Federated Learning in Financial Services by Sharma, Suvarna; Kumar, Jeetendra; Gupta, Rashmi;

Short description:

This book reviews the increasing challenges of data privacy and regulatory constraints in the financial industry, highlighting how federated learning (FL) enables secure AI-driven financial services. Federated architectures, secure multi-party computation, differential privacy, and homomorphic encryption are covered. 

Long description:

As financial institutions increasingly rely on AI and ML for data-driven decision-making, concerns about data privacy, security, and regulatory compliance are growing. Federated learning (FL) emerges as a key solution, enabling collaborative AI model training across multiple organizations without sharing raw data. This book explores advancements in FL technology in the financial industry, specifically in the context of privacy-preserving AI. It also examines the significant shift from traditional centralized machine-learning approaches to decentralized learning techniques.

Structured into four comprehensive sections, the book offers an in-depth examination of the subject. The first section provides an overview and introduction to FL, and reviews the increasing challenges of data privacy and regulatory constraints in the financial industry, highlighting how federated learning enables secure AI-driven financial services. The second section explores federated architectures, secure multi-party computation, differential privacy, and homomorphic encryption. The third section highlights practical applications of AI and federated learning in areas such as risk management, fraud detection, credit scoring, and customer personalization, demonstrating how FL enhances security, scalability, and operational efficiency in financial systems. Financial applications where federated learning enhances security, scalability, and efficiency are also addressed. The fourth section discusses emerging trends in federated learning, including blockchain-based federated learning, zero-trust architectures, and its integration with decentralized finance (DeFi). The book concludes by examining practical implementations and regulatory considerations, ensuring compliance with data protection laws such as GDPR and CCPA.

Table of Contents:

Preface. Series Preface. 1. Introduction to Federated Learning in Financial Services. 2. Federated Learning Demystified: Core Concepts, Working, Benefits, and Challenges. 3. Federated Learning for Financial Data Privacy and Security in Secure AI Systems. 4. Governance and Data Management in Federated Learning. 5. Credit Risk Assessment and Scoring Models. 6. Fraud Detection and Anti-Money Laundering (AML). 7. Wealth Management and Investment Strategies. 8. Building Federated Learning Infrastructure in Finance. 9. Federated Learning for Financial Data Privacy and Security Services. 10. Cross-Border Collaboration and Data Exchange. 11. Federated Learning for ESG Disclosure Analytics: A Study on NSE Sectoral Indices. 12. Application of Machine Learning for Detecting Financial Irregularities in Local Government Revenue Collection Information System. 13. Regulatory and Ethical Considerations in Federated Finance. 14. Emerging Trends and Future Innovations. 15. Evaluation of Logistic Regression and Decision Tree Classifiers to Detect Smurf Attacks on a Network Interface. 16. Protecting the Sensitive Data: Federated Learning System Privacy Improvements.