Identifying Fraud Rings Using Domain Aware Weighted Community Detection

AbstractWith the increase in online platforms, the surface area of malicious activities has increased manifold. Bad actors abuse policies and services like claims, coupons, payouts, etc., to gain material benefits. These fraudsters often work collusively (rings), and it is difficult to identify underlying relationships between them when analyzing individual actors. Fraud rings identification can be modeled as a community detection problem on graphs where nodes are the actors, and the edges represent common attributes between them. However, the challenge lies in incorporating the attributes’ domain-informed importance and hierarchy in coming up with edge weights. Treating all edge types as equal (and binary) can be fairly naive; we show that using domain knowledge considerably outperforms other methods. For community detection itself, while the weight information is expected to be learned automatically in deep learning-based methods like Graph Neural Networks (GNN), it is explicitly provided in traditional methods. In this paper, we propose a scalable and extensible end-to-end framework based on domain-aware weighted community detection to detect fraud rings. We first convert a multi-edge weighted graph into a homogeneous weighted graph and perform domain-aware edge-weight optimization to maximize modularity using the Leiden community detection algorithm. We then use features of communities and nodes to classify both community and a node as fraud or not. We show that our methods achieve up to 9.92% lift in F1-score on internal data, which is significant at our scale, and up to 4.81% F1-score lift on two open datasets (Amazon, Yelp) vs. an XGBoost based baseline.KeywordsFraud detectionGraph learningCommunity detectionFraud rings identificationGraph neural network