A new approach to fraud
Why machine learning?
Traditional fraud prevention solutions regularly deliver false positives, locking out legitimate buyers when they wrongly detect fraudulent behavior. Machine learning, however, adapts to your consumer profiles to offer a truly customized solution at speed and scale.
In a nutshell, live machine learning allows you to accurately and instantly predict whether a customer is trustworthy or not. Savvy organizations can use this knowledge to focus on improving the experience for genuine customers, while keeping fraudsters at bay.
Break the rules, trust the score
Inflexible, rule-based fraud prevention solutions largely rely on a team of operators to perform manual reviews. Not only is this needlessly time-consuming and expensive; the process is also prone to error, resulting in missed revenue opportunities and worse: irate customers.
In contrast, machine learning allows fraud managers to monitor buyer behavior from fraud scores calculated based on countless data sets and signals. This offers unmatched accuracy, ensuring precise fraud detection and supporting higher authorization rates.
Lock out fraudsters
The highly dynamic and scalable platform is the only machine learning fraud solution that updates in real-time, so that if fraud is attempted on any of Sift Science global network of 6,000+ customers’ sites or apps, you’ll be able to prevent similar behavior on your site – instantly.