Radar prevented $4B in attempted fraud in 2017 alone, by learning from the transactions processed on the Stripe network for hundreds of thousands of businesses and helping users tailor defenses for their individual companies. Operating across enterprises and leading-edge startups in more than 100 countries, Radar instantly combines transaction data with intelligence from banks and credit card networks. With today’s announcement, Stripe makes a new bundle of advanced fraud prevention tools available to risk professionals within large businesses.
“Stripe’s machine learning models are now trained on hundreds of billions of individual data points drawn from the Stripe network. We’ve used these data points to update our fraud models, helping businesses on Stripe more accurately identify fraudsters and reduce fraud rates by up to 25 percent, while keeping payment acceptance rates high,” said Michael Manapat, engineering manager for Radar and Machine Learning, Stripe. “With Radar for Fraud Teams, we’re launching tooling for teams fighting fraud, particularly those of our larger users, giving them the granular controls to more effectively and efficiently manage fraud.”
Enhancing Radar with faster machine learning improvements Today marks the most comprehensive update to Radar’s machine learning models since its launch in 2016. Stripe added hundreds of new signals that distinguish legitimate customers from fraudsters, including purchase patterns that are highly predictive of fraud. As a result, the upgraded machine learning models help businesses reduce fraud by up to an additional 25 percent (while keeping payment acceptance rates high).
Proxy detection is one example of a new, highly predictive signal that has been incorporated into Radar’s machine learning models. It measures the round-trip time between Stripe and a potential fraudster’s browser, helping to pinpoint if a fraudster is using a proxy or VPN.
Radar also constantly evaluates patterns that are unique to a user’s business. Now on a daily frequency, Radar updates and re-trains its models, in the process evaluating each user’s unique transaction profile to determine which model will achieve the best performance. By training machine learning models for specific use cases, Radar can precisely serve businesses of all sizes and types with more accurate and performant results. By using a cloud-based service, users will automatically benefit from future daily updates, with defenses that will adapt even more quickly to constant changes in fraudster tactics.
Introducing ‘Radar for Fraud Teams’
Designed for sophisticated teams of fraud professionals, Radar for Fraud Teams improves visibility and offers granular control for identifying and preventing fraud.
Now, fraud professionals within large organizations can use Radar to optimize for:
- Faster and more accurate reviews: When reviewing payments, Radar shows relevant info and related payments that a user’s business has processed. By gaining a broader view into attributes like a typical purchase pathway or a mismatch between country of incoming IP address and country where a card was issued, risk professionals can more quickly evaluate fraudulent activity.
- Custom rules with real-time feedback: Radar’s fraud prevention logic can now be customized with unique rules (i.e. “block all transactions above $1,000 when the IP country does not match the card’s country.”) It also previews the rule on historical data to help risk professionals evaluate its impact on live transactions.
- Custom risk thresholds: Radar helps risk professionals to maximize revenue by allowing them to set custom thresholds at which to block payments.
- Block and allow lists: Users now have an easy way to create and maintain lists of attributes—card numbers, emails, IP addresses, and more—that should be consistently blocked or allowed.
- Rich analytics on fraud performance: Radar highlights dispute trends for a user’s business, the effectiveness of reviewing flagged payments, and the impact of rules customized for a user’s business.
“Online businesses are experiencing dramatic shifts as fraud becomes more complex, increasingly global, and expensive to combat. With the ability to constantly learn from data, machine learning has emerged as an adaptive and efficient way to thwart billions of dollars in losses,” said Jordan McKee, principal analyst, 451 Research. “Stripe Radar now makes this layer of intelligence available to dedicated teams of fraud professionals within a business, empowering them with data and tools to enhance their ability to combat fraud. We also see a fit for Stripe Radar with multinational enterprises, who can use it to augment their in-house fraud teams with machine learning and contextual review tools. With today’s launch, Radar becomes the latest example of how Stripe is broadening its product offering to serve increasingly large, sophisticated companies.”
Radar for Fraud Teams has already made fraud management easier and more effective for Watsi, Fitbit, Restocks, Patreon, and more. To learn more, visit stripe.com/radar.