https://doi.org/10.1016/j.ipm.2021.102725. Ozen, E., & Sanjay, T. (2022a). Empirical Analysis of the Effect of Foreign Trade in Computer and Communication Services on Economic Growth in India. Journal of Economics and Business Issues, 2(2), 24–34. https://doi.org/https://jebiacademic.org/index.php/jebi/article/view/41. Ozen, E., Taneja, S., & Makalesi, A. (2022b). Critical Evaluation of Management of NPA / NPL in Emerging and Advanced Economies: a Study in Context of India, Yalova Sosyal Bilimler Dergisi, 12(2), 99–111. https://doi.org/https://dergipark.org.tr/ en/pub/yalovasosbil/issue/72655/1143214.

220

Sanjay Taneja, Neha Bansal and Ercan Ozen

Pillay, K., & van der Merwe, A. (2021). Big Data Driven Decision Making Model: A case of the South African banking sector. South African Computer Journal, 33(2). https://doi.org/10.18489/SACJ.V33I2.928. Power, D. J. (2007). A Brief History of Decision Support Systems. Decision Support Systems, 4(1969). Rajnoha, R., Štefko, R., Merková, M., & Dobrovič, J. (2016). Business intelligence as a key information and knowledge tool for strategic business performance management. E a M: Ekonomie a Management, 19(1). https://doi.org/10.15240/ tul/001/2016-1-013. Ravi, V., & Kamaruddin, S. (2017). Big data analytics enabled smart financial services: Opportunities and challenges. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10721 LNCS. https://doi.org/10.1007/978-3-319-72413-3_2. Reddy, C. S., Sangam, R. S., & Srinivasa Rao, B. (2019). A survey on business intelligence tools for marketing, financial, and transportation services. Smart Innovation, Systems and Technologies, 105. https://doi.org/10.1007/978-981-13-1927-3_53. Reepu, R., Taneja, S., Ozen, E., & Singh, A. (2023j). A Globetrotter to the Future of Marketing: Metaverse. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 1-11). IGI Global. https://doi.org/10.4018/978-1-6684-83121.ch001. Roth, M. A., Wolfson, D. C., Kleewein, J. C., & Nelin, C. J. (2002). Information integration: A new generation of information technology. IBM Systems Journal, 41(4). https://doi.org/10.1147/sj.414.0563. Satheesh, M. K., & Nagaraj, S. (2021). Applications of Artificial Intelligence on Customer Experience and Service Quality of the Banking Sector Background Study. In International Management Review (Vol. 17, Issue 1). Schlesinger, P. A., & Rahman, N. (2015). Self-Service Business Intelligence resulting in disruptive technology. Journal of Computer Information Systems, 56(1). https://doi.org/10.1080/08874417.2015.11645796. Shi-Nash, A., & Hardoon, D. R. (2017). Data analytics and predictive analytics in the era of big data. In Internet of Things and Data Analytics Handbook. https://doi.org/ 10.1002/9781119173601.ch19. Siddiqi, N. (2006). Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring. In John Wiley & Sons, Inc. (Vol. 1). Singh, V., Taneja, S., Singh, V., Singh, A., & Paul, H. L. (2021). Online advertising strategies in Indian and Australian e-commerce companies: A comparative study. Big Data Analytics for Improved Accuracy, Efficiency, and Decision Making in Digital Marketing, 124–138. https://doi.org/10.4018/978-1-7998-7231-3.ch009. Srivastava, G., Muneeswari, S., Venkataraman, R., Kavitha, V., & Parthiban, N. (2022). A review of the state of the art in business intelligence software. In Enterprise Information Systems (Vol. 16, Issue 1). https://doi.org/10.1080/17517575. 2021.1872107. Suprata, F. (2021). Data Storytelling with Dashboard: Accelerating Understanding Through Data Visualization in Financial Technology Company Case Study. Jurnal METRIS, 20(1). https://doi.org/10.25170/metris.v20i1.2375.

