Developers Build AI Fraud Investigation Agent Using TigerGraph, MCP, and GraphRAG

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A team participating in the TigerGraph × Hacker House Goa 2026 Challenge has built an agentic fraud investigation system designed to go beyond standard anomaly-score detection used in banking. The system combines TigerGraph Savanna Cloud, which stores over 590,000 transactions and 5,500 historical fraud cases, with a large language model agent that reasons iteratively over graph-based evidence. A Model Context Protocol (MCP) layer connects the AI agent to the graph database, enabling multi-hop relationship queries across transactions, cardholders, devices, and past cases. A deterministic policy engine enforces strict bank compliance rules and determines investigation verdicts, including card blocking and regulatory reporting under FinCEN SAR requirements. The architecture deliberately separates probabilistic AI reasoning from authoritative rule-based decision-making to ensure compliance and operational reliability.

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