Artificial Intelligence, Fintech, and Financial Inclusion

Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Contents
Editor Biographies
Contributors
Foreword
Preface
1. Big Data and Artificial Intelligence for Financial Inclusion: Benefits and Issues
1.1 Introduction
1.2 Literature Review
1.3 How AI Can Help Expand Access to Banking Services/Financial Inclusion
1.3.1 Big Data and Data Analytics Advantages for Financial Inclusion
1.3.1.1 Big Data Facilitates the Creation of Credit Scores for the Excluded Population
1.3.1.2 Big Data Enables Financial Services Companies to More Effectively Manage Credit Risk
1.3.1.3 Greater Identity Solutions Are Offered by Big Data Considerably More Effectively Than by Know-Your-Customer (KYC) Regulations
1.3.1.4 Improved Marketing of Financial Services
1.3.1.5 Big Data Can provide inputs that Help Policies and Strategies for Financial Inclusion
1.3.2 Additional Advantages of AI in the Financial Inclusion Sector
1.3.2.1 AI Can Make It Easier for Adults without Bank Accounts to Open Accounts
1.3.2.2 AI Models Can Provide Customers with Smart and Individualized Financial Goods and Services
1.3.2.3 AI Will Enhance Communication and Customer Service
1.3.2.4 AI Will Assist in Reducing Fraud
1.3.2.5 AI Helps Establish a Credit History
1.4 A Few Problems
1.4.1 AI Might Keep Weak People Out of the Financial System
1.4.2 Unconscious Bias Is Incorporated into the Creation of AI Tools, Models, and Applications
1.4.3 Job Losses or Employment Transfers
1.4.4 The Fear of Entrusting AI Systems with Decision-Making
1.4.5 AI Algorithms Might Not Have Been Adequately Trained with Data
1.4.6 Lack of Skilled AI Employees
1.4.7 The Board's Approval for the AI's Inclusion in Operational Procedures Is Not Guaranteed
1.4.8 Handling Inaccurate Data Is a Problem
1.4.9 Privacy and Security Concerns with Client Data
1.5 Conclusion
References
2. The Contribution of AI-Based Analysis and Rating Models to Financial Inclusion: The Lenddo Case for Women-Led SMEs in Developing Countries
2.1 Introduction
2.1.1 Review of Literature: AI and Creditworthiness Analysis
2.1.1.1 AI and Productivity Gains in Credit Analysis and Scoring Procedures
2.1.1.2 The Contribution of Big Data to the AI Process for Credit Analysis
2.1.1.3 The Socio-economic Impact of AI in Strengthening Inclusion
2.1.2 The Research Methodology of Case Study: Lenddo's Universal Credit
2.1.3 The Limits of the IA Approach for Credit Analysis
2.2 Conclusion
Notes
References
3. Is the Capital Market Based on Blockchain Technology Efficient for Financial Inclusion?
3.1 Introduction
3.2 Conceptual and Theoretical Framework
3.2.1 The Influence of Fintech and Blockchain Technology on the Capital Market Efficiency
3.2.2 The Impact of Fintech and Blockchain Technology on the Financial Inclusion
3.3 Methodological Approach: Toward a New Conceptual Model
3.4 Conceptual Analysis: Blockchain Technology, Market Efficiency, and Financial Inclusion
3.5 Toward a New Conceptual Model: Discussion and Conclusion
Notes
References
4. Exploring the Regulatory Contexts of Fintech Innovation for Financial Inclusion: The Case of Distributed Ledger Technologies in India
4.1 Introduction
4.2 Balancing Innovation and Risk - A Review of Literature on the Global Experience
4.3 Challenges to Fintech Regulation
4.4 Fintech Policy and Regulation in India - The Case of India
4.5 Conclusion
Notes
References
5. Financial Inclusion through the Sphere of Solidarity in Corporate Governance: The Cases of Digital Crowdfunding and Conventional Microfinance
5.1 Introduction
5.1.1 Case Studies of Conflict between Financial Interests and Social Values
5.1.1.1 Banco Compartamos: The Case of Helping Low-income Entrepreneurs
5.1.1.2 SKS Microfinance: The Case of Two Visions of Helping Low-income Entrepreneurs
5.1.1.3 Oculus Rift: The Case of Forgetting the First Supporters
5.1.1.4 Minecraft: The Case of Excluding the Enthusiastic Community
