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 xvii

1 Evolution of Fintech in Financial Era 1
Tanya Kumar and Satveer Kaur

1.1 Introduction 1

1.2 Review of Literature 2

1.3 Objectives and Research Methodology 4

1.4 Working of FinTech 4

1.5 Tools and Techniques used in FinTech 5

1.6 Future Framework of FinTech 7

1.7 Evolution of FinTech in Financial World 7

1.8 Discussion and Conclusion 9

References 9

Webliography 11

2 Digital Transformation of Financial Services in the Era of Fintech 13
Ayesha Siddiqui, Arti Yadav and Najib H.S. Farhan

2.1 Introduction 13

2.2 Review of Literature 16

2.2.1 Studies on FinTech 16

2.2.2 Studies on Digital Transformation 18

2.3 Digital Transformation: A Conceptual Overview 20

2.4 FinTech Ecosystem 21

2.5 Role of Fintech and Digital Transformation with Respect to Financial Services 24

2.6 Conclusion 27

References 27

3 Reshaping Banking with Digital Technologies 35
Ankita Srivastava and Aishwarya Kumar

3.1 Banking and Artificial Intelligence (AI) 35

3.1.1 Basic Algorithms and Machine Intellect 37

3.1.2 Artificial Common Intelligence 37

3.1.3 Ultra Smart AI 37

3.2 Fintech Evolution 38

3.3 AI Opportunities in Fintech 40

3.3.1 Automation 40

3.3.1.1 Robotic Process Automation (RPA) 40

3.3.1.2 iPaaS 41

3.3.1.3 iSaaS 41

3.3.1.4 Bots 41

3.3.1.5 Enterprise Automation 41

3.3.2 Improved Decision Making 42

3.3.3 Customization 43

3.4 Reshaping the Banking 44

3.4.1 Payments 44

3.4.2 Lending & AI-Based Credit Analysis 46

3.4.3 Wealth Management 48

3.4.3.1 Portfolio Management 48

3.4.3.2 Compliance Management 49

3.4.3.3 Robo-Advisory 50

3.5 Insurance 52

3.6 Challenges Faced by Fintech in Banking 53

3.6.1 Regulatory Compliance 53

3.6.2 Customer Trust 53

3.6.3 Blockchain Integration 53

3.7 Conclusion 54

References 54

4 Adoption of Fintech: A Paradigm Shift Among Millennials as a Next Normal Behaviour 59
Pushpa A., Jaheer Mukthar K. P., Ramya U., Edwin Hernan Ramirez Asis and William Rene Dextre Martinez

4.1 Introduction 60

4.1.1 Evolution of Fintech 61

4.1.2 Technology Innovation in the Financial Sector - Building a Digital Future 63

4.1.3 Taxonomy of Fintech Business Model 66

4.1.4 Fintech Ecosystem 69

4.1.5 Prepositions for Fintech Adoption 72

4.1.6 Challenges of the Fintech Industry 74

4.2 Statement of the Problem and Research Questions 75

4.3 Research Questions and Objectives 76

4.4 Conceptual Framework and Proposed Model 77

4.4.1 Conceptual Framework 77

4.4.2 The TAM Model 77

4.4.3 Proposed Model and Hypothesis Framed 79

4.5 Conclusion 83

Acknowledgement 83

References 83

5 A Comprehensive Study of Cryptocurrencies as a Financial Asset: Major Topics and Market Trends 91
Gioia Arnone and Ajantha Devi Vairamani

5.1 Introduction 91

5.2 Literature Review 92

5.3 Methodology 95

5.4 Findings 96

5.5 Cryptocurrencies as a Major Financial Asset 97

5.6 What is the Value of Cryptocurrencies? Current Market Trends 98

5.7 Conclusion 100

References 101

6 Customers’ Satisfaction and Continuance Intention to Adopt Fintech Services: Developing Countries’ Perspective 105
Song Bee Lian and Liew Chee Yoong

