Collaborative anti-money laundering using federated learning

Money laundering is a critical enabler of organized crime, integrating illicit profits into the legitimate economy. Despite significant regulatory efforts, the problem is persistent and growing, with recent estimates suggesting a staggering $3.1 trillion of illicit funds flowing globally in 2023. Traditional anti-money laundering (AML) measures are largely based on siloed, rule-based systems, and they struggle to keep up with criminals' sophisticated and ever-changing tactics.

While machine learning offers a promising approach to improve money laundering detection by leveraging more complex patterns in transactional data, significant challenges remain to implement this in practice. These include the lack of realistic, labeled datasets and the complexities of sharing information across institutions.

In this project, we present a holistic AML framework that aims to tackle these challenges; addressing several important issues related to data, collaboration, and explainability. We demonstrate the use of our pipeline to create a synthetic dataset informed by empirical data to simulate realistic inter-bank heterogeneity and show the improved performance from employing federated learning. We also highlight how data quality has a crucial impact on model development and address some important practical concerns around implementing federated learning for AML between different banks.