Case Study: Revolut boosts fraud detection accuracy with SEON

Revolut Leverages SEON’s Anti-Fraud Platform With Great ROI

Revolut, the global digital banking and fintech platform operating in 39 countries, wanted to strengthen fraud prevention as it scaled worldwide. To improve its global risk models without adding friction to the customer experience, Revolut turned to SEON for richer fraud signals and digital footprinting data.

Using SEON’s enriched data — including CNAME, social, and device signals — Revolut fed additional inputs into its machine-learning fraud models. The result was a 2% improvement in fraud detection accuracy, helping Revolut catch more fraud cases it would otherwise miss, while also supporting cost-efficient scaling and fast integration in under three weeks.

Revolut

Dmitri Lihhatsov

Fincrime Product Owner