Agentic AI System Built to Investigate Fraud End-to-End Using Graph Database

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Developers have built an AI-powered fraud investigation agent for the TigerGraph Agentic Fraud Investigation Hackathon, using the HHGOA_IEEE dataset. The agent takes a single fraud alert and autonomously gathers evidence by querying transaction histories, mapping shared devices and regions, and comparing against past cases. It separates reasoning tasks handled by a large language model from rule enforcement, which is managed by deterministic policy code to reduce errors. The system uses vector search and graph traversal to identify fraud rings and retrieve similar historical cases, writing its findings back into the graph so future investigations can learn from them. Each investigation is fully logged and replayable, producing one structured case file per alert with a complete evidence and reasoning trail.

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