By Raman Mandapaka
Ever since the enactment of Dodd-Frank, bank managements have used stress test models for the purposes required by law: to identify and quantify risk in their loan portfolios, and to employ that data as the basis for capital allocation. But viewing stress test models so narrowly, merely as the means to fulfill a regulatory requirement, is a major missed opportunity. Banks might consider a more valuable role for their stress test models and the related data—transforming them from passive risk measures into dynamic tools for optimizing business strategy.
Large, regional and midsize banks have clearly made significant investment in stress test models, both in terms of dollars spent and the staff support and operational processes needed to collect and analyze the data. Instead of viewing this investment as a “sunk cost” that can’t be recouped, banks could actually generate a return on this investment by leveraging the models and related data to help determine go-to-market strategies under changing economic scenarios.
From capital allocation to dynamic optimization
The original purpose of stress test models, of course, was to assess how a bank’s portfolio might behave under a range of economic scenarios, and to ensure that enough capital was on hand to mitigate risk under those scenarios. Since the Federal Reserve has determined that the major banks under its supervision now have sufficient capital, it is worth considering how the stress test models also can be applied to a bank’s overall business. Data gleaned from the stress test model might enable a bank to adjust its risk appetite—and thus its portfolio allocation—in response to the direction of key economic factors. By projecting certain economic assumptions forward, and using the analytics embedded in the model, a bank might decide to scale back commercial real estate lending, for example, or shift resources to asset-based loans.
Such dynamic strategies clearly have implications for the P&L, enabling a bank to reduce its concentration of riskier assets or to shift its focus to products and services with higher margins, depending on their economic outlook. An added benefit is that the bank may be able to release capital allocated to certain parts of the portfolio and redeploy that capital to support growth. Furthermore, for banks that are pursuing an aggressive M&A strategy, the ability to use stress test models and data proactively will help in analyzing and restructuring the portfolios of acquired companies.
A valuable pool of big data
Another unintended benefit of stress test models is the opportunity to leverage the vast amounts of data banks had to collect in order to build the models. All businesses today are grappling with the challenge of gathering more—and more meaningful—data. Yet for banks, highly valuable data may already be available if they know where to look.
In the process of constructing their stress test models, banks have collected a wealth of data—on their portfolios and borrowers, to cite just a few examples. They also have streamlined the data collection process, confirmed data quality, and improved accessibility to the data across the enterprise. Such information is of great value to business units at the bank, and efforts should be made to apply this data to drive strategic decisions beyond simple regulatory compliance applications.
Leadership from the top
I believe many banks—particularly small and midsize institutions—may miss this opportunity due to internal silo issues. Stress test modeling is handled in the risk management group, while strategic decisions are the province of business unit and marketing leaders. As a result, risk managers may not see the business applications for their models, and business unit heads may not realize the wealth of data and analytical resources built into the models.
The impetus to bridge this gap must come from the top, with CEOs, COOs or CFOs creating working groups of risk management, business unit, marketing and other executives, and possible outside consultants, to develop ways to leverage the stress test models to help the bank grow.
Raman Mandapaka is managing director and head of quantitative analytics and risk management at Navigant Consulting.
