Executive summary

In this paper, we propose a logical architecture that supports application of best practices and standards of the financial services industry in ongoing monitoring (OM) of front office model performance. This construct evolves from ever-increasing regulatory mandates the world over, latent economic incentives of OM and attendant generic frameworks. We also discuss a few best practices in OM of front office model performance, such as streamlining model workflows, removing redundancies by leveraging reviews across asset classes, and exploring automation scope.

Introduction

Over the years, financial institutions (FIs), by choice or out of necessity, have raised their skin in the game of deploying pricing and market models. CRISIL reckons this trend is only likely to get stronger in the foreseeable future with new models coming on board. As the extent and depth of issues related to financial decision-making increase, the range and complexity of the post-conventional (solution) risk models built on quantitative finance and sophisticated analytics are anticipated to increase correspondingly.

However, as financial history shows, the chances and consequences of losses are respectively real and massive, from the mismatch between an always-expanding population of complex models and the real-world environment. Unsurprisingly, this model risk also invites greater regulatory scrutiny and mandates. Hence, the need for an efficient and robust model risk management (MRM) function, which validates and regularly monitors the risk and performance of these models, is acute. Our experience tells us that our client FIs, who are overwhelmingly valueseeking, never shy away from investing substantively in the design and implementation of MRM architecture and ongoing model monitoring. While OM processes primarily covered models addressing regulatory mandates such as stress testing and regulatory capital allocation, these are now being increasingly employed for asset-liability management (ALM) and pricing models. 

Past episodes of financial disasters, such as the fall of Long-Term Capital management during 1998 following the East Asian and Russian crises1 , the 2007-08 Global Financial Crisis2 , and the 2012 London Whale trading debacle3 , highlight that the reasons of model risk materialisation can range from incorrect use and even misuse of financial models to defective models. And these can have severe economic and reputational consequences for FIs. 

The painful aftermath of the Global Financial Crisis provided a conducive macro-financial environment for the US Federal Reserve (the Fed) and the Office of the Comptroller of the Currency to issue a Supervisory and Regulatory (SR) guidance on MRM — SR 11-7 — in 2011. SR 11-7 mandates FIs to undertake periodic reviews of models in keeping with the latter’s complexity and materiality, and overall use relative to their operations. The definition4 of model risk in this document has become the gold standard. Furthermore, the European Central Bank (ECB)5 and the Bank of England (BoE)’s Prudential Regulatory Authority (PRA)6 are among the advanced economies’ regulators to issue supervisory guidance on model risk and OM of model performance.