Fintech and Cryptocurrency

Mohd Naved, PhD, is an associate professor with a career spanning over a decade in the fields of business analytics, data science, and artificial intelligence. He has over 80 publications in reputed scholarly journals and books, and his research focuses on the applications of these disciplines in various industries.V. Ajantha Devi, PhD, is a distinguished researcher and holds the position of Research Head at AP3 Solutions, located in Chennai, Tamil Nadu, India. She earned her Ph.D. from the University of Madras in 2015, marking the inception of her impressive academic journey. She has over 45 papers published in prestigious international journals and conference proceedings, and she has authored, co-authored and edited numerous books.She holds five Australian patents and one Indian patent, and she has won numerous awards.Aditya Kumar Gupta, PhD, is an associate professor at Amity University, Noida, India, with over 20 years of experience in both academia and industry. He is the editor of the Amity Case Research Journal, and he has been a reviewer for many scientific journals.

• Preface xvii1 Evolution of Fintech in Financial Era 1Tanya Kumar and Satveer Kaur1.1 Introduction 11.2 Review of Literature 21.3 Objectives and Research Methodology 41.4 Working of FinTech 41.5 Tools and Techniques used in FinTech 51.6 Future Framework of FinTech 71.7 Evolution of FinTech in Financial World 71.8 Discussion and Conclusion 9References 9Webliography 112 Digital Transformation of Financial Services in the Era of Fintech 13Ayesha Siddiqui, Arti Yadav and Najib H.S. Farhan2.1 Introduction 132.2 Review of Literature 162.2.1 Studies on FinTech 162.2.2 Studies on Digital Transformation 182.3 Digital Transformation: A Conceptual Overview 202.4 FinTech Ecosystem 212.5 Role of Fintech and Digital Transformation with Respect to Financial Services 242.6 Conclusion 27References 273 Reshaping Banking with Digital Technologies 35Ankita Srivastava and Aishwarya Kumar3.1 Banking and Artificial Intelligence (AI) 353.1.1 Basic Algorithms and Machine Intellect 373.1.2 Artificial Common Intelligence 373.1.3 Ultra Smart AI 373.2 Fintech Evolution 383.3 AI Opportunities in Fintech 403.3.1 Automation 403.3.1.1 Robotic Process Automation (RPA) 403.3.1.2 iPaaS 413.3.1.3 iSaaS 413.3.1.4 Bots 413.3.1.5 Enterprise Automation 413.3.2 Improved Decision Making 423.3.3 Customization 433.4 Reshaping the Banking 443.4.1 Payments 443.4.2 Lending & AI-Based Credit Analysis 463.4.3 Wealth Management 483.4.3.1 Portfolio Management 483.4.3.2 Compliance Management 493.4.3.3 Robo-Advisory 503.5 Insurance 523.6 Challenges Faced by Fintech in Banking 533.6.1 Regulatory Compliance 533.6.2 Customer Trust 533.6.3 Blockchain Integration 533.7 Conclusion 54References 544 Adoption of Fintech: A Paradigm Shift Among Millennials as a Next Normal Behaviour 59Pushpa A., Jaheer Mukthar K. P., Ramya U., Edwin Hernan Ramirez Asis and William Rene Dextre Martinez4.1 Introduction 604.1.1 Evolution of Fintech 614.1.2 Technology Innovation in the Financial Sector - Building a Digital Future 634.1.3 Taxonomy of Fintech Business Model 664.1.4 Fintech Ecosystem 694.1.5 Prepositions for Fintech Adoption 724.1.6 Challenges of the Fintech Industry 744.2 Statement of the Problem and Research Questions 754.3 Research Questions and Objectives 764.4 Conceptual Framework and Proposed Model 774.4.1 Conceptual Framework 774.4.2 The TAM Model 774.4.3 Proposed Model and Hypothesis Framed 794.5 