Behavioral Association Analysis for Organized Fraud Ring Detection in Large-Scale User Interaction Data

The proliferation of organized fraud rings poses significant threats to online platforms and financial systems worldwide. This paper presents a comprehensive behavioral association analysis framework for detecting coordinated fraudulent activities in large-scale user interaction data. We construct heterogeneous user interaction graphs from multi-source behavioral data and extract coordinated behavioral fingerprints across accounts through multi-dimensional association signals including fund flows, device sharing patterns, and temporal synchronization features. Community detection algorithms are employed to identify tightly-connected fraud groups within massive interaction networks. Experimental evaluation on real-world datasets demonstrates that our association-based approach achieves superior detection performance compared to traditional methods, with precision rates reaching 94.7% and recall rates of 89.3% on organized fraud ring identification tasks.