LaundroGraph: Using deep learning to support anti-money laundering efforts

In recent years, deep learning techniques have proved to be highly valuable for tackling countless research and real-world problems. Researchers at Feedzai, a financial data science company based in Portugal, have demonstrated the potential of deep learning for the prevention and detection of illicit money laundering activities. In a paper presented at the 3rd ACM International Conference on AI in Finance, the team at Feedzai introduced LaundroGraph, a self-supervised model that could simplify the cumbersome process of reviewing large amounts of financial interactions looking for suspicious transactions or monetary exchanges. Their model is based on a graph neural network, an artificial neural network (ANN) designed to autonomously process large amounts of data that can be represented as a graph. "Wanting to strengthen our AML solution, and after identifying major pains with the current AML reviewing process, we thought about solutions to overcome these challenges using AI," Mario Cardoso, a Research Data Scientist at Feedzai, told TechXplore.