Developers Build AI Fraud Investigator Using TigerGraph and Local Language Models

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A team built Casework, an agentic fraud investigation application, as part of the TigerGraph and HHGoa developer challenge. The tool connects graph-based evidence from TigerGraph Savanna, a local Llama 3.2 language model via Ollama, vector document retrieval, and policy rules to investigate flagged financial transactions. Starting from a suspicious transaction trigger, the system traces relationships, calculates signals, retrieves historical cases, and produces a structured investigation record with a recommended action. The language model's role is deliberately constrained — it proposes queries and reviews evidence but cannot execute database commands or financial actions, keeping decisions tied to explicit policy rules. The project's source code and 20 benchmark case files are publicly available on GitHub.

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