Dev Team Builds Graph-Powered Fraud Investigation Agent at TigerGraph Hacker House Goa

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A development team participating in the TigerGraph Hacker House Goa challenge built an AI-driven fraud investigation agent that mirrors how a human analyst would review suspicious transactions. The agent uses TigerGraph to examine a card's transaction history, device profile, billing region, and past bank decisions on similar cases before recommending any action. It incorporates a LightGBM classifier trained on transaction data from July to October, achieving an AUC of 0.905 compared to the bank's own score of 0.866. When evidence is insufficient to act, the agent is designed to seek customer verification before blocking any transaction, following built-in fraud policy rules. The team also used graph algorithms to uncover a 28-card fraud ring linked to a single device and identified burst-purchase patterns tied to known benchmark cases.

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