To help you adapt to quickly changing fraud patterns, we’ve tripled the speed at which we update Radar’s machine learning models.

As our network grows, Stripe is more likely to see critical data inputs across multiple attempted payments, making it easier to catch fraudulent usage. And with more data, our models’ recall—a standard measure of machine learning accuracy—has improved by over 20%. This means that Stripe catches more fraudulent payment attempts while minimizing the impact on legitimate transactions.

Striking the right balance across fraud prevention and conversion is important to every business, but in a vacuum, it’s hard to know if you’re doing well. Radar now provides relevant benchmarks, so you can compare your fraudulent dispute rates, false positive rates, and block rates to other businesses in your region or in similar industries, right from the Dashboard. The aggregated, custom cohorts of businesses powering these benchmarks can help you understand how you may want to adjust your strategy.

To help you adapt to quickly changing fraud patterns, we’ve tripled the speed at which we update Radar’s machine learning models.

As our network grows, Stripe is more likely to see critical data inputs across multiple attempted payments, making it easier to catch fraudulent usage. And with more data, our models’ recall—a standard measure of machine learning accuracy—has improved by over 20%. This means that Stripe catches more fraudulent payment attempts while minimizing the impact on legitimate transactions.