Conclusion 83Acknowledgement 83References 835 A Comprehensive Study of Cryptocurrencies as a Financial Asset: Major Topics and Market Trends 91Gioia Arnone and Ajantha Devi Vairamani5.1 Introduction 915.2 Literature Review 925.3 Methodology 955.4 Findings 965.5 Cryptocurrencies as a Major Financial Asset 975.6 What is the Value of Cryptocurrencies? Current Market Trends 985.7 Conclusion 100References 1016 Customers’ Satisfaction and Continuance Intention to Adopt Fintech Services: Developing Countries’ Perspective 105Song Bee Lian and Liew Chee Yoong6.1 Introduction 1066.2 Understanding the Fintech Phenomenon in Developing Countries 1086.3 Literature Review 1096.3.1 Technology Acceptance Model (TAM) 1096.3.2 Customer Satisfaction 1106.3.3 Customer Innovativeness 1106.3.4 Hedonic Motivation 1116.3.5 Perceived Usefulness 1116.3.6 Perceived Ease of Use 1126.3.7 System Quality 1136.3.8 Technology Self-Efficacy 1136.3.9 Continuance Intention to Adopt 1146.3.10 Hypothesis Development 1146.3.11 Conceptual Model 1156.4 Research Methodology 1156.4.1 Sample and Data Collection 1156.4.2 Measures of the Constructs 1166.4.3 Validity and Reliability Assessment 1166.5 Results 1206.5.1 Demographic Profile of the Respondent 1206.5.2 Structural Model Assessment 1206.6 Discussion 1226.7 Theoretical and Practical Implications 1266.8 Conclusion 127References 1287 Fintech Apps: An Integral Tool in Titivating Banking Operations 137Arun Prakash A., Leelavathi R., Rupashree R. and V.G. Jisha7.1 Introduction 1387.2 Objectives 1427.3 Statement of the Problem 1437.4 Need for the Study 1447.5 Review of Literature 1447.6 Proposed Model 1457.7 Lending APPS 1457.8 Investment Apps 1457.9 Payment Apps 1477.10 Insurance Apps 1487.11 Persuading Factors that Increase the Usage of Fin-Tech Apps 1497.12 Methodology 1507.13 Results and Discussions 1517.14 Multiple Linear Regression 1527.15 Structural Equation Modelling 1547.16 Conclusion 155References 1558 Analytical Study of Fin-Tech in Banking: A Utility Model 157Neha Kamboj and Mamta Sharma8.1 Introduction 1588.2 Literature Analysis and Development of Hypothesis 1608.2.1 Perceived Utility (PU) and Willingness to Adopt Fin-Tech (WUF) Services 1618.2.2 Sensible Usability (SU) and Willingness to Use Fin-Tech (WUF) Services 1618.2.3 Customer Belief (CU) and Willingness to Use Fin-Tech (WUF) Services 1628.2.4 Social Implications (SI) and Willingness to Use Fin-Tech (WUF)Services 1628.3 Research Design 1638.4 Empirical Results 1658.4.1 Effect of Perceived Utility of Fin-Tech Services (PU) on the Willingness of Customers to Use Fin-Tech (ICUF) Services 1688.4.2 Effect of Perceived Ease of Use of Fin-Tech Services (PEU) on the Willingness of Customers to Use Fin-Tech (ICUF) Services 1688.4.3 Effect of Customer Belief in Fin-Tech Services (CU) on the Willingness of Customers to Utilize Fin-Tech (ICUF) Services 1688.4.4 Social Influence (SI) Impact on the Willingness of Customers to Utilize Fin-Tech (ICUF) Services 1698.5 Conclusion 169References 1709 Is Digital Currency a Payment Disruption Mechanism? 173Vanishree Mysore Ramkrishna and Vyshnavi Loganathan9.1 Introduction 1739.2 Review of Literature 1759.3 Methodology and Sampling 1779.4 Results and Discussion 1799.4.1 Financial Literacy & Inclusion 1809.4.2 Infrastructure 1819.4.3 Technical Know-How 1839.4.4 Trust and Belief 1849.5 Acceptance of CBDC 1859.6 Conclusion 188References 19010 Investor Sentiment Driving Crypto-Trade in India 193Sushant Waghmare and Dipesh Uike10.1 Introduction 19410.2 Review of Literature 19510.2.1 Finance & Sentiments 19510.2.2 Cryptocurrency 19610.2.3 Addiction or Analysis? 