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On-Demand Webinar

Minimizing Model Risk with Automated Machine Learning

In today’s complicated financial landscape accurate models are a necessity for banks to remain competitive, but developing accurate models is challenging. Models are inherently complex — and if developed poorly can do more harm than good.


In this panel discussion, we cover how banks can use Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We discuss current trends and expectations for model risk management regulatory compliance, and how industry-leading financial institutions are leveraging Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts.

You'll discover:

  • How Automated Machine Learning enhances compliance to model risk management regulation (FIL 22-2017, SR 11-7, OCC 2001-12)
  • Key terms and functions required by new regulation
  • How Machine Learning reduces model risk, while ensuring the implementation of cutting edge machine learning models


  • 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.