6.1 Introduction 106

6.2 Understanding the Fintech Phenomenon in Developing Countries 108

6.3 Literature Review 109

6.3.1 Technology Acceptance Model (TAM) 109

6.3.2 Customer Satisfaction 110

6.3.3 Customer Innovativeness 110

6.3.4 Hedonic Motivation 111

6.3.5 Perceived Usefulness 111

6.3.6 Perceived Ease of Use 112

6.3.7 System Quality 113

6.3.8 Technology Self-Efficacy 113

6.3.9 Continuance Intention to Adopt 114

6.3.10 Hypothesis Development 114

6.3.11 Conceptual Model 115

6.4 Research Methodology 115

6.4.1 Sample and Data Collection 115

6.4.2 Measures of the Constructs 116

6.4.3 Validity and Reliability Assessment 116

6.5 Results 120

6.5.1 Demographic Profile of the Respondent 120

6.5.2 Structural Model Assessment 120

6.6 Discussion 122

6.7 Theoretical and Practical Implications 126

6.8 Conclusion 127

References 128

7 Fintech Apps: An Integral Tool in Titivating Banking Operations 137
Arun Prakash A., Leelavathi R., Rupashree R. and V.G. Jisha

7.1 Introduction 138

7.2 Objectives 142

7.3 Statement of the Problem 143

7.4 Need for the Study 144

7.5 Review of Literature 144

7.6 Proposed Model 145

7.7 Lending APPS 145

7.8 Investment Apps 145

7.9 Payment Apps 147

7.10 Insurance Apps 148

7.11 Persuading Factors that Increase the Usage of Fin-Tech Apps 149

7.12 Methodology 150

7.13 Results and Discussions 151

7.14 Multiple Linear Regression 152

7.15 Structural Equation Modelling 154

7.16 Conclusion 155

References 155

8 Analytical Study of Fin-Tech in Banking: A Utility Model 157
Neha Kamboj and Mamta Sharma

8.1 Introduction 158

8.2 Literature Analysis and Development of Hypothesis 160

8.2.1 Perceived Utility (PU) and Willingness to Adopt Fin-Tech (WUF) Services 161

8.2.2 Sensible Usability (SU) and Willingness to Use Fin-Tech (WUF) Services 161

8.2.3 Customer Belief (CU) and Willingness to Use Fin-Tech (WUF) Services 162

8.2.4 Social Implications (SI) and Willingness to Use Fin-Tech (WUF)Services 162

8.3 Research Design 163

8.4 Empirical Results 165

8.4.1 Effect of Perceived Utility of Fin-Tech Services (PU) on the Willingness of Customers to Use Fin-Tech (ICUF) Services 168

8.4.2 Effect of Perceived Ease of Use of Fin-Tech Services (PEU) on the Willingness of Customers to Use Fin-Tech (ICUF) Services 168

8.4.3 Effect of Customer Belief in Fin-Tech Services (CU) on the Willingness of Customers to Utilize Fin-Tech (ICUF) Services 168

8.4.4 Social Influence (SI) Impact on the Willingness of Customers to Utilize Fin-Tech (ICUF) Services 169

8.5 Conclusion 169

References 170

9 Is Digital Currency a Payment Disruption Mechanism? 173
Vanishree Mysore Ramkrishna and Vyshnavi Loganathan

9.1 Introduction 173

9.2 Review of Literature 175

9.3 Methodology and Sampling 177

9.4 Results and Discussion 179

9.4.1 Financial Literacy & Inclusion 180

9.4.2 Infrastructure 181

9.4.3 Technical Know-How 183

9.4.4 Trust and Belief 184

9.5 Acceptance of CBDC 185

9.6 Conclusion 188

References 190

10 Investor Sentiment Driving Crypto-Trade in India 193
Sushant Waghmare and Dipesh Uike

10.1 Introduction 194

10.2 Review of Literature 195

10.2.1 Finance & Sentiments 195

10.2.2 Cryptocurrency 196

10.2.3 Addiction or Analysis? 197

10.2.4 Fear of Missing Out (FOMO) 198

10.3 Research Methodology 200

10.3.1 Aim of the Study 200

10.3.2 Objectives of the Study 200

10.3.3 Sampling Methodology & Data Analysis 200

10.3.4 Limitations of the Study 201

10.4 Data Analysis & Interpretation 201

10.5 Conclusions, Suggestions & Recommendations 214

References 217

11 Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective 221
W. Jaisingh, Preethi N. and R. K. Kavitha

