Money Laundering Detection Using Graph Neural Networks Enhanced with Autoencoder Components

The paper addresses the topic of detecting money laundering operations in transaction data represented as graph data-structures. We propose the integration of autoencoder components in Graph Neural Networks (GNN) architectures, in order to incorporate a reconstruction step in the traditional edge classification problem and enhance model quality based upon the usage of reconstruction errors. We show that enhancing GNNs with autoencoder components improves the predictive performance of money laundering detection, on data represented as homogeneous graphs. Additionally, the Shapley value is computed in order to gain further insight into the most important features from distinguishing normal and fraudulent activities.