Federated Learning for Financial Fraud Detection

Federated Learning (FL) for detecting financial fraud is the central theme of this study. Modern FinTech ecosystems increasingly face the need to fight fraud by collaborating. The chapter addresses growing privacy concerns and data silo challenges in decentralized financial systems. Innovative methods that enable collaborative model training without compromising sensitive customer information are discussed. Quantitative analyses are employed to evaluate model performance under regulatory constraints. Data privacy, secure aggregation, and adaptive communication protocols are implemented in experimental setups. It discusses challenges such as non-IID data and adversarial attacks. Significant improvements in detection accuracy and reduced false positives are reported. The study offers strategic insights that benefit policymakers, financial institutions, and regulatory authorities. It contributes to advancing secure and resilient financial risk management in FinTech. The necessity for continuous innovation to address emerging fraud tactics in a dynamic digital landscape is stressed.