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Anti-money laundering (AML) regulations mandate financial institutions todeploy AML systems based on a set of rules that, when triggered, form the basisof a suspicious alert to be assessed by human analysts. Reviewing these casesis a cumbersome and complex task that requires analysts to navigate a largenetwork of financial interactions to validate suspicious movements.Furthermore, these systems have very high false positive rates (estimated to beover 95\%). The scarcity of labels hinders the use of alternative systems basedon supervised learning, reducing their applicability in real-worldapplications.  In this work we present LaundroGraph, a novel self-supervised graphrepresentation learning approach to encode banking customers and financialtransactions into meaningful representations. These representations are used toprovide insights to assist the AML reviewing process, such as identifyinganomalous movements for a given customer. LaundroGraph represents theunderlying network of financial interactions as a customer-transactionbipartite graph and trains a graph neural network on a fully self-supervisedlink prediction task. We empirically demonstrate that our approach outperformsother strong baselines on self-supervised link prediction using a real-worlddataset, improving the best non-graph baseline by $12$ p.p. of AUC. The goal isto increase the efficiency of the reviewing process by supplying theseAI-powered insights to the analysts upon review. To the best of our knowledge,this is the first fully self-supervised system within the context of AMLdetection.

LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering