Fraud Detection at Scale: A 99% Accuracy Case Study

WebbyButter built a custom Graph-based AI surveillance layer that sits directly on the client's transaction stream. By mapping every transaction to a massive, real-time relationship graph, the system can identify suspicious 'clusters' and anomaly patterns that are invisible to linear models. We utilized a hybrid approach: a lightweight GNN for instant screening and a deeper LLM-based 'intent analyzer' for high-value suspicious flags. This multi-layered defense ensures that 99% of fraud is caught before the transaction even completes. The solution also features a real-time 'heat map' for the fraud investigation team, providing visual insights into emerging attack vectors and allowing for rapid response. Our team also developed a federated learning framework that allows the system to share insights across different regional nodes while maintaining strict data privacy and local regulatory compliance. This ensures that a new fraud pattern detected in one region is instantly defended against across the entire global network.

Since the implementation of the GNN-powered fraud engine, the client has prevented over $500M in annual fraudulent transactions. False positives—where legitimate transactions are blocked—have plummeted by 95%, dramatically increasing customer trust and NPS scores. The system now processes every transaction in under 12ms, maintaining the speed of a modern digital bank while providing enterprise-grade security. The fraud investigation team has also seen a 70% reduction in manual workload, as the AI now provides clear rationales for every flagged event. The solution has also significantly improved the organization's regulatory standing, as the explainable AI models provide the level of auditability required by modern financial regulators. This project has not only protected the client's assets but has also become a key marketing differentiator, with customers citing the bank's security features as a primary reason for their loyalty.