Mitigating the Tail Effect in Fraud Detection by Community Enhanced Multi-Relation Graph Neural Networks

Fraud detection, a classical data mining problem in finance applications, has risen in significance amid the intensifying confrontation between fraudsters and anti-fraud forces. Recently, an increasing number of criminals are constantly expanding the scope of fraud activities to covet the property of innocent victims. However, most existing approaches require abundant historical records to mine fraud patterns from financial transaction behaviors, thereby leading to significant challenges to protect minority groups, who are less involved in the modern financial market but also under the threat of fraudsters nowadays. Therefore, in this paper, we propose a novel community-enhanced multi-relation graph neural network-based model, named CMR-GNN, to address the important defects of existing fraud detection models in the tail effect situation. In particular, we first construct multiple types of relation graphs from historical transactions and then devise a clustering-based neural network module to capture diverse patterns from transaction communities. To mitigate information lacking tailed nodes, we proposed tailed-groups learning modules to aggregate features from similarly clustered subgraphs by graph convolution networks. Extensive experiments on both the real-world and public datasets demonstrate that our method not only surpasses the state-of-the-art baselines but also could effectively harness information within transaction communities while mitigating the impact of tail effects.