MRM: How Banks Are Scaling Models in the Age of AI
As artificial intelligence and regulatory changes drive rapid growth in model inventories, banks are entering a new phase of Model Risk Management (MRM). A white paper by Risk.net and Moody’s highlights how financial institutions are adapting their MRM frameworks to balance compliance with innovation. The report draws on a survey of 79 industry professionals and interviews with experts from major banks like Standard Chartered, Citigroup, and Nordea. It explores key challenges, including modernizing governance, managing complex model ecosystems, and integrating AI and machine learning into existing validation processes. Centralized platforms and automation are identified as crucial tools for scaling controls without stifling speed or competitive advantage. The analysis addresses bottlenecks in development and deployment while outlining the future of model risk functions in an AI-enabled banking environment. Ultimately, the piece emphasizes the need for banks to strengthen governance and efficiency to meet rising expectations around inventory management and AI oversight, ensuring they remain compliant while preserving their ability to innovate in a rapidly evolving technological landscape.
Wire timeline
MRM: How Banks Are Scaling Models in the Age of AI
As artificial intelligence and regulatory changes drive rapid growth in model inventories, banks are entering a new phase of Model Risk Management (MRM). A white paper by Risk.net and Moody’s highlights how financial institutions are adapting their MRM frameworks to balance compliance with innovation. The report draws on a survey of 79 industry professionals and interviews with experts from major banks like Standard Chartered, Citigroup, and Nordea. It explores key challenges, including modernizing governance, managing complex model ecosystems, and integrating AI and machine learning into existing validation processes. Centralized platforms and automation are identified as crucial tools for scaling controls without stifling speed or competitive advantage. The analysis addresses bottlenecks in development and deployment while outlining the future of model risk functions in an AI-enabled banking environment. Ultimately, the piece emphasizes the need for banks to strengthen governance and efficiency to meet rising expectations around inventory management and AI oversight, ensuring they remain compliant while preserving their ability to innovate in a rapidly evolving technological landscape.
Home