Open Access

Applying Graph Theory to Detect Collusive Fraud Rings in Distributed Reward Systems

https://doi.org/10.55640/ijctisn-v03i05-01

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Igor Litovsky ¹ ,

⁴ Founder & CTO, Mastermind Loyalty Toronto, Canada

Abstract

Distributed reward systems circulate digital value through points, referral bonuses, coupon credits, vouchers, cashback balances, and partner-linked entitlements. Coordinated fraud in such systems often spreads across multiple accounts and shared infrastructure, weakening account-level screening. This article examines how graph theory can support the detection of collusive fraud rings in reward platforms with distributed value flows. The study draws on ten recent publications on graph anomaly detection, graph fraud detection, dynamic graphs, hypergraph learning, frequency-aware modeling, and interpretable graph analytics. The analytical section identifies the structural signatures of collusion, clarifies which graph representations expose them most effectively, and formulates a deployment model for ring-oriented detection. The article argues that collusive abuse becomes more legible when accounts, devices, addresses, payment instruments, referral links, and redemption paths are modeled as one evolving multi-entity graph. The proposed synthesis offers a practical basis for scoring, analyst review, and enforcement design in reward ecosystems.

Keywords

graph theory, collusive fraud, fraud rings, distributed reward systems, graph anomaly detection, dynamic graphs

References

Lou, C., Wang, Y., Li, J., Qian, Y., & Li, X. (2025). Graph neural network for fraud detection via context encoding and adaptive aggregation. Expert Systems with Applications, 261, 125473. https://doi.org/10.1016/j.eswa.2024.125473

Lu, J., Xu, Q., & Hu, J. (2026). A novel graph learning framework for interpretable and imbalance financial fraud detection. Engineering Applications of Artificial Intelligence, 167, 113709. https://doi.org/10.1016/j.engappai.2025.113709

Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., Xiong, H., & Akoglu, L. (2023). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012–12038. https://doi.org/10.1109/TKDE.2021.3118815

Motie, S., & Raahemi, B. (2024). Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications, 240, 122156. https://doi.org/10.1016/j.eswa.2023.122156

Prasetya, H. A., Liu, X., Murata, T., & Matono, A. (2026). A multi-rounded adversarial scenario for graph-based promo fraud detection. Social Network Analysis and Mining, 16(1), 24. https://doi.org/10.1007/s13278-025-01566-0

Ren, L., Hu, R., Li, D., Liu, Y., Wu, J., Zang, Y., & Hu, W. (2023). Dynamic graph neural network-based fraud detectors against collaborative fraudsters. Knowledge-Based Systems, 278, 110888. https://doi.org/10.1016/j.knosys.2023.110888

Wang, Q., Shen, Y., & Dong, H. (2025). Hypergraph-based contrastive learning for enhanced fraud detection. Frontiers in Artificial Intelligence, 8, 1703135. https://doi.org/10.3389/frai.2025.1703135

Wang, X., Liu, Z., Liu, J., & Liu, J. (2023). Fraud detection on multi-relation graphs via imbalanced and interactive learning. Information Sciences, 642, 119153. https://doi.org/10.1016/j.ins.2023.119153

Zhang, G., Wu, J., Yang, J., Beheshti, A., Xue, S., Zhou, C., & Sheng, Q. Z. (2021). Fraudre: Fraud detection dual-resistant to graph inconsistency and imbalance. 2021 IEEE International Conference on Data Mining (ICDM), 867–876. https://doi.org/10.1109/ICDM51629.2021.00098

Zhao, W., & Chen, H. (2026). Learning frequency-aware graph fraud detection. Neural Networks, 198, 108600. https://doi.org/10.1016/j.neunet.2026.108600

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Applying Graph Theory to Detect Collusive Fraud Rings in Distributed Reward Systems

Abstract

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References

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