Case Study: Large North American Retail Bank saves $30M with Feedzai

Feedzai Boosts Legacy Fraud Systems for Digital Banking Era

A large North American retail bank serving over 17 million clients across 29 countries was struggling with a growing fraud alert queue, low fraud-team satisfaction, and missed fraud cases under a rules-only approach. Feedzai was brought in to improve detection without replacing the bank’s incumbent fraud system, using its adaptive machine learning-based transaction fraud solution.

Feedzai implemented a hybrid overlay that integrated with the bank’s existing systems, including transactional API calls, Kafka streaming, and a third-party case management tool with a fraud-outcomes feedback loop. The result was $30 million in savings over three years, a 75% average value detection rate at a 0.1% intervention rate, and a 12:1 false positive rate, while giving the bank greater autonomy, flexibility, and speed in fraud decisioning.