Advances in Fraud Detection with Automated Machine Learning

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Preventing fraud is a mission-critical objective of every banking institution. But those committing fraud continue to evolve their tactics to evade detection by even the best prepared organizations. But with recent advances in artificial intelligence (AI) and automated machine learning, banks are able to improve the accuracy of their fraud prevention models faster and more efficiently than ever before.

 

In this on-demand webinar, you’ll get an overview of the current state of machine learning in fraud detection – and learn how you can stay one step ahead of those looking to harm your business.

You’ll discover how Automated Machine Learning provides:

  • The ability to develop and refresh fraud detection predictive models at any time
  • The ability to deploy models with a click of a button
  • The ability to operationalize fraud detection models by following a process that is user-centric
  • DataRobot's platform makes my work exciting, my job fun, and the results more accurate and timely -- it's almost like magic!
    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

    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

  • DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.
    Deena Narayanaswamy
    Deena Narayanaswamy

    Head of Data Insights, Avant

  • 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.
    Evariant