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.
Minimizing Model Risk with Automated Machine Learning will demonstrate how banks can use Automated Machine Learning to gain a competitive advantage, while quickly aligning their business operation to regulatory requirements. We'll provide an overview of current trends and expectations for model risk management regulatory compliance, and how industry leading financial institutions are leveraging Automated Machine Learning to provide a much stronger framework for model development and validation than traditional manual efforts.
Attendees of this panel discussion will learn:
- 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 Automated 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