The calibration bug that taught our fraud agent to wave fraud through
Building an agentic fraud investigator on TigerGraph — and the two measurement mistakes that silently inverted it. All 18 architecture diagrams, interactive: FraudGraph Blueprints : system, agent loop, MCP path, schema, GraphRAG, policy rules, all twenty benchmark cases, evaluation and failure modes. The architecture at a glance Three triggers, one LangGraph agent, TigerGraph reached through the…
The article discusses a fraud investigation system built on TigerGraph, a graph database, to identify and handle fraud cases. The system involves a LangGraph agent that retrieves evidence and makes decisions based on a Bayesian ledger and policy engine. The core of the system cannot import any LLMs (language models) to ensure accountability and transparency in regulatory compliance.
The agent generates verdicts, probabilities, actions, and approval routes without needing an API key, and the entire process can be reproduced without any paid services.
The author highlights two measurement mistakes in the system: first, the closed-case file used for training is not a representative sample of alerts, and second, the vector search and traversal methods used to identify similar cases may not capture the nuances required for accurate fraud detection. The article emphasizes the importance of accurate data representation and effective querying techniques to build a reliable fraud investigation system.
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