Case Study: Tabby improves BNPL risk accuracy with Fingerprint

Tabby flags risky transactions with Fingerprint and cuts fraud signals by ~25%

Tabby, a fintech company and buy now, pay later (BNPL) provider in the Middle East, faced the challenge of managing fraud risk within a highly international user base. Their complex risk environment, characterized by many expatriate customers frequently changing devices and locations, made it difficult to distinguish between legitimate users and fraudulent activity like account takeovers and micro-lending abuse. They needed a solution that could improve risk accuracy without adding friction for good customers or affecting approval rates. They chose to implement Fingerprint's device intelligence.

Fingerprint provided a high-quality device identification solution that integrated easily into Tabby's existing risk framework. By incorporating Fingerprint's data into their risk rules, Tabby gained the ability to instantly reject risky transactions and flag others with an expected risk of approximately 25%. This targeted impact allowed Tabby to focus stricter controls on high-risk areas while maintaining stable conversion and approval rates, supporting safer growth.

Tabby