Enabling the AI-Driven Enterprise

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


Jacob Kosoff
Head of MRM and Model Validation, Regions Bank
Scott Hallworth
Chief Data Officer & Chief Model Risk Officer, Capital One