Security & Fraud

Stripe Enlists Machine Learning In Fraud Fight

Stripe Radar Fraud Prevention

Online commerce platform Stripe announced Wednesday (Oct. 19) the launch of its set of machine learning-based tools that will help businesses to fight fraud.

Stripe Radar combines data from the hundreds of thousands of businesses processing transactions on the Stripe network with intelligence from banks and credit card networks to provide users with the ability to customize the fraud defenses for their individual business.

The fraud prevention service is fully integrated into users’ Stripe accounts and requires no additional integration or setup in order to work.

“With Stripe Radar, businesses essentially have caller ID for incoming charges,” John Collison, president and cofounder of Stripe, explained.

“Stripe’s behavior network uses machine learning to learn from hundreds of thousands of businesses around the world running on Stripe, as well as signals from our financial partners. Because of this network, Stripe Radar can effectively spot patterns and detect fraud, protecting every Stripe user from the moment their first charge comes in,” Collison continued.

Though fraud remains a significant burden and challenge to online businesses, which are liable for any payment fraud that takes place, Stripe said it looks to help merchants overcome the burden of time-consuming legacy fraud tools that can block many legitimate orders.

According to Stripe, Radar’s machine learning-powered tools were able to block more than $40 million of attempted fraud within a two-month beta period for the nonprofit organization Watsi, which helps fund medical treatments for people globally.

“For Stripe users, Radar dramatically reduces what’s needed to effectively manage fraud by providing a sophisticated set of tools that are built on top of Stripe’s machine-learning algorithms,” the company said in a statement. “These include a smartly prioritized list of flagged charges and an ability to preview and set custom rules (without any code or engineering work).”

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