Case Study: BlaBlaCar strengthens fraud prevention with MANGOPAY’s Nethone

BlaBlaCar boosts fraud detection with MANGOPAY after spotting 94% precise SMS pumping waves

BlaBlaCar, a global ridesharing platform, faced significant fraud challenges including the sale of dormant accounts, social-engineering scams, and infrastructure abuse like SMS pumping. To protect its 29 million members without disrupting their experience, the company needed to enhance its ability to detect these evolving threats. BlaBlaCar combined its internal machine-learning models with the profiling solution from Mangopay's Nethone to gain better visibility.

The solution involved integrating Mangopay's Nethone to uncover hidden fraud patterns not visible from the platform alone. Nethone's signals were combined with BlaBlaCar's own data in a shared architecture with a continuous feedback loop. This allowed BlaBlaCar to react faster, such as detecting a wave of SMS pumping attacks with 94% precision and identifying 24,000 fraudulent account-creation attempts from a coordinated network. Mangopay provided the external expertise and bigger-picture perspective that complemented BlaBlaCar's internal systems.

BlaBlaCar