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On-Demand Webinar

Enhancing Anti-Money Laundering (AML) Programs with Automated Machine Learning

Compliance organizations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs.  Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates.  Automated machine learning provides a solution to address this challenge.

 

In this webinar, Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

You’ll discover how Automated Machine Learning provides:

  • The ability to develop and refresh AML predictive models at any time
  • The ability to deploy models with a click of a button
  • The ability to operationalize AML models by following a process that is user-centric

Speakers

  • 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.
    Evariant
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