Sentinel Uses TigerGraph and AI to Automate Bank Fraud Investigations

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A developer built Sentinel, an agentic fraud investigation tool, as a submission to the Hacker House Goa × TigerGraph hackathon. The system analyzes six months of card-transaction graph data to generate verdicts, fraud-pattern labels, recommended bank actions, and FinCEN-compliant SAR narratives for flagged alerts. A key design choice separates responsibilities: a large language model writes explanatory prose while a deterministic policy engine makes all decisions based on the bank's own rulebook. The underlying fraud classification model achieved a cross-validated AUC of 0.9465, with counterintuitive findings — such as a new device flag correlating slightly against fraud — only emerging through empirical training rather than intuition. Eighteen GSQL queries run in parallel via asyncio, reducing per-investigation processing time from roughly 25 seconds to about 2 seconds.

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