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[ARTICLE · art-139227] src=dev.to ↗ pub= topic=ai-agents verified=true sentiment=↑ positive

Built an agentic fraud investigator using

A developer built an agentic fraud investigator on TigerGraph for the HH Goa 2026 hackathon, chaining graph traversal, case memory, a policy engine, and next-best-action recommendations that produce suspicious activity reports. The project, with code on GitHub and a Streamlit demo, argues that because fraud is relational, the underlying database should be graph-based.

by read1 min views1 publishedSep 24, 2026
Built an agentic fraud investigator using
Image: Dev.to

Built an Agentic Fraud Investigator using TigerGraph for #HHGOA2026 🚀

My agent investigates fraud like an analyst:

Graph Traversal -> Case Memory -> Policy Engine -> Next-Best Action + SAR Fraud is relational, so the DB should be too.

Repo: [https://github.com/foxmaster77/tigergraph](https://github.com/foxmaster77/tigergraph)

LInk : [https://tigergraph-2cd6jdrgrem2jblxb49zjj.streamlit.app](https://tigergraph-2cd6jdrgrem2jblxb49zjj.streamlit.app)

@TigerGraphDB @247pmstudio

source & further reading
dev.to — original article Building FraudGraph AI: Agentic Fraud Investigation with TigerGraph and GraphRAG Google Lens Exceeds 25 Billion Monthly Searches, Raising the Stakes for Visual Search Jev: AI for Decisions, Not Just Generation
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