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Generative Artificial Intelligence in Finance
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Generative Artificial Intelligence in Finance

by Pethuru Raj Chelliahk

Artificial Intelligence

Book Details

Book Title

Generative Artificial Intelligence in Finance

Author

Paul Papanek Stork

Publisher

Willy

Publication Date

2025

ISBN

9781394271047

Number of Pages

482

Language

English

Format

PDF

File Size

3.1MB

Subject

Artificial Intelligence

Table of Contents

  • Part I: Foundations and Applications of AI in Finance
  • Chapter 1: Artificial Intelligence Application and Research in Accounting, Finance, Economics, Business, and Management
  • 1.1 Introduction
  • 1.2 Literature Review
  • 1.3 Applications in Accounting
  • 1.4 Applications in Finance
  • 1.5 Applications in Economics
  • 1.6 Applications in Business and Management
  • 1.7 Risks of AI
  • 1.8 Conclusion
  • 1.9 References
  • Chapter 2: Automating Data Entry in the Indian Banking Industry Through Generative AI
  • 2.1 Introduction
  • 2.2 Literature Review
  • 2.3 Methodology
  • 2.4 Data Entry Automation with Generative AI
  • 2.5 Results and Analysis
  • 2.6 Discussion
  • 2.7 Conclusion
  • 2.8 References
  • Chapter 3: Future Approach: Generative AI, Stylized Architecture, and Its Potential in Finance
  • 3.1 Introduction
  • 3.2 Risk Considerations
  • 3.3 Generative AI Applications in Finance
  • 3.4 Significant Challenges
  • 3.5 Generative AI Architecture
  • 3.6 Conclusion
  • 3.7 References
  • Chapter 4: Generative Artificial Intelligence (GAI) for Accurate Financial Forecasting
  • 4.1 Introduction
  • 4.2 Literature Review
  • 4.3 Methodology
  • 4.4 Performance Metrics
  • 4.5 Forecasting Algorithm
  • 4.6 Analysis of Results
  • 4.7 Conclusion
  • 4.8 References
  • Chapter 5: The Far-Reaching Impacts of Emerging Technologies in Accounting and Finance
  • 5.1 Introduction
  • 5.2 Study Objectives
  • 5.3 Definition and Elements of AI
  • 5.4 Applications in Accounting and Finance
  • 5.5 Blockchain in Finance
  • 5.6 Robotic Process Automation
  • 5.7 Big Data Analytics
  • 5.8 Integration of Technologies
  • 5.9 Ethical and Privacy Considerations
  • 5.10 Emerging Trends
  • 5.11 Conclusion
  • 5.12 Bibliography
  • Part II: Generative AI in Risk Management and Fraud Detection
  • Chapter 6: Deep Diving into Financial Frauds
  • 6.1 Introduction and Background
  • 6.2 Ad-Click Fraud Detection
  • 6.3 Credit Card Fraud Detection
  • 6.4 Document Dispensation Fraud Detection
  • 6.5 Cross-Domain Analysis
  • 6.6 Ethical and Privacy Considerations
  • 6.7 Advancements in AI/ML
  • 6.8 Challenges and Risks
  • 6.9 Conclusion and Future Scope
  • 6.10 References
  • Chapter 7: Generative AI: A Transformative Tool for Mitigating Risks for Financial Frauds
  • 7.1 Introduction
  • 7.2 Characteristics of Generative AI
  • 7.3 AI Types in Financial Assets
  • 7.4 Risks and Mitigation Strategies
  • 7.5 Employee Training and Regulation
  • 7.6 Hallucination and Training Issues
  • 7.7 Future Research
  • 7.8 Conclusion
  • 7.9 References
  • Chapter 8: Innovation Unleashed: Generative AI in Risk Evaluation
  • 8.1 Introduction
  • 8.2 New Challenges and Roles
  • 8.3 Reviews and Findings
  • 8.4 Conclusion
  • 8.5 Future Research
  • 8.6 References
  • Chapter 9: The Significance of Generative AI in Fraud Detection Within Banking
  • 9.1 Introduction
  • 9.2 Literature Review
  • 9.3 Applications in Banking Fraud
  • 9.4 Case Studies
  • 9.5 Ethical Considerations
  • 9.6 Future Directions
  • 9.7 Conclusion
  • 9.8 Recommendations
  • 9.9 References
  • Chapter 10: Role of Generative AI for Fraud Detection and Prevention
