Federated Learning Approaches to Collaborative Fraud Detection Across Financial Institutions

Abstract

Financial fraud detection has emerged as a critical challenge for banking institutions worldwide, with billions of dollars lost annually to increasingly sophisticated fraudulent activities. Traditional centralized machine learning approaches face significant limitations due to data privacy regulations, institutional data silos, and the inability to leverage collective intelligence across organizations. This paper presents a comprehensive review of Federated Learning (FL) methodologies applied to collaborative fraud detection systems in the financial sector. FL enables multiple financial institutions to jointly train robust fraud detection models while maintaining strict data privacy and regulatory compliance. We examine the architectural frameworks, algorithmic innovations, and practical implementations of FL-based fraud detection systems, with particular emphasis on model initialization strategies, communication efficiency optimization, and handling data heterogeneity across institutions. Through analysis of recent developments, we demonstrate how FL addresses key challenges including non-independent and identically distributed data, communication overhead, and convergence stability. Our review reveals that FL-based approaches achieve detection accuracy improvements of up to 20% compared to isolated institutional models, while simultaneously ensuring compliance with data protection regulations such as GDPR and CCPA. We discuss the integration of advanced techniques including structured gradient compression, adaptive local training strategies, and initialization protocols that enhance both detection performance and system efficiency. The paper concludes by identifying emerging research directions and practical considerations for deploying FL systems in real-world financial environments.