Behavioral Association Analysis for Organized Fraud Ring Detection in Large-Scale User Interaction Data
Keywords:
fraud detection, behavioral association, graph analysis, community detectionAbstract
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.References
1. "Heterogeneous graph neural network," *Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining*, pp. 793--803, 2019. https://doi.org/10.1145/3292500.3330961
2. "Enhancing graph neural network-based fraud detectors against camouflaged fraudsters," *Proceedings of the 29th ACM International Conference on Information & Knowledge Management*, pp. 315--324, 2020. https://doi.org/10.1145/3340531.3411903
3. "Live-streaming fraud detection: A heterogeneous graph neural network approach," *Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining*, pp. 3670--3678, 2021. https://doi.org/10.1145/3447548.3467065
4. "Graph neural network for fraud detection via spatial-temporal attention," IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 8, pp. 3800--3813, 2022. https://doi.org/10.1109/TKDE.2020.3025588
5. "Enhancing financial fraud detection with hierarchical graph attention networks: A study on integrating local and extensive structural information," Finance Research Letters, vol. 58, p. 104458, 2023. https://doi.org/10.1016/j.frl.2023.104458
6. "Removing camouflage and revealing collusion: Leveraging gang-crime pattern in fraudster detection," *Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining*, pp. 5104--5115, 2023. https://doi.org/10.1145/3580305.3599895
7. "Fighting against organized fraudsters using risk diffusion-based parallel graph neural network," *Proceedings of the 32nd International Joint Conference on Artificial Intelligence*, pp. 6138--6146, 2023. https://doi.org/10.24963/ijcai.2023/682
8. "Dynamic relation-attentive graph neural networks for fraud detection," *Proceedings of the 2023 IEEE International Conference on Data Mining Workshops (ICDMW)*, pp. 1092--1096, 2023. https://doi.org/10.1109/ICDMW60847.2023.00143
9. "Financial fraud detection using graph neural networks: A systematic review," Expert Systems with Applications, vol. 240, p. 122156, 2024. https://doi.org/10.1016/j.eswa.2023.122156
10. "A spatial-temporal gated network for credit card fraud detection by learning transactional representations," IEEE Transactions on Automation Science and Engineering, vol. 21, no. 4, pp. 6978--6991, 2024. https://doi.org/10.1109/TASE.2023.3344080
11. "Heterogeneous graph neural networks for fraud detection and explanation in supply chain finance," Information Systems, vol. 121, p. 102335, 2024. https://doi.org/10.1016/j.is.2023.102335
12. "A heterogeneous graph-based framework for scalable fraud detection," *Proceedings of the 19th International Workshop on Mining and Learning with Graphs (MLG@KDD 2023)*, 2023. https://www.mlgworkshop.org/2023/papers/MLG__KDD_2023_paper_4.pdf
13. "Group-based fraud detection network on e-commerce platforms," *Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining*, pp. 5463--5475, 2023. https://doi.org/10.1145/3580305.3599535
14. "MINT: Detecting fraudulent behaviors from time-series relational data," Proceedings of the VLDB Endowment, vol. 16, no. 12, pp. 3610--3623, 2023. https://doi.org/10.14778/3611479.3611535
15. "GoSage: Heterogeneous graph neural network using hierarchical attention for collusion fraud detection," Proceedings of the Fourth ACM International Conference on AI in Finance, pp. 185--192, 2023. https://doi.org/10.1145/3604237.3626856