  • 10.1 Introduction
  • 10.2 Understanding Fraud
  • 10.3 Generative AI Fundamentals
  • 10.4 Applications and Use Cases
  • 10.5 Benefits and Challenges
  • 10.6 Conclusion
  • 10.7 References
  • Part III: Ethical, Legal, and Regulatory Considerations
  • Chapter 1: Ethical and Regulatory Compliance in Human Resources
  • 1.1 Introduction
  • 1.2 Importance of Compliance
  • 1.3 Literature Review
  • 1.4 Methodology
  • 1.5 Ethical Implications in HR
  • 1.6 Ensuring Legal Standards
  • 1.7 Best Practices and Strategies
  • 1.8 Discussion
  • 1.9 Conclusion
  • 1.10 References
  • Chapter 2: Navigating the Frontier of Finance
  • 2.1 Introduction and Background
  • 2.2 Generative AI Concepts
  • 2.3 Risks and Challenges
  • 2.4 Methodology and Data Analysis
  • 2.5 Results and Discussion
  • 2.6 Conclusion
  • 2.7 References
  • Chapter 3: Ensuring Compliance and Ethical Standards in Fintech
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Methodology
  • 3.4 Case Study and Findings
  • 3.5 Conclusion
  • 3.6 References
  • Chapter 4: Privacy Laws and Leak of Financial Data
  • 4.1 Introduction
  • 4.2 Case Study
  • 4.3 Background and Resolution
  • 4.4 Conclusion
  • 4.5 References
  • Chapter 5: Ethics and Laws Governing AI in Financial Systems
  • 5.1 Introduction
  • 5.2 AI Applications in Finance
  • 5.3 Ethical Challenges
  • 5.4 Indian Case Studies
  • 5.5 Conclusion
  • 5.6 References
  • Part IV: Industry-Specific Applications and Innovations
  • Chapter 1: Generative AI Tools for Product Design and Engineering
  • 1.1 Introduction
  • 1.2 Ideation and Optimization
  • 1.3 Customization and Prototyping
  • 1.4 Collaboration and Simulation
  • 1.5 Regulatory and Cost Aspects
  • 1.6 Market Insights
  • 1.7 Conclusion
  • 1.8 References
  • Chapter 2: AI-Driven Generative Design in Engineering
  • 2.1 Introduction
  • 2.2 Evolution of AI in Design
  • 2.3 Fundamentals of Generative AI
  • 2.4 Generative Design Applications
  • 2.5 Case Studies
  • 2.6 Ethical Issues and Future Trends
  • 2.7 Conclusion
  • 2.8 References
  • Chapter 3: Blockchain Transforming Indian Insurance Industry
  • 3.1 Introduction
  • 3.2 Importance of Blockchain
  • 3.3 Application in Insurance
  • 3.4 Industry Challenges and Regulation
  • 3.5 Prospects and Conclusion
  • 3.6 References
  • Chapter 4: Explainable AI in Fintech
  • 4.1 Introduction
  • 4.2 Explainable AI Concepts
  • 4.3 Financial Predictive Analysis
  • 4.4 Regulatory Compliance
  • 4.5 Conclusion and Future Scope
  • 4.6 References
  • Chapter 5: Empowering Financial Efficiency in India with AI
  • 5.1 Introduction and Background
  • 5.2 AI Integration in Finance
  • 5.3 Benefits and Challenges
  • 5.4 Future Prospects and Trends
  • 5.5 Insights for Stakeholders
  • 5.6 Conclusion
  • 5.7 References
  • Chapter 6: Framework and Interface of AI Systems in Indian Banking
  • 6.1 Introduction and Objectives
  • 6.2 Literature Review
  • 6.3 AI System Framework
  • 6.4 Interface Design
  • 6.5 Industry Impact
  • 6.6 Regulatory Environment
  • 6.7 Case Studies
  • 6.8 Future Trends
  • 6.9 Conclusion
  • 6.10 References
  • Chapter 7: Generative AI in Engineering and Product Design
  • 7.1 Introduction
  • 7.2 Working Principles and Benefits
  • 7.3 Techniques and Data Needs
  • 7.4 Applications and Prototyping
  • 7.5 Simulation and Optimization
  • 7.6 Human-AI Collaboration
  • 7.7 Challenges and Future Developments
  • 7.8 References
  • Chapter 8: Innovation Unleashed: Generative AI in Risk Evaluation
  • 8.1 Introduction
  • 8.2 New Challenges and Roles
  • 8.2.1 Challenges
  • 8.2.2 Roles of AI
  • 8.3 Review of Literature and Findings
  • 8.3.1 Literature Review
