Strengthen Fraud Protection in BNPL

About Pledg

Pledg is a French fintech, founded in 2016 to provide payment solutions (BNPL, deferred and instalment payments) to merchants and financial institutions across online and in-store channels. Its products are designed to boost conversion and sales while delivering a smooth, modern checkout experience for consumers. In February 2024, Pledg became a wholly-owned subsidiary of Crédit Agricole Personal Finance & Mobility (Sofinco), strengthening its capacity to deliver embedded credit experiences at scale. Today, Pledg supports retailers, travel companies, ticketing players, service providers and B2B marketplaces in over 10 European countries.

The challenge

From the customer’s point of view, Pledg’s financing journey was already fully digital, automated and seamless. Behind the scenes, however, suspected-fraud investigations were too manual and time-consuming to meet Pledg’s growth ambitions.

Risk analysts often had to piece together identity clues from scattered tools and ad-hoc checks, which made it hard to confidently approve borderline cases or quickly block sophisticated fraud attempts at checkout.

As volumes grew, this manual approach also created operational bottlenecks, inconsistent decisions and longer handling times for edge cases.

To address this challenge, the team started looking for a solution able to automatically assess user risk at the beginning of the financing journey.

The objective was to improve the speed and accuracy of fraud detection activities without slowing down legitimate customers looking to complete their purchases.

The Solution

Pledg ran parallel Proofs of Concept with several fraud prevention providers to compare performance. Trustfull delivered the most accurate and actionable results. During the initial rollout:

• Pledg’s risk analysts gained access to Trustfull’s intuitive dashboard
• The Trustfull API was integrated directly into their workflow, surfacing high-quality, ready-to-use digital signals (e.g. email and phone reputation, IP and network risk, consistency checks)

This immediately shortened investigations and made it easier to justify decisions and maintain a clear audit trail on new customer requests.

Over a period of several months, performance data consistently showed that Trustfull’s risk and trust factors linked to users’ contact details and IP addresses were genuinely discriminative to detect fraud attempts at the beginning of the customer journey.

As a result, Pledg incorporated a selection of Trustfull signals and rules into its internal scoring algorithms and used them to trigger automated workflows for certain situations, such as hard-blocking requests below a low Trustfull score or allowing verified users to progress with fewer manual checks.

The net effect: fewer analyst touchpoints, faster time-to-decision, and greater confidence that fraudulent customers are stopped at the door.