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

Anti-money laundering (AML) efforts are critical for maintaining the integrity of the global financial system. However, traditional AML approaches face limitations due to data silos between financial institutions. This paper proposes a federated learning framework for privacy-preserving AML in multi-bank collaborations. The proposed approach enables banks to jointly train machine learning models for detecting suspicious activities without sharing raw customer data. We evaluate the framework on synthetic transaction datasets and demonstrate improved AML performance compared to single-bank models while preserving data privacy. The results show promise for enhancing AML efforts through secure inter-bank collaboration.