19710.2.4 Fear of Missing Out (FOMO) 19810.3 Research Methodology 20010.3.1 Aim of the Study 20010.3.2 Objectives of the Study 20010.3.3 Sampling Methodology & Data Analysis 20010.3.4 Limitations of the Study 20110.4 Data Analysis & Interpretation 20110.5 Conclusions, Suggestions & Recommendations 214References 21711 Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective 221W. Jaisingh, Preethi N. and R. K. Kavitha11.1 Introduction 22211.2 State-of-the-Art Review 22311.3 Application Areas of Cryptocurrencies 22411.3.1 Fundraising and Investments 22411.3.2 Freight Transportation and Travel 22611.3.3 Education 22711.3.4 Publication and Advertising as Means of Communication 22711.3.5 E-Commerce and Entertainment 22811.3.6 Real Estate and Stock Market 22811.3.7 Trained Financial Planners 22811.4 Financial Transaction Using Blockchain Technology 22911.4.1 Introduction 22911.4.2 Technology Acceptance Model 23011.4.3 External Constructs 23011.4.3.1 Trust (T) 23011.4.3.2 Support (RS) for Regulatory Standards 23211.4.3.3 Experience (E) 23211.4.3.4 Social Influence (SI) 23311.4.3.5 Design (D) 23311.4.4 Summary 23411.5 An Analysis of Cryptocurrency Mining Using a Hybrid Approach 23411.5.1 Introduction 23411.5.2 Cryptocurrency Mining Strategies 23511.5.3 Summary and Discussion 23611.6 Forecasting Cryptocurrency Price Using Convolutional Neural Networks 23711.6.1 Introduction 23711.6.2 Assistive Technologies in Machine Learning and Deep Learning 23811.6.3 Convolutional Neural Networks with Weighted and Attentive Memory Channels 23811.6.3.1 Attentive Memory Module 23911.6.3.2 Convolution & Pooling Module 23911.6.4 Summary and Discussion 23911.7 Blockchain Technology and Cryptocurrencies for the Collaborative Economy 24011.7.1 Introduction 24011.7.2 Collaborative Economy and Digital Platforms 24111.7.3 Emergence of Collaborative Consumption 24211.7.3.1 Promoting the Diffusion of Collaborative Practices 24211.7.4 Summary 24211.8 Conclusions 243References 24312 A Study on the Influence of Personality on Savings and Investment in Cryptos 251K. Manimekalai, T. Satheeshkumar and G. Manokaran12.1 Introduction 25212.2 Literature Review 25312.2.1 Openness to Experience 25412.2.2 Conscientiousness 25612.2.3 Extroversion 25712.2.4 Aggreeableness 25812.2.5 Neuroticism 25912.3 Objectives of the Research 26012.4 Methodolgy 26012.5 Discussion 268References 26913 Deep Neural Network in Security: A Novel Robust CAPTCHA Model 277Manasi Chhibber, Rashmi Gandhi, Aakanshi Gupta and Ashok Kumar Yadav13.1 Introduction 27713.2 Literature Review 27913.2.1 Convolutional Neural Networks 28013.2.2 Transfer Learning 28113.3 Proposed Approach 28313.3.1 Data Pre-Processing and Exploratory Analysis 28313.3.2 Data Acquisition 28313.4 Results and Discussions 28913.4.1 Cnn 28913.4.2 DenseNet 29213.4.3 MobileNet 29513.4.4 Vgg 16 29713.5 Conclusion 300References 30014 Customer’s Perception of Voice Bot Assistance in the Banking Industry in Malaysia 303Manimekalai Jambulingam, Indhumathi Sinnasamy and Magiswary Dorasamy14.1 Introduction 30314.2 Problem Statement 30414.3 What is a Voice Bot? 30514.3.1 Characteristics of a Voice Bot 30714.3.2 Why are Voice Bots Becoming Popular? 