The Role of Business Intelligence in the Financial Sector

221

Taneja, S. Kaur, S. & Özen, E., (2022a). Using green finance to promote global growth in a sustainable way. International Journal of Green Economics, 16(3), 246-257. https://doi.org/10.1504/ijge.2022.10052887. Taneja, S., & Özen, E. (2023a). To analyse the relationship between bank’s green financing and environmental performance. International Journal of Electronic Finance, 12(2), 163-175. https://doi.org/10.1504/IJEF.2023.129919. Taneja, S., Bhatnagar, M., Kumar, P., & Rupeika-apoga, R. (2023b). India’s Total Natural Resource Rents (NRR) and GDP: An Augmented Autoregressive Distributed Lag (ARDL) Bound Test. Journal of Risk and Financial Management, 16(2), 91. https://doi.org/doi.org/10.3390/jrfm16020091. Taneja, S., Bhatnagar, M., Kumar, P., Grima, S. (2023c). A panel analysis of the effectiveness of the asset management in Indian agricultural companies. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 653-660. https://doi.org/10.18280/ijsdp.180301. Taneja, S., Gupta, M., Bhushan, P., Bhatnagar, M., & Singh, A. (2023k). Cultural Marketing in the Digital Era. In Cultural Marketing and Metaverse for Consumer Engagement (pp. 109-122). IGI Global. 10.4018/978-1-6684-8312-1.ch008. Taneja, S., Jaggi, P., Jewandah, S., & Ozen, E. (2022b). Role of Social Inclusion in Sustainable Urban Developments: An Analyse by PRISMA Technique. International Journal of Design and Nature and Ecodynamics, 17(6), 937–942. https://doi.org/ 10.18280/ijdne.170615. Taneja, S., Ozen, E. (2023d). Impact of the European Green Deal (EDG) on the agricultural carbon (CO2) emission in Turkey. International Journal of Sustainable Development and Planning, Vol. 18, No. 3, pp. 715-727. https://doi.org/10. 18280/ijsdp.180307. Trigo, A., Belfo, F., & Estébanez, R. P. (2014). Accounting Information Systems: The Challenge of the Real-time Reporting. Procedia Technology, 16. https://doi.org/ 10.1016/j.protcy.2014.10.075. Vassakis, K., Petrakis, E., & Kopanakis, I. (2018). Big data analytics: Applications, prospects and challenges. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 10). https://doi.org/10.1007/978-3-319-67925-9_1. Watson, H. J., & Wixom, B. H. (2007). The current state of business intelligence. Computer, 40(9). https://doi.org/10.1109/MC.2007.331. Wu, D. D., Chen, S. H., & Olson, D. L. (2014). Business intelligence in risk management: Some recent progresses. Information Sciences, 256. https://doi.org/10.1016/j.ins. 2013.10.008. Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37(6). https://doi.org/10. 1016/j.ijinfomgt.2017.06.004. Xue, M., Xiu, G., Saravanan, V., & Montenegro-Marin, C. E. (2021). Cloud computing with AI for banking and e-commerce applications. Electronic Library, 39(4). https://doi.org/10.1108/EL-07-2020-0207.

Chapter 13

The Dynamic Linkages Between Stock Market Indices and Exchange Rates for BRICS Sukhmani Kaur1, Shalini Aggarwal2,† and Vikas Arora1 1University 2University

School of Business, Chandigarh University, Mohali, Punjab, India School of Business-AIT, Chandigarh University, Mohali, Punjab, India

Abstract Purpose: To study the association between the stock market indices (SMI) and exchange rates (ER) for BRICS economies from 2000-2020 on daily basis data. Design/methodology/approach: The paper uses johansen cointegration test (JCT) to find out the long term (LT) association. Further, VECM model, short term (ST) causality and longterm (LT) causality is used to know the association better. Findings: Presence of cointegration betwixt the stock market indices (SMI) of BRICS economies shows that there exists long term (LT) association among the indices. They will move in the same direction. Similarly, the presence of cointegrating variables among the exchange rate of BRICS economies shows the presence of long-term (LT) association among the exchange rates. Practical Implication: The association betwixt the exchange rates and stock rates is of utmost importance because the strong relationship between them will help to make economic policies and to take international capital budgeting decisions, whereas the negative or

 †

Corresponding Author’s Email: [email protected]. Corresponding Author’s Email: [email protected].

In: Global Financial Analytics and Business Forecasting Editors: Sanjay Taneja, Ercan Özen, Pawan Kumar et al. ISBN: 979-8-89113-223-8 © 2024 Nova Science Publishers, Inc.

224

Sukhmani Kaur, Shalini Aggarwal and Vikas Arora weak relation will affect the market, and effect can be transmitted quickly to other global markets which we have seen in 2008 financial crises.

