Engineers Build AI Agent That Investigates Fraud Cases End-to-End Using Graph Database

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A development team has built a nine-node LangGraph agent capable of conducting full fraud investigations, from evidence gathering to regulatory report filing. The system uses TigerGraph Community Edition as its knowledge graph, exposing graph operations via TigerGraph MCP tools, while NVIDIA NIM handles large language model reasoning. Rather than replacing existing fraud detection models, the agent adds an investigation layer that takes flagged transactions through context analysis, pattern recognition, policy checks, and permanent case storage. The system was tested against all 20 cases in the HHGOA benchmark dataset, successfully producing FinCEN-standard Suspicious Activity Report narratives where regulatory filing was required. Each completed investigation is written back into the graph as memory, allowing future cases to draw on prior findings.

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