Hybrid Graph Representation Learning for Money Laundering Detection

Abstract

Money laundering detection stands as one of the most important challenges in the anti-financial crime sector, given its grave repercussions on the financial industry. The evolving nature of fraud schemes and the increasing volume of financial transactions impose limitations on the detection capabilities of traditional anti-money laundering (AML) systems. In the light of the recent breakthroughs in the field of graph machine learning, graph neural networks (GNNs) and graph transformers (GTs) have emerged as prominent solutions to these limitations, achieving a remarkable performance in detecting complex and broad fraudulent patterns. However, fusing the powerful characteristics of these classes of graph models into a unified framework for fraud detection has been little explored. In this paper, we address this gap by presenting GraphFuse — a hybrid graph representation learning model tailored for money laundering detection in financial transaction graphs. The novel edge centrality and transaction signature encodings offer GraphFuse a slight advantage over the best-performing GNN and GT models, improving upon the best GT baseline by 0.76 p.p. in F1 score. Additionally, we introduce three variants of the Transformer-based component of GraphFuse, each with a different level of computational complexity. The competitive performance of Graph-Fuse is supported by extensive experiments on open-source, large-scale synthetic financial transactions datasets. Our code is available at https://github.com/mfrija/aml-graphfuse.