About Tabby

Tabby is a financial technology company in the Middle East that gives millions of people power over their money and flexibility in everyday spending. With a highly international customer base, including over 80% expatriates in the UAE and around 40% in the Kingdom of Saudi Arabia (KSA), Tabby operates in a complex risk environment where user identity, device continuity, and fraud signals are often fragmented across geographies, devices, and platforms.

The challenge: Managing fraud risk in a highly international market

As a fast-growing BNPL provider, Tabby must make real-time underwriting decisions during checkout while maintaining a seamless user experience across mobile and web. This challenge is amplified by the region’s demographics: a large share of users are expats, meaning even trusted customers often change devices, phone numbers, or locations over time.

From a fraud perspective, two patterns stood out:

• Account takeover (ATO) driven by phishing and social engineering attacks
• Micro-lending abuse, where fraud activity is hidden within otherwise normal merchant transactions involving real users

“There was some impact on conversion, so we had to be thoughtful about our approach,” the Tabby team explained. “Fraud isn’t the main risk for us, but we still need to catch risky behavior without disrupting good customers.” Tabby needed a way to better distinguish between legitimate returning users and risky recurring behavior without adding friction or materially affecting approval rates.

Why Tabby chose Fingerprint

To address fraud before it became a problem, Tabby took a proactive approach and evaluated several device intelligence solutions available in the market. The team was already familiar with device fingerprinting through earlier use of the open-source FingerprintJS library.

“We evaluated Fingerprint alongside other options,” the Tabby team shared. “Fingerprint stood out as a reliable way to recognize repeat devices and how easily it fit into our existing risk framework.”

Fingerprint stood out as a pragmatic, high-signal addition to Tabby’s existing risk framework. Instead of replacing internal models or credit logic, Fingerprint offered reliable device-level datapoints that could strengthen underwriting decisions, particularly for recurring users.

Key factors in the decision included:

• High-quality device identification without heavy user friction
• Straightforward integration that fit easily into existing risk rules

Strengthened Tabby’s existing fraud stack without requiring a major system overhaul

A scalable addition to Tabby’s risk strategy

As Tabby continues to expand across markets with diverse user populations, Fingerprint serves as a lightweight, scalable layer of device intelligence that complements existing credit and fraud systems.

Instead of adding friction or operational complexity, Fingerprint gives Tabby clearer visibility into high-risk edge cases, helping the team improve risk separation and support safer, more confident growth in a complex BNPL environment.