Keywords: exchange rate, stock markets indices, wald test, VECM, BRICS

Introduction In a constantly fluctuating world where everything is constantly changing, whether it’s technology, fashion, taste and preferences, strict competition policies, environment, macroeconomic variables like tax policies, exchange rates, interest rates, stock market rate, foreign policies etc. are also fluctuating on constant pace and have a different impact on different countries (Kaur et al., 2021). With the ease of doing business and systematic border cross-border movements across the world in terms of FDI/FII on the stock exchange has brought a great impact on other various exchanges. The appropriate financial knowledge of the economic markets and their rates guides the investors in making investment and financing decisions (Aggarwal et al., 2021). As the return amount is not solely determined by the values in assets, per se, it is inclusive of currency fluctuations as well. So, if the current value is increased proportionally, it will increase the gains for investors, and if there is any decrease, the results will be vice-versa (Aggarwal and Khurana, 2018). For instance the financial crises in 2008 has impacted developed as well as emerging countries by bringing a reduction in the imports and foreign investments which leads to fluctuations in the currency rates. Furthermore, the speculative activities increase by the investors due to volatility in the rates, which has brought capital conflicts. In addition, there are several relationships which are the foundations of both micro-and macro-based relations among stock prices and foreign exchange. As per. Dornbusch and Fischer (1980), it had been observed that there is an impact on international competitiveness in local firms. The impact can be negative or positive, affecting the smoothness of cash flow. Ultimately, it will directly impact the frequency of both high and low in stock prices (Bansal and Aggarwal, 2017) Ideally, the foreign exchange rate can be easily affected by the stock prices frequency (Branso 1983) furtherher, the stock price fluctuations impact the supply and demand exchange, which again follows to either appreciate or depreciate the exchange rate. A booming market lures various investors. From a theoretical perspective, the firms that are involved in export-oriented trades gain severe benefits in currency depreciation, as weak currency value makes them export

The Dynamic Linkages Between Stock Market Indices ...

225

a large number of goods and services and automatically increases their stock prices (Aggarwal and Khurana, 2018). Simultaneously, as the profits decline, the stock prices also depreciate. Finally the currency depreciation leads to an adverse impact on the firm’s stock prices. Due, to portfolio management, the involvement among stock and exchange rates remains dynamically (Bahmani-Oskooee&Saha, 2016). Assets allocation and risk management also play a pivotal role in portfolio management. There has been more significant financial uncertainty due to economic turbulence. The Transactions in leading currencies in foreign exchanges have declined due to these adversities in the economic environment. This again leads to a regretful impact on the returns in stocks (Caporale et al., 2014).

Literature Review The impact of changing exchange rates (ER) on stock market index (SMI) returns and vice versa and how these elements interact with financial markets constitute an amazing area of study. The relationship for the changes in the exchange rates and variations in SMI returns has received more attention from researchers, according to studies. Measuring the connection betwixt the ER and SMIis gaining importance and is crucial for financial investment as it leads to the management of portfolio, asset allocation, and management of the risk. The majority of the studies that have been done on the dynamic relationship betwixt and stock returns (SR) have bring into being conflicting results. Some research studies the evidence of a positive association betwixt stock returns (SR) and exchange rates (ER) by using wavelet analysis from 2006 to 2016 on daily basis data (Dahir et al., 2018). Another study by Ahmed (2017) and Chkili et al., (2011) has also found the same result. Although there are many studies that show the negative association between the stock indices of BRICS economies after the US crisis (Han and Zhou, 2017). The another study by Chkilia and Nguyenb (2014), aims to analyze the dynamic linkages between the SR and ER with respect to BRICS for two extraordinary periods of prime crises (the late 1990s and the late 2000s) by using Markov switching regressive autoregressive univariate analysis from 1997 to 2013 on weekly data and reveals that there is no significant association between ER and SMI of BRICS economies except for South Africa. Likewise, another study by Robert and Gay (2016) were determined to reveal the association between SMI,

