Case Study: EntroPay achieves automated fraud prevention and 15% higher user conversion with Sift

EntroPay - Customer Case Study

EntroPay is a global payments provider that issues instant prepaid virtual Visa cards for online purchases. Because cards can be funded and spent immediately—and often with stolen payment details—EntroPay faced high real-time fraud risk, relying on a manual, rules-based review team of eight and systems that couldn’t prevent fraud as it occurred.

EntroPay implemented Sift Science over a weekend, training a customized machine-learning model on historical labeled transactions and using the Sift Score to automate accept/block/challenge decisions. As a result, manual review hours fell by 90% and the full-time fraud team was effectively reduced to zero, while conversion of legitimate users increased by 15% as the company refocused on customer success.

EntroPay

Mark Anthony Spiteri

Head of Product and Business