Model risk and the importance of model risk management has gotten significant attention in the last few years. As financial companies increase their reliance on quants and quantitative models for decision making, they are increasingly exposed to model risk and are looking for ways to mitigate it. The financial crisis of 2008 and various high profile financial accidents due to model failures has brought model risk management to the forefront as an important topic to be addressed. Many regulatory efforts (Solvency II, Basel III, Dodd-Frank etc.) have been initiated obligating banks and financial institutions to incorporate formal model risk management programs to address model risk. Regulatory agencies have issued guidance letters and supervisory insights to assist companies in developing model risk management programs. In the United States, as the Dodd-Frank act is implemented, newer guidance letters have been issued that emphasize model risk management. Despite these efforts, in practice, financial companies continue to struggle in formulating and developing a model risk management program. A lot of companies acknowledge and understand the model risk management guidelines in spirit but have practical challenges in implementing these guidance letters. In our prior article on model risk , we discussed many drivers to address model risk and challenges in integrating model risk into the quant development process.
In this article, we will discuss ten best practices for the implementation of an effective model risk management program. These best practices have evolved from discussions with industry experts and consulting projects we have worked with in the recent years to create robust risk management programs. These best practices meant to provide practical tips for companies embarking on a formal model risk management program or enhancing their model risk methodologies to address the new realities.