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

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

Abstract

Keywords

References

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