Graph-Based Fraud Tool Argues Both Sides Before Reaching a Verdict

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A fraud investigation system called Fraud Investigator has been built on TigerGraph to address a core flaw in conventional fraud detection: risk scores alone both wrongly flag innocent users and miss real fraud. Data analysis of five source files revealed that all 900 cleared cases scored 0.81 or higher, while confirmed fraud cases had a mean score of 0.47, with 31% scoring below 0.3. The system runs separate prosecution and defence signature checks on each alert, then passes the evidence to a calibrated judge before a policy engine determines any real-world action. TigerGraph's graph features achieved an AUC of 0.91 in distinguishing alerts, compared to just 0.62 for the bank's own risk score. An LLM is used only for plain-language explanations and Suspicious Activity Report narratives, while all decisioning logic remains deterministic.

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