Why Vector RAG Isn't Enough for Fraud Detection

Vector RAG can help fraud investigators retrieve semantically similar documents and case notes, but it is limited when risk emerges from relationships among claims, accounts, devices, addresses, vehicles, and payment routes rather than from similar language. The discussion argues that graph-based retrieval better supports fraud detection by representing entities as nodes and their connections as edges, allowing analysts to trace multi-step evidence paths such as shared addresses, linked payment destinations, and networks connected to known fraud cases. Combining vector search for narrative and policy information with graph traversal for structural context enables AI systems to produce more explainable, relationship-grounded risk assessments. This approach is especially useful against organized fraud rings that rotate identities or alter surface details while reusing underlying infrastructure, as illustrated by banking fraud networks and Capitec’s use of graph features at large scale.

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