30814.3.3 Benefits of a Voice Bot from the Bank’s Perspective 30914.3.4 Benefits of a Voice Bot from the Customer’s Perspective 31114.3.5 Opportunities for Voice Bots in Banking 31214.3.6 Use Cases: Bank Voice Bots Currently Available Around the World 31314.4 Call to Action 31514.5 Literature Review 31614.6 Research Methodology 31714.7 Descriptive Analysis 31814.8 Discussion and Conclusion 321References 32215 Application of Technology Acceptance Model (TAM) in Fintech Mobile Applications for Banking 325Tabitha Durai and F. Lallawmawmi15.1 Introduction 32615.1.1 Significance of the Study 33115.1.2 Research Objectives 33115.1.3 Hypotheses of the Study 33215.1.3.1 Perceived Usefulness Toward the Usage of Fintech 33215.1.3.2 Brand Image Toward the Usage of Fintech 33315.1.3.3 Perceived Risks Toward the Usage of Fintech 33315.2 Methods and Measures 33415.2.1 Instrument Development 33515.3 Results 33615.3.1 Demographic Profile of the Respondents 33615.3.2 Factors Influencing Fintech under Technology Acceptance Model (TAM) 33815.3.2.1 Prominent Factors under Technology Acceptance Model (TAM) that Influence the Usage of Fintech 34015.3.3 Technology Acceptance Model and the Usage of Fintech 34015.3.3.1 Perceived Usefulness Toward the Usage of Fintech 34015.3.3.2 Brand Image Toward the Usage of Fintech 34215.3.3.3 Perceived Risks Toward the Usage of Fintech 34315.4 Discussion 34415.5 Conclusion 346References 34716 Upsurge of Robo Advisors: Integrating Customer Acceptance 351C. Nagadeepa, Reenu Mohan, Antonio Peregrino Huaman Osorio and Willian Josue Fernandez Celestino16.1 Introduction 35116.2 Chatbots 35516.2.1 Benefits of Using Chatbots in Banks 35516.3 Robo-Advisor 35616.3.1 Robo-Adviser – A Brief History 35716.3.2 Historic Account of Robo Advisors 35816.3.3 Robo-Advisor Versus FA (Financial Advisor) 35916.3.4 Robo Advisors in Market 35916.3.5 How Robo-Advisors Work? 36416.3.6 Types of Robo-Advisor 36516.3.7 Top Robo-Advisors 36716.3.8 Points to be Consider while Selection of Robo-Advisor 37116.3.9 Benefits of Robo-Advisors 37216.3.10 Limitations of Robo-Advisors 37316.4 Acceptance of Robo-Advisor 37416.4.1 Theoretical Background and Research Propositions 37416.4.2 A Glimpse of Earlier Research Studies 37516.4.3 Methodology and Hypothesis 37616.4.4 Collection of Data 37716.4.5 Hypotheses Testing 37716.5 Conclusion 379References 37917 Super Apps: The Natural Progression in Fin-Tech 383Kavitha D., Uma Maheswari B. and Sujatha R.17.1 Introduction 38317.2 Journey from an App to a Super App 38517.3 Super App vs. A Vertically Integrated App 38517.4 Architecture and Design of Super Apps 38617.4.1 Monolithic Architecture 38817.4.2 Modular Architecture 38817.4.3 Microservices Architecture 38917.5 Business Models of Super Apps 39017.6 The Super App Market Space and the Business Models 39117.6.1 WeChat 39117.6.2 Alipay 39317.6.3 Grab 39417.6.4 Paytm 39517.6.5 The Latest Entrant: Tata Neu 39617.6.6 Other Players 39617.7 Factors Contributing to the Success of Super Apps 39717.8 Super Apps in Fin-Tech and their Role in the Financial Services Segment 40017.9 Role of Super Apps in Financial Inclusion 40317.10 Benefits of Super Apps in the Financial Services Sector 40517.10.1 Economies of Scale & Cost of Financial Intermediation 40517.10.2 Size & Speed of Product and Service Offerings 40617.10.3 The Power of Data and the Speed of Responsiveness 40617.11 Risks due to Super Apps in the Financial System 40617.11.1 Financial Stability 40717.11.2 Market Concentration 40717.11.3 Dis-Intermediation of Banks from Customer 40817.12 Regulatory Measures to Mitigate the Risks 40817.13 The Future of Super Apps 40817.14 Conclusion 410References 411Index 413