To some degree, we're living in a world where we are cursed with our own success. Financial institutions have seen tremendous benefits from analytics, and as a result, they are using predictive models on an increasingly broader scale, to measure capital reserve requirements and manage complex customer decisions. But as my rap doppelganger would say: “More Models, More Problems.”
The greater complexity and number of predictive models in use makes it even more difficult to track and manage model performance, not to mention comply with regulatory requirements. Since the financial crisis, banking regulators have increased their scrutiny of how institutions use predictive analytics. These days, regulators are not only concerned with the safety and soundness of the analytics themselves, in terms of how the models are built and whether they are still validating. Regulators are also focused on the impact of the decisions—that is, who gets a particular decision and why. In the US in particular, with the foundation of the Consumer Financial Protection Bureau (CFPB), there’s a growing emphasis on making decisions that are deemed “fair,” and it’s a perspective that I suspect will become increasingly common worldwide. This fairness often translates into making decisions that are consistent, as well as easy to understand and explain.
With model management, the good news is banking institutions can kill two birds—performance and compliance—with one stone. The same model tracking, monitoring and documentation practices required for regulatory compliance also enable institutions to evaluate and refine model performance in ways that control losses and boost portfolio profitability.
I recently discussed this very subject in the webinar Comply and Compete: Model Management Best Practices. I invite you to watch the recorded session, where I share five smart practices with the dual goals of compliance and competitive advantage in mind.
