A Buyer’s Guide to the Fraud Technology Market

The fraud technology market is characterized by fragmentation, with many tools failing to provide a comprehensive view of fraud activities, leading to manual efforts in context assembly before decisions can be made. This market is divided into categories that address specific aspects of fraud, such as transaction monitoring, scam and social engineering controls, identity and authentication, identity verification and onboarding, chargebacks and disputes, and orchestration and workflow hubs. Each category solves particular problems in fraud detection, but often lacks the ability to connect data across systems, resulting in inefficiencies. A connected intelligence layer is suggested to reduce manual reconciliation, improve network pattern detection, and enhance explainability by treating relationships as first-class data. The use of graph technology, like TigerGraph, is advocated for its ability to store and query connections directly, enabling more effective and explainable fraud detection. When evaluating vendors, the focus should be on reducing manual context assembly, ensuring the tool supports multi-hop relationship reasoning, and providing explainable evidence paths, rather than merely increasing alert generation.