  • 8.3.2 Key Findings
  • 8.4 Discussion and Synthesis
  • 8.5 Conclusion and Future Research Directions
  • 8.5.1 Conclusion
  • 8.5.2 Future Research
  • 8.6 References
  • Chapter 9: The Significance of Generative AI in Fraud Detection Within Banking
  • 9.1 Introduction
  • 9.2 Review of Literature
  • 9.3 Applications of Generative AI in Banking Fraud Detection
  • 9.3.1 Overview of Generative AI
  • 9.3.2 Fraud Scenarios in Banking
  • 9.3.3 Applications and Use Cases
  • 9.4 Case Studies on Generative AI in Fraud Detection
  • 9.5 Ethical and Regulatory Considerations
  • 9.6 Future Directions for Research
  • 9.7 Conclusion
  • 9.8 Recommendations for Practitioners and Policymakers
  • 9.9 References
  • Chapter 10: Role of Generative AI for Fraud Detection and Prevention
  • 10.1 Introduction
  • 10.2 Understanding Fraud in the Context of Financial Transactions
  • 10.3 Fundamentals of Generative AI and Its Relevance
  • 10.4 Applications and Use Cases of Generative AI in Fraud Detection and Prevention
  • 10.5 Benefits and Challenges of Implementing Generative AI
  • 10.6 Conclusion
  • 10.7 References
  • Part III: Ethical, Legal, and Regulatory Considerations
  • Chapter 11: Ethical and Regulatory Compliance Challenges of Generative AI in Human Resources
  • 11.1 Introduction
  • 11.2 Importance of Compliance and Ethical Considerations
  • 11.3 Research Objectives and Methodology
  • 11.4 Literature Review
  • 11.4.1 The Role of AI in HR: Automation, Decision-Making, and Augmentation
  • 11.4.2 Ethical Concerns in AI and HR: Bias, Discrimination, and Fairness
  • 11.4.3 Legal Frameworks and Regulations: GDPR, EEOC, and Other Relevant Laws
  • 11.4.4 Transparency, Explainability, and Accountability in AI
  • 11.5 Methodology
  • 11.5.1 Explanation of the Secondary Data Analysis Approach
  • 11.5.2 Data Sources: Existing Research Papers, Case Studies, Reports, and Relevant Databases
  • 11.5.3 Data Collection and Selection Criteria
  • 11.5.4 Data Analysis Techniques (Content Analysis, Thematic Analysis, etc.)
  • 11.6 Ethical Implications of Generative AI in HR
  • 11.6.1 Bias and Discrimination in Hiring and Employee Management
  • 11.6.2 Privacy Concerns and Data Protection
  • 11.6.3 The Impact of AI on Diversity and Inclusion Efforts
  • 11.6.4 Stakeholder Perspectives on AI Ethics
  • 11.7 Ensuring Compliance with Legal Standards
  • 11.7.1 GDPR and Data Privacy Requirements
  • 11.7.2 Equal Employment Opportunity (EEO) Laws and Regulations
  • 11.7.3 Auditing and Reporting Mechanisms
  • 11.7.4 The Role of HR Professionals and Legal Advisors
  • 11.8 Best Practices and Strategies
  • 11.8.1 Regular Auditing and Bias Mitigation
  • 11.8.2 Employee Training and Awareness Programs
  • 11.8.3 Collaborative Efforts Between HR and IT Teams
  • 11.9 Discussion
  • 11.9.1 Synthesis of Findings
  • 11.9.2 Identification of Key Challenges and Opportunities
  • 11.9.3 Recommendations for HR Practitioners and Policymakers
  • 11.10 Conclusion
  • 11.11 Summary of Main Findings
  • 11.12 Significance of Ethical AI in HR Practices
  • 11.13 Future Research Directions and Potential Advancements
  • References
  • Chapter 12: Navigating the Frontier of Finance
  • 12.1 Introduction and Background
  • 12.2 Generative AI Concepts in Finance
  • 12.3 Risks and Challenges Associated with Generative AI
  • 12.4 Methodology and Data Analysis
  • 12.5 Results and Discussion
  • 12.6 Conclusion
  • 12.7 References
  • Chapter 13: Ensuring Compliance and Ethical Standards with Generative AI in Fintech: A Multi-Dimensional Approach
  • 13.1 Introduction to Generative AI in Fintech
  • 13.2 Literature Review
  • 13.3 Methodology
  • 13.4 Case Study
  • 13.5 Findings
