Feedzai TrustScore: Enabling Network Intelligence to Fight Financial Crime
Traditional fraud detection relies on rule-based systems or institution-specific custom AI models that are slow to deploy, require constant maintenance, and take a reactive and siloed view of fraud because each is limited to data from a single institution, while fraudsters operate and collaborate across borders.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Transaction event input
A financial transaction event enters the system for risk scoring within a live, dynamic financial environment.
Tools used
Feedzai TrustScoreFeedzai IQ™Mixture of Expertsfederated learning framework
Outcome
Real-world results from Feedzai TrustScore show a boost in fraud detection, a reduction in false alerts, and a faster time to market, with new clients able to start making predictions immediately without needing historical data collection.
What failed first
Custom AI models trained on a single institution's historical data are blind to emerging fraud patterns that have not appeared in their own environment, and rule-based systems require months of analyst work before deployment with constant manual updates thereafter.