The latest articles on DEV Community by foxmaster77 (@foxmaster77). https://dev.to/foxmaster77 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F4141709%2F2d336d51-c727-4d66-b507-f95dd72e2d58.png https://dev.to/foxmaster77 en foxmaster77 Thu, 24 Sep 2026 18:16:38 +0000 https://dev.to/foxmaster77/how-i-built-an-agentic-fraud-investigator-using-tigergraph-langchain-in-24-hours-f98 https://dev.to/foxmaster77/how-i-built-an-agentic-fraud-investigator-using-tigergraph-langchain-in-24-hours-f98 <h2> The Challenge </h2> <p>At HHGOA 2026, we were given a real-world fraud detection problem: <br> investigate 20 complex fraud cases using an AI agent powered by TigerGraph.</p> <p>The result? A fully autonomous Fraud Investigation Agent that detects fraud rings, <br> retrieves compliance policy via GraphRAG, and recommends Next Best Actions — all in real-time.</p> <h2> Architecture </h2> <h2> Why TigerGraph? </h2> <p>Standard SQL or vector databases miss <strong>relationship fraud</strong> entirely. <br> A money mule network is invisible in a flat table — but TigerGraph's <br> multi-hop traversal exposes the entire ring instantly via GSQL:</p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code> sql SELECT src, dst FROM Account-(SHARED_DEVICE&gt;)-Device WHERE Device.risk_score &gt; 0.8 5 Fraud Patterns Detected Pattern Code Risk Card Testing PT001 High Account Takeover PT002 High Synthetic Identity PT003 Medium Money Mule Network PT004 Critical Merchant Collusion PT005 High The Agent Tools (LangChain) The ReAct agent autonomously selects from 8 tools: get_transaction_history — GSQL query on Account node get_connected_accounts — multi-hop graph traversal detect_fraud_patterns — GSQL algorithm execution get_fraud_policy — ChromaDB GraphRAG retrieval manage_case — write findings back to graph execute_action — block/freeze/escalate GraphRAG = No Hallucinated Actions Our agent never invents a recommendation. All actions are grounded in real bank policy retrieved from a ChromaDB vector store built from our fraud_policy.txt document. This is GraphRAG in practice. Results ✅ All 20 benchmark cases investigated autonomously ✅ SAR (Suspicious Activity Report) generated per case ✅ NBA (Next Best Action) returned for each case ✅ Full live UI at https://tigergraph-2cd6jdrgrem2jblxb49zjj.streamlit.app/ GitHub https://github.com/foxmaster77/tigergraph Built solo in under 24 hours for #HHGOA2026 🚀 </code></pre> </div> tigergraph langchain python hackathon