Regulatory Scrutiny of Models Comes with a Silver Lining

There's no doubt that increased regulatory scrutiny of predictive models has been a burden on financial institutions. But there is a silver lining: it has brought about a renewed focus on model management. And once an institution has the people, processes and technology in place for properly managing and tracking their models, it can go from merely complying with regulations to evaluating and refining model performance in ways that control losses and boost portfolio profitability. Compliance and performance – it's a win-win.

Of course, all this is easier said than done. Indeed, setting up an effective model management infrastructure remains one of the toughest challenges facing our clients, who often struggle to:

• Manage growing model portfolios. The sheer number of predictive models is increasing rapidly; large lenders may have thousands in production. This presents a mammoth challenge to manage and track them, and ensure they continue to perform well.
• Respond to regulatory requests. Many find it difficult to respond promptly without dragging down productivity of analytic teams.
• Ensure transparency. Models must be easy to understand, defend and explain—to regulators, customers and even your own executives. For instance, a regulator might ask why a particular characteristic or segmentation was used.
• Keep up with documentation. Tracking and reporting may not be glamorous, but they are clearly a necessity for demonstrating compliance and responding rapidly to questions from management and regulators.

My colleague Andrew Jennings previously set forth nine best practices that can help financial institutions address these challenges. Since his recommendations are worthy of a deeper dive, I’ll be publishing a series of blog posts that explores each practice in greater detail. Stay tuned to the blog.