Dev builds AI fraud investigation agent using TigerGraph and multi-agent reasoning

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Developer Vansh Deo of team QueryCrew built Zyg0s, an autonomous fraud investigation platform, as part of the TigerGraph Agentic Fraud Investigation Hackathon held under the Hacker House Goa track. The system uses TigerGraph Savanna Cloud, Model Context Protocol, and Groq to power seven specialized AI agents that analyze over 590,000 financial transactions from the IEEE-CIS dataset. Rather than relying on simple binary fraud classification, Zyg0s models investigations as an eight-stage evidentiary process, grading evidence across a four-tier defensibility framework and quantifying uncertainty. The platform enforces bank fraud policy rules, generates regulator-grade FinCEN BSA/AML Suspicious Activity Reports, and stores case outcomes in graph-native memory. It was designed to address key industry pain points including high false-alarm rates, risks of wrongly freezing customer accounts, and the inability of generic LLMs to produce legally defensible audit trails.

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