Building FraudGraph Agent: Autonomous Fraud Investigation & Policy-Compliant Next-Best Action with TigerGraph

Developers have introduced 'FraudGraph Agent,' an enterprise-grade autonomous system designed to combat modern payment fraud. Built for the TigerGraph x Hacker House Goa challenge, the solution integrates TigerGraph 4.2.5, the Model Context Protocol (MCP), and Vector GraphRAG to move beyond traditional, manual investigation workflows. By combining graph-native traversal with a deterministic policy engine, the agent can diagnose fraud typologies, gather evidence, and recommend actions like blocking cards or filing FinCEN Suspicious Activity Reports (SARs) without the risk of AI hallucinations. The system emphasizes a strict separation of concerns, where LLMs handle synthesis while a policy engine enforces regulatory compliance. With a 0.987 ROC-AUC performance on test datasets, the FraudGraph Agent demonstrates how autonomous, graph-based reasoning can significantly reduce false positives and operational friction in financial fraud detection, providing a scalable, audit-ready framework for modern banking operations.