Blog

AI agents, RAG, and integrations — notes from production.

Common Mistakes Adding an LLM Layer to a Legacy Fraud Engine

The recurring LLM fraud engine mistakes when adding a reasoning layer to legacy rules: no shadow mode, all-traffic calls, ungrounded output, no fallback path.

What Counts as Explainable AI in Fraud Detection?

Explainable AI in fraud detection means more than SHAP values. What a fraud decision has to explain, to whom, and where post-hoc explanations fall short.

5 Fraud Detection Signals Beyond Transaction Amount

Transaction amount is a weak fraud signal alone. Five fraud detection signals that do more work - velocity, device, graph, behavior, text - and how each fails.

Rules Engine vs LLM Fraud Scoring: What Actually Changes

A rules engine, a gradient-boosted model, and an LLM layer each change something specific in fraud scoring. What actually shifts, and what doesn't.

How Real-Time Fraud Scoring Systems Are Architected

Real-time fraud scoring is an architecture problem: the latency budget, the online feature store, graceful degradation, and the async audit path.

Build vs Buy Fraud Detection: When to Build In-House

Most teams should buy fraud detection first. The specific pressures - false-positive cost, proprietary signal, volume, compliance - that justify building.