GraphGuard: Building a Graph-Powered AI Agent for Fraud Investigation with FalkorDB
Track: Track 1 — Agents That Act on Connected Data Event: Graph Hacks 2026 by WeMakeDevs and FalkorDB Introduction AI agents are becoming increasingly capable of answering questions, analyzing information and automating workflows. However, an important challenge remains: an agent can only make a reliable decision when it has the right context. Consider a suspicious financial transaction. Looking…
GraphGuard is an AI-powered prototype designed to investigate financial transactions by linking related records and presenting an evidence trail. The system utilizes FalkorDB, a graph database, to model connections between accounts, transactions, and devices. By representing data as nodes and relationships, GraphGuard enables a more comprehensive analysis beyond isolated records.
Key capabilities of GraphGuard include investigating an account and its transactions, tracing transactions through connected accounts, identifying accounts with shared devices, flagging risk indicators, and suggesting follow-up checks. The prototype uses synthetic data and relies on transparent investigation rules, rather than an LLM for reasoning.
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