We’re Thankful For…

It’s starting to feel a lot like Siftsgiving! Here in San Francisco, the weather’s getting cooler, and Sifties are in and out of the office as they spend time with their families and friends. While fraudsters never rest– and neither do our machine learning algorithms – our team is taking time to celebrate the season and appreciate all that we have

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How Shippo Beats Fraud (With No False Positives)

Companies battling fraud often face a trade-off: optimize for conversion (which could let some fraud through) or optimize for more stringent fraud prevention (which could inadvertently block some legitimate customers)?

Read our new case study to find out how Shippo – a shipping app that allows e-commerce merchants to easily print labels and track packages – significantly reduced fraud, while maintaining zero false positives and no full-time employees dedicated to fraud prevention. 

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Get Your E-Commerce Fraud Team Ready for the Holiday Rush

Santa has a leisurely 364 days to figure out who’s approved for gifts, but what about your business? You face the “naughty or nice?” question every single day – and during the holiday season, more orders and less time can create a perfect storm for fraud. Here are some tips to help you prepare for the busiest time of year:

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Feature Spotlight: The Hidden Clues in an Email Address

We love giving our users an opportunity to discover more about what’s going on behind the scenes. Sometimes we’ll hear one of our customers say, "Can machine learning really do that?" or "Wow, I didn't realize that's how Sift Science worked!"

So we're introducing a new blog series called Feature Spotlight to give you a peek under the hood of Sift Science and dig into the various features that make up our machine learning platform. In each post, we'll examine one of the features that Sift Science analyzes to detect fraud – ranging from "Does the user share a Device Fingerprint with a known fraudster?" to "transaction velocity over the past day". Check it out and send us your questions!

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