Sift Science


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    • Sift Science
    • 51 - 200 Employees

Description: Sift Science has built the world’s most advanced fraud detection system. Using large-scale machine learning technology to predict fraudulent behavior with unparalleled accuracy, Sift Science leverages a global network of fraud data. Catch the fraud that is unique to your business and train your customized model to stop fraudsters in real time. Sift Science’s flexible, adaptive, and automated solution helps businesses of all sizes detect and prevent fraud, before it hits your bottom line.

Products: Payment Fraud: Accurately detect fraudsters in real time with machine learning. Prevent fraudulent transactions and chargebacks before they happen.

Content Abuse: Prevent bad actors from posting malicious or low-quality content that can permanently damage your business’ reputation and drive good users off your site.

Promo Abuse: Leverage real-time machine learning to accurately prevent promo abusers from depleting your marketing budget.

Account Abuse: Accurately detect bad actors in real time with machine learning, and prevent them from creating phony accounts on your site or app.

Device Fingerprinting: Uniquely identifies and tracks every device that accesses your site. Once you know which devices are used by the bad guys, blocking them is a piece of cake.

Headquartered: San Francisco, CA
Date Founded: 2011

Case Studies (0)

Executive Interviews (1)

Interviewee Name: Kevin Lee

Company Role: Trust and Safety Architect

Interview Summary: Fraud attacks have become much more sophisticated. Account takeovers are happening more often. Many security attacks involve multiple methods and unexpected attacks can devastate businesses in just a few days, as we saw with Neiman Marcus and Target. False promotion and abuse is seen not only on social media sites but is also targeted at business. To combat these risks, fraud solutions need to be smarter to keep pace with fraudsters to prevent attacks and react quickly when they do happen. This requires a fast-learning solution with the ability to continually evolve – which calls for the application machine learning for fraud detection. In this episode we talk to Kevin Lee from Sift Science and examine the shifts in the info security landscape over the past ten or fifteen year. Lee also highlights what new kinds of fraud are now possible and what machine learning solutions are available.

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Mentions on TechEmergence(1)

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