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Developer Builds Autonomous Fraud Detection Agent Using TigerGraph and LangChain in 24 Hours

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A solo developer built a fully autonomous fraud investigation agent at the HHGOA 2026 hackathon within 24 hours. The system uses TigerGraph's graph database to detect relationship-based fraud patterns that traditional SQL or vector databases typically miss. Powered by a LangChain ReAct agent, it selects from eight tools to perform multi-hop graph traversals, detect five fraud patterns, and execute actions such as blocking or freezing accounts. Policy-grounded recommendations are ensured through a GraphRAG setup using a ChromaDB vector store built from real bank policy documents, preventing hallucinated outputs. The agent successfully investigated all 20 benchmark cases autonomously, generating Suspicious Activity Reports and Next Best Actions for each.

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Developer Builds Autonomous Fraud Detection Agent Using TigerGraph and LangChain in 24 Hours · ShortSingh