Federated Learning for Collaborative Financial Crimes Detection

Toyotaro Suzumura, Yi Zhou, Ryo Kawahara, Nathalie Baracaldo, Heiko Ludwig

Abstract: Mitigating financial crime risk (e.g., fraud, theft, money laundering) is a large and growing problem. In some way it touches almost every financial institution, as well as many individuals, and in some cases, entire societies. Advances in technology used in this domain, including machine learning-based approaches, can improve upon the effectiveness of financial institutions’ existing processes. However, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms. Where financial institutions address financial crimes through the lens of their own firm, perpetrators may devise sophisticated strategies that may span across institutions and geographies. In this chapter, we describe a methodology to share key information across institutions by using a federated graph learning platform that enables us to train more accurate detection models by leveraging federated learning as well as graph learning approaches. We demonstrate that our federated model outperforms a local model by 20% with the UK FCA TechSprint data set. This new platform opens up the door to efficiently detect global money laundering activity.