LAS-GNN: A Graph Neural Network for Temporal Money Laundering Motif Detection

Abstract

We enhance Graph Neural Networks (GNNs) for identifying suspicious accounts involved in money laundering patterns. Extending the work of Egressy et al. (AAAI 2024), we propose a novel GNN architecture to detect suspicious subgraph motifs in the weighted temporal networks underlying financial data. Our architecture allows for the indication of edge directionality within a single Aggregator function, element-wise edge weight multiplication, and an LSTM aggregator that can learn from the sequential order of edges imposed by timestamps. The resulting model, LAS-GNN, is based on an inductive learning framework and can generalize across different networks. Experimental results on synthetic networks show that LAS-GNN is robust and can identify basic money laundering motifs to near perfection, outperforming a graph isomorphism network benchmark with edge features.