Developers Build AI-Powered Graph System to Investigate Suspicious Transactions

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A team participating in the TigerGraph × Hacker House Goa challenge has built FraudGraph Investigator, an AI-driven system designed to conduct structured fraud investigations rather than simple transaction classification. The system combines TigerGraph, LangGraph, and Model Context Protocol (MCP) to trace connections between customers, cards, devices, locations, and related transactions. Instead of assigning a risk score, it follows a multi-stage workflow covering evidence collection, hypothesis evaluation, uncertainty assessment, and policy-governed decision-making. A key design principle separates AI-assisted investigation from final operational decisions, with explicit policy rules determining whether to block a card or take other action. The architecture is layered to keep data, reasoning, policy enforcement, and the user interface independently testable and extendable.

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