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On-Demand Webinar

Enhancing Fraud Detection with Automated Machine Learning and Streaming Analytics

Building predictive applications allow companies to respond to new threats and take advantage of developing opportunities. But executing these new applications against high-volume event streams with sub-second latency requires a powerful combination of machine learning and streaming analytics.

In this webinar, you’ll learn how to create and evaluate new machine learning models with DataRobot and deploy them within the SQLstream Blaze streaming analytics engine – so that you can identify risk in real-time and prevent fraud as it happens – rather than after the fact.

You'll discover how DataRobot and SQLstream Blaze provides:

  • Automated machine learning models that can be created by anyone
  • Rapid deployment against incoming, high-volume events with extremely low-latency
  • The ability to update those models seamlessly - with no downtime
  • Deep transparency, including prediction reason codes, to enable rapid, targeted investigations


  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely – it's almost like magic!
    Omair Tariq
    Omair Tariq

    Data Analyst, Symphony Post Acute Network

  • I think we need to take it upon ourselves in the industry to build the predictive models that understand what the needs and wants of our customers are, and go through the whole curation process, become their concierge.
    Oliver Rees
    Oliver Rees

    General Manager – Torque Data at Virgin Australia

  • At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.
    Akshay Tandon
    Akshay Tandon

    VP of Strategy Analytics, LendingTree

  • We know part of the science and the heavy lifting are intrinsic to the DataRobot technology. Prior to working with DataRobot, the modeling process was more hands-on. Now, the platform has optimized and automated many of the steps, while still leaving us in full control. Without DataRobot, we would need to add two full-time staffers to replace what DataRobot delivers.