Re-Imagining Model Risk Management
to Capture the AI Opportunity in Banking
The Incumbent’s Secret Weapon?
Is MRM a key advantage for banks in the AI era?
As AI/ML models become more pervasive in banking, they are becoming subject to intensifying regulatory scrutiny.
While sometimes seen as a barrier to speedy adoption of AI/ML models, MRM frameworks and experience can be turned into a source of competitive advantage.
- How leading banks are responding to MRM challenges in a highly regulated environment (and what to expect from regulators in the near future)
- 5 areas where leading practitioners are beginning to incorporate technology enhancements to better manage AI model risk
- Technical enhancements banks are introducing to manage model risk for AI and Machine Learning
- How AI introduces the need for a new MRM operating model
Get the MRM whitepaper.
Meet the Authors
Professor of Electrical & Computer Engineering and Computer Science at Carnegie Mellon University for over a decade, Anupam is passionate about enabling responsible adoption of artificial intelligence.
Shameek has spent most of his career in driving responsible adoption of data analytics/ AI in the financial services industry. Most recently, Shameek was Group Chief Data Officer at Standard Chartered Bank.
TruEra provides AI Quality solutions that analyze machine learning, drive model quality improvements, and build trust. Powered by enterprise-class Artificial Intelligence (AI) Explainability technology based on six years of research at Carnegie Mellon University, TruEra’s suite of solutions provides much-needed model transparency and analytics that drive high model quality and overall acceptance, address unfair bias, and ensure governance and compliance.