AMLGaurd: Graph Based Money Laundering Detection in Financial Networks

Detecting money laundering within financial networks is challenging due to the complexity of illicit transactions and the scarcity of labeled data. In this study, we model accounts as nodes and transactions as edges to develop an unsupervised anomaly detection framework, AMLGaurd, utilizing Graph Auto-Encoders (GAEs). GAEs encode the structural and transactional information of financial entities into a latent space and reconstruct the network to identify anomalies based on reconstruction discrepancies. We classify anomalies into four categories: contextual, structural, joint-type, and structure-type, each capturing different irregular patterns in the network. Furthermore, GAE encoder based embeddings can also be used for supervised edge classification, which can promote a multi-task learning setting. Although our present findings offer encouraging insights, this methodology establishes the groundwork for future progress in identifying complex money laundering schemes. This research enhances the application of graph-based unsupervised learning techniques in anti-money laundering (AML) systems, contributing to more effective and scalable financial security measures.