11.1 Introduction 222

11.2 State-of-the-Art Review 223

11.3 Application Areas of Cryptocurrencies 224

11.3.1 Fundraising and Investments 224

11.3.2 Freight Transportation and Travel 226

11.3.3 Education 227

11.3.4 Publication and Advertising as Means of Communication 227

11.3.5 E-Commerce and Entertainment 228

11.3.6 Real Estate and Stock Market 228

11.3.7 Trained Financial Planners 228

11.4 Financial Transaction Using Blockchain Technology 229

11.4.1 Introduction 229

11.4.2 Technology Acceptance Model 230

11.4.3 External Constructs 230

11.4.3.1 Trust (T) 230

11.4.3.2 Support (RS) for Regulatory Standards 232

11.4.3.3 Experience (E) 232

11.4.3.4 Social Influence (SI) 233

11.4.3.5 Design (D) 233

11.4.4 Summary 234

11.5 An Analysis of Cryptocurrency Mining Using a Hybrid Approach 234

11.5.1 Introduction 234

11.5.2 Cryptocurrency Mining Strategies 235

11.5.3 Summary and Discussion 236

11.6 Forecasting Cryptocurrency Price Using Convolutional Neural Networks 237

11.6.1 Introduction 237

11.6.2 Assistive Technologies in Machine Learning and Deep Learning 238

11.6.3 Convolutional Neural Networks with Weighted and Attentive Memory Channels 238

11.6.3.1 Attentive Memory Module 239

11.6.3.2 Convolution & Pooling Module 239

11.6.4 Summary and Discussion 239

11.7 Blockchain Technology and Cryptocurrencies for the Collaborative Economy 240

11.7.1 Introduction 240

11.7.2 Collaborative Economy and Digital Platforms 241

11.7.3 Emergence of Collaborative Consumption 242

11.7.3.1 Promoting the Diffusion of Collaborative Practices 242

11.7.4 Summary 242

11.8 Conclusions 243

References 243

12 A Study on the Influence of Personality on Savings and Investment in Cryptos 251
K. Manimekalai, T. Satheeshkumar and G. Manokaran

12.1 Introduction 252

12.2 Literature Review 253

12.2.1 Openness to Experience 254

12.2.2 Conscientiousness 256

12.2.3 Extroversion 257

12.2.4 Aggreeableness 258

12.2.5 Neuroticism 259

12.3 Objectives of the Research 260

12.4 Methodolgy 260

12.5 Discussion 268

References 269

13 Deep Neural Network in Security: A Novel Robust CAPTCHA Model 277
Manasi Chhibber, Rashmi Gandhi, Aakanshi Gupta and Ashok Kumar Yadav

13.1 Introduction 277

13.2 Literature Review 279

13.2.1 Convolutional Neural Networks 280

13.2.2 Transfer Learning 281

13.3 Proposed Approach 283

13.3.1 Data Pre-Processing and Exploratory Analysis 283

13.3.2 Data Acquisition 283

13.4 Results and Discussions 289

13.4.1 Cnn 289

13.4.2 DenseNet 292

13.4.3 MobileNet 295

13.4.4 Vgg 16 297

13.5 Conclusion 300

References 300

14 Customer’s Perception of Voice Bot Assistance in the Banking Industry in Malaysia 303
Manimekalai Jambulingam, Indhumathi Sinnasamy and Magiswary Dorasamy

14.1 Introduction 303

14.2 Problem Statement 304

14.3 What is a Voice Bot? 305

14.3.1 Characteristics of a Voice Bot 307

14.3.2 Why are Voice Bots Becoming Popular? 308

14.3.3 Benefits of a Voice Bot from the Bank’s Perspective 309

14.3.4 Benefits of a Voice Bot from the Customer’s Perspective 311

14.3.5 Opportunities for Voice Bots in Banking 312

14.3.6 Use Cases: Bank Voice Bots Currently Available Around the World 313

14.4 Call to Action 315

14.5 Literature Review 316

14.6 Research Methodology 317

14.7 Descriptive Analysis 318

14.8 Discussion and Conclusion 321

References 322

15 Application of Technology Acceptance Model (TAM) in Fintech Mobile Applications for Banking 325
Tabitha Durai and F. Lallawmawmi