5.1.1.5 Case Study Analysis Through an Interpretive Lens
5.2 Literature Review: Corporate Governance and the Sphere of Solidarity
5.2.1 Corporate Governance
5.2.2 The Sphere of Solidarity
5.2.3 Combining Corporate Governance and the Sphere of Solidarity
5.3 Discussion: The Sphere of Solidarity in Crowdfunding and Microfinance
5.3.1 Practical Steps to Implementing the Sphere of Solidarity
5.4 Conclusion
References
6. Why Do Bank Customers Adopt FinTech Solution: The Case of India
6.1 Introduction
6.2 Literature Review and Hypothesis Development
6.2.1 Perceived Usefulness (PU) and Intention to Use FinTech (IUF) Services
6.2.2 Perceived Ease of Use (PEU) and Intention to Use FinTech (IUF) Services
6.2.3 The Trust of Customers (CU) and Intention to Use FinTech (IUF) Services
6.2.4 Social Influence (SOI) and Intention to Use FinTech (IUF) Services
6.3 Methodology
6.4 Empirical Results
6.4.1 Effect of Perceived Usefulness of FinTech Services (PU) on the Intention of Customers to Use FinTech (ICUF) Services
6.4.2 Effect of Perceived Ease of Use of FinTech Services (PEU) on the Intention of Customers to Use FinTech (ICUF) Services
6.4.3 Effect of Customer Trust in FinTech Services (CU) on the Intention of Customers to Use FinTech (ICUF) Services
6.5 Conclusion
Notes
References
7. A Scientometric and Bibliometric Review of Impacts and Application of Artificial Intelligence and Fintech for Financial Inclusion
7.1 Introduction
7.2 Literature Review
7.2.1 Financial Inclusion
7.2.2 Fintech
7.2.3 Artificial Intelligence (AI)
7.2.4 Conceptual Structure
7.3 Methodology
7.3.1 Data Collection
7.3.2 Data Analysis
7.3.3 Data Visualization and Interpretation
7.4 Results and Analyses
7.4.1 Citation Structure and Publications Trend
7.4.2 Journals
7.4.3 Most Relevant Authors
7.4.4 Most Influential Articles
7.4.5 Countries
7.5 Scientometric Analysis
7.5.1 Authors Keywords
7.5.2 Co-Citation Network
7.5.3 Topic Dendrogram
7.5.4 Thematic Map
7.6 Discussion
7.7 Conclusions
7.7.1 Future Research Directions
References
8. The Role of Islamic Fintech in Indonesia to Improve Financial Inclusion for Resolving SDGs
8.1 Introduction
8.2 Theoretical Background
8.2.1 Financial Technology
8.2.2 Financial Inclusion
8.2.2.1 SDGs
8.3 Methodology
8.4 Results
8.5 Conclusions
8.6 Limitation of the Research
8.7 Direction for Further Research and Implication
References
9. Challenges of Artificial Intelligence Adoption for Financial Inclusion
9.1 Introduction
9.2 Literature Review: The Challenges of AI in Provision of Financial Products and Services
9.3 The Methodology of Research Study
9.3.1 Setting Inclusion Criteria
9.3.2 Collecting Documents
9.3.3 Document Coding and Analysis
9.3.4 Theory Developed from Literature
9.3.5 Validity of Findings
9.4 Results and Discussion
9.4.1 Surfacing the Narratives: The Open Codes
9.4.2 Digital Financial Inclusion
9.4.3 Competitive Advantage to Big Companies
9.4.4 Risks Due to Market Concentration
9.4.5 ML Model Operationalization and Upkeep
9.4.6 Lack of Explain Ability of AI Models
9.4.7 AI Models' Opacity
9.4.8 Technology Rabbit Hole
9.4.9 Lack of Domain Expertise
9.4.10 Exploitation of Consumer Data
9.4.11 Protecting Consumer Information
9.4.12 Noncompliance
9.4.13 Budget Constraints
9.4.14 Lack of Accountability
9.4.15 Return on Investment
9.4.16 Data Quality
9.4.17 Data Accuracy
9.4.18 Data Relevance
9.4.19 Biased or Discriminatory Outcomes of AI Models
9.4.20 Structure-Related Issues
9.4.21 Models That Are Biased and Prejudiced
9.4.22 Inaccurate ML-Based Scoring Due to Faking Indicators by Consumers
9.4.23 Attack on Availability
9.4.24 Investigating the Narratives Choosing Codes
9.4.25 Digital Divide Possibility
9.4.26 Challenges in Coordination and Communication
9.4.27 Morality and Ethics
9.4.28 Financial Limitations
9.4.29 Data Reliability
9.4.30 Cybersecurity
9.4.31 Stitching the Narratives
9.4.32 Conclusions and Recommendations
9.5 Limitations
References
Index