Mr. MANOJ BHOYAR

Federated Learning for Privacy-Preserving AML in Multi-Bank Collaborations

Abstract:

Anti-Money Laundering (AML) efforts are essential for preserving the integrity of the global financial system. However, traditional AML practices often face significant limitations due to their siloed nature, which hinders the sharing of sensitive data across institutions. This paper explores the potential of Federated Learning (FL) as a privacy-preserving solution to address these challenges and enhance AML efforts through multi-bank collaboration. We propose a comprehensive framework that allows banks to collaboratively train machine learning models without exposing proprietary or sensitive customer data, ensuring compliance with regulatory and privacy requirements. Our approach demonstrates substantial improvements in detecting complex money laundering patterns, achieving up to 30% higher accuracy in identifying suspicious activities compared to traditional isolated methods. By leveraging FL, institutions can maintain strict data confidentiality while benefiting from shared insights across the financial ecosystem. Extensive experimentation and real-world case studies validate the effectiveness of our framework, showcasing its scalability, robustness, and applicability in diverse banking environments. This research provides a significant step forward in reconciling the dual imperatives of effective financial crime prevention and stringent data protection, paving the way for a more collaborative and secure future in AML practices.