Complete guide to preventing ATO
Account takeover (ATO) is a growing problem. Learn how to protect your users’ accounts, your brand, and your bottom line.
50% reduction in chargebacks
6.5x more fraud blocked automatically
Our business is time-sensitive, and we needed to find a solution that quickly and effectively reduced fraud. Fraud used to keep me up at night – now it doesn’t.
Sam Shank, CEO and Co-founder
Streamlined and data-driven decisions
Utilize Lists, network visualization, and Score API
We process thousands of transactions every day; if we didn’t have Sift Science and our other systems, we would need at least 30 people reviewing daily.
Gaspar Salva, Revenue Protection Analyst
Incredible accuracy right off the bat
Less friction for good customers
We didn’t have a serious fraud problem, but we wanted to reduce the friction for our good customers, improve the experience for users – we wanted to trust our clients, and we wanted to be confident in our visitors.
Gustavo Tonti, Fraud Manager
Saw useful results immediately after setup
Constant improvements in accuracy for preventing fraud
Sift Science is flexible enough that it can handle all of our needs when it comes to fraud detection. It's holistic.
Jan Hecking, Principal Software Engineer
Sift Science prevents fraud and increases revenue for top travel companies around the world. Our machine learning automatically utilizes learnings from our global network of customers, as well as your own data, to provide a clear view of good and bad users.
We are the only company to provide true real-time learning, reevaluating your risk every time a user takes action on any site or app across our customer network.
Automatically block risky users and reduce your reliance on expensive multi-factor validations and manual review.
When you know who your good customers are, you can eliminate friction and make it easier for them to buy.
Sift Science’s global data network utilizes learnings from travel companies worldwide, so you can protect yourself from new threats – no matter where they occur.
Our machine learning models know your business and are constantly learning from your customers across our entire network. Some example signals include: