Multi-View Graph-Based Hierarchical Representation Learning for Money Laundering Group Detection

Anti-money laundering (AML) is crucial to maintaining national financial security. Contemporary AML methods focus on homogeneous mining or unitary money laundering pattern. These methods ignore a characteristic of gang operation in money laundering. Thus, in this paper, we propose a multi-view graph-based hierarchical representation learning method, named MG-HRL, to mine organized money laundering groups. In particular, we extract multi-level representations of transaction subgraphs, including transaction features, user features, structural features, and high-order association features from multiple observational perspectives. To learn the correlation between users, we model transaction networks as heterogeneous information networks (HINs) and design six meta-paths related to money laundering scenarios to mine correlations among users. Combining with correlation representations of users, we propose a heterogeneous hypergraph representation learning method to learn high-order representations of transaction subgraphs. Through hierarchical representation learning, the MG-HRL achieves full exploration of money laundering groups. Finally, we conduct experiments on two public transaction datasets. The result shows that MG-HRL method performs better than other state-of-the-art baselines.