  • 13.6 Conclusion
  • References
  • Chapter 14: Privacy Laws and Leak of Financial Data in the Era of Generative AI
  • Introduction
  • Case Study
  • Background
  • Issue
  • Impact
  • Response
  • Resolution
  • Conclusion
  • References
  • Chapter 15: Ethics and Laws: Governing Generative AI’s Role in Financial Systems
  • Introduction
  • Applications of AI in Financial Systems
  • Ethical Challenges
  • Ethical AI in Indian Finance: Case Studies and Insights
  • Conclusion
  • References
  • Chapter 16: Generative AI Tools for Product Design and Engineering
  • 16.1 Introduction
  • 16.2 Concept Generation and Ideation
  • 16.3 Topology Optimization
  • 16.4 Design Customization
  • 16.5 Rapid Prototyping and Iteration
  • 16.6 Multi-Objective Optimization
  • 16.7 AI-Powered Collaboration
  • 16.8 Material Selection and Integration
  • 16.9 Generative Simulations and Testing
  • 16.10 Generative Design for Additive Manufacturing
  • 16.11 Sustainability and Environmental Impact
  • 16.12 Regulatory Compliance and Standards
  • 16.13 Cost Optimization
  • 16.14 Market Trends and Consumer Insights
  • 16.15 Conclusion
  • References
  • Chapter 17: AI-Driven Generative Design Redefines the Engineering Process
  • 17.1 Introduction
  • 17.1.1 Overview of Generative AI
  • 17.1.2 Evolution of AI in Product Design and Engineering
  • 17.1.2.1 Emergence of Computational Tools
  • 17.1.2.2 Rule-Based Expert Systems
  • 17.1.2.3 Rise of Machine Learning
  • 17.1.2.4 Neural Networks and Deep Learning
  • 17.1.2.5 Generative AI in Design
  • 17.1.2.6 Integrating AI Across the Product Lifecycle
  • 17.1.3 Scope and Objectives
  • 17.1.3.1 Objectives
  • 17.2 Literature Survey
  • 17.3 Fundamentals of Generative AI
  • 17.3.1 Basics of Machine Learning
  • 17.3.2 Deep Learning and Neural Networks
  • 17.3.2.1 Architecture of Neural Networks
  • 17.3.2.2 Training Neural Networks
  • 17.3.3 Generative Models
  • 17.4 Generative Design in Product Development
  • 17.4.1 Design Space Exploration
  • 17.4.1.1 Rapid Iteration
  • 17.4.1.2 Diverse Concept Generation
  • 17.4.2 Customization and Personalization
  • 17.4.2.1 Optimization and Performance Enhancement
  • 17.4.2.2 Optimization Techniques
  • 17.4.3 AI-Driven Simulation and Prototyping
  • 17.5 Case Studies
  • 17.5.1 Ethical and Legal Considerations
  • 17.5.2 Future Trends and Emerging Technologies
  • 17.6 Conclusions
  • References
  • Chapter 18: Insurance Disruption: Analytics on Blockchain Transforming Indian Insurance Industry
  • Introduction
  • Blockchain Technology
  • Why is Blockchain Important?
  • Enabling Industry Collaboration
  • Blockchain and Insurance
  • What is it?
  • Where it is Applicable?
  • How will it Benefit?
  • Insurance Sector: India
  • Challenges
  • Blockchain and the Insurance Regulatory Framework
  • Prospects
  • Conclusion
  • References
  • Chapter 19: Application of Explainable Artificial Intelligence in Fintech
  • 19.1 Introduction
  • 19.2 The Current Landscape of Explainable Artificial Intelligence (XAI)
  • 19.2.1 Explainable Artificial Intelligence
  • 19.2.2 Working of Various Kinds of XAI Models
  • 19.2.3 Advancement in Fintech
  • 19.3 Advancing Financial Predictive Analysis
  • 19.3.1 Bankruptcy and Credit Risk Prediction
  • 19.4 Advancements of XAI in Financial Predictions
  • 19.4.1 Bankruptcy Prediction
  • 19.4.2 Credit Card Approval Prediction
  • 19.5 Conclusion and Future Scope
  • 19.5.1 Theoretical Implications
  • 19.5.2 Implications for Other Researchers
  • 19.5.3 Future Scope
  • References
  • Chapter 20: Empowering Financial Efficiency in India
  • 20.1 Introduction
  • 20.1.1 Background of AI in Accounting and Finance
