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Sift Science

Fight fraud with real time machine learning. Integrate in an afternoon.
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What is Sift Science?

Sift Science catches fraud by using large-scale machine learning to identify those patterns automatically.
Sift Science is a tool in the Fraud Detection as a Service category of a tech stack.

Who uses Sift Science?

Companies
8 companies reportedly use Sift Science in their tech stacks, including Instacart, Zola, and Weroom.

Why developers like Sift Science?

Here’s a list of reasons why companies and developers use Sift Science
Top Reasons
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Sift Science Reviews

Here are some stack decisions, common use cases and reviews by companies and developers who chose Sift Science in their tech stack.

Brandon Leonardo
Brandon Leonardo
Sift Science
Sift Science

So before, we were having … not a huge, but we were having a fraud problem where people were placing orders, and they were getting fulfilled even though they were very obviously using a stolen credit card. So we started using Sift, which basically, we send Sift a collection of signals from users, so like they added this item to the cart. They tried to add a credit card, but it failed. They added this address and then they submitted. So we send them the collection of signals, and they run machine learning on those signals and send us back a classification of the user, and we use that as one of our elements to decide if we should fulfill that order or not.

So that's all happening in real-time. Without human intervention, you can tell. If they have a very high Sift score, you can say, “This person is clearly fraudulent. They’re using credit cards from six different places and ordering only Patrón.” Sift Science

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Sift Science's Features

  • Reduce manual reviews & chargebacks
  • Detect Fraud Automatically in Real-Time
  • Distill Patterns From Data
  • Billing & Shipping Address Mismatch
  • Device Fingerprint
  • Travel Velocity

Sift Science Alternatives & Comparisons

What are some alternatives to Sift Science?
ThisData
We use behavioral patterns to build an identity profile for each user. This provides your app with a second factor of authentication that doesn't add friction to the user experience, or even require the user to opt-in.

Sift Science's Followers
6 developers follow Sift Science to keep up with related blogs and decisions.
DiaryofaMadeMan
edwardsmith21
Hary Selman
leximo
stalaie
MohammadAsh15