Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

Understand

Graph Neural Networks (GNNs) have been widely applied to fraud detection problems in recent years, revealing the suspiciousness of nodes by aggregating their neighborhood information via different relations.

• However, few prior works have noticed the camouflage behavior of fraudsters, which could hamper the performance of GNN-based fraud detectors during the aggregation process.
• In this paper, we introduce two types of camouflages based on recent empirical studies, i.e., the feature camouflage and the relation camouflage.
• Existing GNNs have not addressed these two camouflages, which results in their poor performance in fraud detection problems.