  • 20.1.2 Significance of AI in Indian Finance
  • 20.1.2.1 Enhanced Customer Experience
  • 20.1.2.2 Fraud Detection and Prevention
  • 20.1.2.3 Credit Scoring and Risk Management
  • 20.1.2.4 Algorithmic Trading and Investment Management
  • Chapter 21: Framework and Interface: The Backbone of AI Systems in Banking in India
  • 21.1 Introduction
  • 21.1.1 Background
  • 21.1.2 Objectives
  • 21.1.3 Scope
  • 21.2 Literature Review
  • 21.2.1 Evolution of AI in Banking
  • 21.2.2 AI Applications in Indian Banking
  • 21.2.3 Challenges and Opportunities
  • 21.3 Framework of AI Systems in Banking
  • 21.3.1 Data Acquisition and Management
  • 21.3.2 Machine Learning Models
  • 21.3.3 Natural Language Processing (NLP) Integration
  • 21.3.4 Robotic Process Automation (RPA)
  • 21.3.5 Security and Compliance
  • 21.4 Interface Design for AI Systems
  • 21.4.1 User-Friendly Interfaces
  • 21.4.2 Personalization and Customer Experience
  • 21.4.3 Customer Service Chatbots
  • 21.4.4 Data Visualization
  • 21.5 Impact of AI in Indian Banking
  • 21.5.1 Improved Efficiency and Productivity
  • 21.5.2 Enhanced Customer Experiences
  • 21.5.3 Risk Management
  • 21.5.4 Fraud Detection and Prevention
  • 21.6 Regulatory Environment
  • 21.6.1 RBI Guidelines on AI in Banking
  • 21.6.2 Data Privacy and Security Regulations
  • 21.6.3 Ethical Considerations
  • 21.7 Case Studies
  • 21.7.1 HDFC Bank
  • 21.7.2 ICICI Bank
  • 21.7.3 State Bank of India (SBI)
  • 21.8 Future Trends
  • 21.8.1 AI Adoption in Rural Banking
  • 21.8.2 Integration of Blockchain and AI
  • 21.8.3 AI in Wealth Management
  • 21.9 Conclusion
  • 21.9.1 Summary of Key Findings
  • 21.9.2 Implications for the Banking Industry
  • 21.9.3 Recommendations for Future Research
  • References
  • Chapter 22: Harnessing Generative AI for Engineering and Product Design: Conceptualization, Techniques, Advancements and Challenges
  • 22.1 Introduction to Generative AI
  • 22.2 Working on Generative AI
  • 22.3 Benefits of Generative AI
  • 22.3.1 Rapid Conceptualization
  • 22.3.2 Enhanced Creativity
  • 22.3.3 Optimization and Simulation
  • 22.3.4 Time and Cost Efficiency
  • 22.3.5 Iterative Improvement
  • 22.4 Generative AI Technique
  • 22.4.1 Generative Adversarial Networks (GANs)
  • 22.4.2 Variational Autoencoders (VAEs)
  • 22.4.3 Recurrent Neural Networks (RNNs)
  • 22.4.4 Long Short-Term Memory Networks (LSTMs)
  • 22.5 Data Requirements
  • 22.5.1 Diverse Data Sources
  • 22.5.2 High-Quality Training Data
  • 22.5.3 Data Labeling and Annotation
  • 22.5.4 Metadata Integration
  • 22.5.5 Data Security and Privacy
  • 22.5.6 Scalability
  • 22.6 Applications in Concept Generation
  • 22.6.1 Automated Design Drafting
  • 22.6.2 Material Selection
  • 22.6.3 User-Centric Design
  • 22.6.4 Performance Optimization
  • 22.6.5 Applications of Generative AI in Product Design Phases
  • 22.7 Prototyping and Iteration
  • 22.7.1 Streamlined Prototyping
  • 22.7.2 Design Exploration
  • 22.7.3 Iterative Refinement
  • 22.7.4 Cost-Efficient Development
  • 22.8 Optimization and Simulation
  • 22.8.1 Design Optimization
  • 22.8.2 Material Optimization
  • 22.8.3 Performance Simulation
  • 22.8.4 Environmental Simulation
  • 22.9 Significance of Human-AI Collaboration
  • 22.9.1 Complementary Expertise
  • 22.9.2 Efficiency and Ideation
  • 22.9.3 Iterative Design
  • 22.9.4 Design Validation
  • 22.10 Challenges and Limitations
  • 22.10.1 Challenges
  • 22.10.2 Limitations
  • 22.11 Future Trends and Developments
  • 22.11.1 Advancements in Algorithmic Complexity
  • 22.11.2 Multidisciplinary Integration
  • 22.11.3 Human-AI Collaboration
  • 22.11.4 Ethical and Regulatory Considerations
  • References