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Real-time fraud prevention for travel companies

Delight travelers, unlock new opportunities, and prevent travel fraud with machine learning.

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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

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Get a holistic picture of travelers and fraudsters

We learn from data at every point in the customer journey, including search, browsing, and buying behavior. We capture a user's profile across all devices – mobile, desktop, and mobile web.

  • Unlock new opportunities

    Open up new markets, offer promotions and packages, and expand loyalty programs without the risk of fraud.

  • Block fraud in real time

    Stop fraudulent transactions and chargebacks before they happen – across desktop, mobile apps, and mobile browsers.

  • Increase issue rates

    Accurate real-time decisions empower you to instantly accept bookings from legitimate last-minute travelers.

A sophisticated, adaptable solution


  • Thousands of data points analyzed

    We present a holistic picture of a user's risk, based on thousands of identity and behavioral signals.

  • Data from travel businesses worldwide

    Bolster your custom model with learnings from our global customer base of 6,000+ sites and apps.

  • Real-time decisions

    Our machine learning models recalculate risk scores every time a user takes action on your app, or on any app or website in our network.

  • Custom models that keep learning

    Send data unique to your business, and your machine learning model will grow increasingly accurate and effective.

Fraud prevention tailored to your travel business

Our solution is fully customizable to your data and fraud detection needs. Sift Science can learn from any data you send us.


Examples of custom fields:
  • Flight details
  • Train reservations
  • Car rentals
  • Hotel bookings
  • Packages
  • Destinations
View integration guide