CaseGuard Uses TigerGraph and AI to Investigate Fraud With Built-In Uncertainty Checks

Developers have built CaseGuard, an autonomous AI fraud investigation system that combines TigerGraph, GSQL, GraphRAG, and agentic reasoning to analyze suspicious financial transactions. Unlike traditional fraud systems that rely on static thresholds, CaseGuard is designed to withhold judgment when confidence is low, instead requesting additional evidence before recommending an action. The system uses native graph queries to detect patterns such as card testing, device-sharing across multiple accounts, and geographic anomalies in transaction history. Completed investigations are stored back into TigerGraph as institutional memory, allowing the system to reference past cases — for instance, using a cardholder's legitimate travel history to clear a false alert. When reporting thresholds are met, CaseGuard can also automatically generate structured FinCEN Suspicious Activity Report narratives, reducing manual documentation work for investigators.