15.1 Introduction 326

15.1.1 Significance of the Study 331

15.1.2 Research Objectives 331

15.1.3 Hypotheses of the Study 332

15.1.3.1 Perceived Usefulness Toward the Usage of Fintech 332

15.1.3.2 Brand Image Toward the Usage of Fintech 333

15.1.3.3 Perceived Risks Toward the Usage of Fintech 333

15.2 Methods and Measures 334

15.2.1 Instrument Development 335

15.3 Results 336

15.3.1 Demographic Profile of the Respondents 336

15.3.2 Factors Influencing Fintech under Technology Acceptance Model (TAM) 338

15.3.2.1 Prominent Factors under Technology Acceptance Model (TAM) that Influence the Usage of Fintech 340

15.3.3 Technology Acceptance Model and the Usage of Fintech 340

15.3.3.1 Perceived Usefulness Toward the Usage of Fintech 340

15.3.3.2 Brand Image Toward the Usage of Fintech 342

15.3.3.3 Perceived Risks Toward the Usage of Fintech 343

15.4 Discussion 344

15.5 Conclusion 346

References 347

16 Upsurge of Robo Advisors: Integrating Customer Acceptance 351
C. Nagadeepa, Reenu Mohan, Antonio Peregrino Huaman Osorio and Willian Josue Fernandez Celestino

16.1 Introduction 351

16.2 Chatbots 355

16.2.1 Benefits of Using Chatbots in Banks 355

16.3 Robo-Advisor 356

16.3.1 Robo-Adviser – A Brief History 357

16.3.2 Historic Account of Robo Advisors 358

16.3.3 Robo-Advisor Versus FA (Financial Advisor) 359

16.3.4 Robo Advisors in Market 359

16.3.5 How Robo-Advisors Work? 364

16.3.6 Types of Robo-Advisor 365

16.3.7 Top Robo-Advisors 367

16.3.8 Points to be Consider while Selection of Robo-Advisor 371

16.3.9 Benefits of Robo-Advisors 372

16.3.10 Limitations of Robo-Advisors 373

16.4 Acceptance of Robo-Advisor 374

16.4.1 Theoretical Background and Research Propositions 374

16.4.2 A Glimpse of Earlier Research Studies 375

16.4.3 Methodology and Hypothesis 376

16.4.4 Collection of Data 377

16.4.5 Hypotheses Testing 377

16.5 Conclusion 379

References 379

17 Super Apps: The Natural Progression in Fin-Tech 383
Kavitha D., Uma Maheswari B. and Sujatha R.

17.1 Introduction 383

17.2 Journey from an App to a Super App 385

17.3 Super App vs. A Vertically Integrated App 385

17.4 Architecture and Design of Super Apps 386

17.4.1 Monolithic Architecture 388

17.4.2 Modular Architecture 388

17.4.3 Microservices Architecture 389

17.5 Business Models of Super Apps 390

17.6 The Super App Market Space and the Business Models 391

17.6.1 WeChat 391

17.6.2 Alipay 393

17.6.3 Grab 394

17.6.4 Paytm 395

17.6.5 The Latest Entrant: Tata Neu 396

17.6.6 Other Players 396

17.7 Factors Contributing to the Success of Super Apps 397

17.8 Super Apps in Fin-Tech and their Role in the Financial Services Segment 400

17.9 Role of Super Apps in Financial Inclusion 403

17.10 Benefits of Super Apps in the Financial Services Sector 405

17.10.1 Economies of Scale & Cost of Financial Intermediation 405

17.10.2 Size & Speed of Product and Service Offerings 406

17.10.3 The Power of Data and the Speed of Responsiveness 406

17.11 Risks due to Super Apps in the Financial System 406

17.11.1 Financial Stability 407

17.11.2 Market Concentration 407

17.11.3 Dis-Intermediation of Banks from Customer 408

17.12 Regulatory Measures to Mitigate the Risks 408

17.13 The Future of Super Apps 408

17.14 Conclusion 410

References 411

Index 413