226

Sukhmani Kaur, Shalini Aggarwal and Vikas Arora

exchange rate and oil prices for BRIC economies by using Box-Jenkins ARIMA model from 1999 to 2006 time period and reveals that there is no significant association amid oil prices and SMI of BRIC countries. The present research system on the relation of variation in exchange rates and variations in stock market returns is rather small and mainly deals with the financial markets of emerging economies, but with a less attention to the BRICS nations (Singh et al., 2022). Using Johansen cointegration test (JCT), VAR, VECM and generalised impulse from 2004-2014, Singh & Singh (2016) examined the LT and ST association and causal connection of the US and BRIC share markets for the time frames of pre and post crises. The results show the presence of one-way causality in all the markets under study. Another study by Tudor &Dutaa (2012), which looks at the Granger causality ofSR and ER movement of emerging financial markets, shows that only Brazil and Russia are affected by the exchange rate movement (Rathee and Aggarwal, 2022, b). Tang and Yao (2018) used co-integration and causality tests to find that the domestic financing structure had significantly impacted the relationship between the ER and share price in 11 developing countries between 1988 and 2014. They set out to understand how domestic financing structures affected the correlation between SMI prices and ER in these countries. The study by Sosvilla-Rivero (2018) shows that while ST volatility associations are weak in the post-global financial crisis period, LT volatility associations are strong. The study also aims to investigate the inter and intra-spillover impacts between the forex and share markets of seven economies for the period from 1990 to 2015. The equity markets of the BRICS nations have a long-term cointegrating relationship, per Aggarwal and Khurana’s 2017 study. However, from 2008 to 2015, there was no proof of a long-term causal link between the four stock markets. Mroua and Trabelsi (2020) used the ARDL and GMM models from 2008 to 2018 to investigate the dynamic linkages and an association betwixt BRICS Exchange rate (ER) changes and SMI volatility. The results demonstrate both a strong LT and ST relationship between market indices.

The Dynamic Linkages Between Stock Market Indices ...

227

Research Methodology The Data Used The daily data is used for the Exchange rate (ER) and SMI of BRICS countries. Table 1 and Table 2 depicts SMI and ER of the country to which represent respectively. The data has been taken from Yahoo finance from 2010-2020 (Karim, Kassim and Arip, 2010). Table 1 has the symbols for theSMI of BRICS countries. The symbols used are as IBOV, RTSI, BSEEN, SHCOMP, FTSE/JSE respectively. Table 2 represents the exchange rates (ER) of BRICSw.r.t to US dollars. The symbols used in the study are BRL/US, RUB/US, INR/US, CNY/US and ZAR/US. URTchecks the stationary of the variables. Augmented dickey fuller (ADF) test is employed to scrutinize the series stationarity. Similarly, Phillip Perron (PP) test is used to scrutinize the stationarity of the series. Table 1. Symbols of stock markets Indices (SMI)

Source: Authors’ compilation.

Table 2. Exchange Rates

228

Sukhmani Kaur, Shalini Aggarwal and Vikas Arora

Methodology Unit root test (URT) is employed to look upon the stationary of the variables, JCT and ST causality to find out the association among the BRICS countries. Johansen’s Cointegration test (JCT): The next step after calculating URT is to go for JCT checks whether BRICS countries SMI and ER rates are cointegrated (Haron and Azmi, 2008). The test help to understand whether the series will move in the same direction or not (Gujarati & Porter, 2011). It will further help to understand the investment in SMI and ER of BRICS countries will reap benefit to the investor in the long run. Johansen’s cointegration model was used in the present study and in which BSESB, RTSI, SHCOMP, IBOV & FTSE/JSE are the SMI of India, Russia, Brazil & South Africa. Further, the study uses two tests for cointegration as suggested by Johansen (1991). It includes Trace test and Eigenvalue test. The present paper applies both the test of cointegration to find out the relationship betwixt the variables studied. Vector Error correction model (VECM) is used in case the variables are cointegrated (kurihara and Nezu, 2006). VECM helps to find the association of LT and ST relationship/cointegration for the variables that are studied. Wald test can be used to understand the ST relationship. T test is used to understand the LT association.

Data Analysis Summary of the descriptive statistics (DS) of variables used for analysis is exhibited in Table 3 and Table 4. Table 3 depicts the DS for the stock indices of BRICS countries. It is analysed that Indian stock exchange is having the highest mean value of 51.873. The lowest mean value could be inferred for Brazil stock exchange i.e., 2.5. The Table 3 shows the DSfor the stock indices of BRICS countries. Mean, median is being shown for different BRICS countries SMI. Probability value forthe jarquebera 1 is showing less than 0.05 denotes that none of the series are normally distributed. Table 4 studies the DS for the exchange rate of BRICS countries. It presents that the Brazil exchange rate’s mean value is the highest (44748.06) followed by South Africa exchange rate (29555.69). Probability value of the jarquebera 1 is showing