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Best stress testing reports

April 23, 2013
Read Time: 0 min

For financial institutions that are just beginning to stress test the portfolio or those re-evaluating current processes, a good place to start measuring effectiveness is the usefulness of the stress testing results or reports. 

The primary benefit of stress testing reports shouldn’t be to “check a box” but instead to identify potential weaknesses in the portfolio and allow management to plan accordingly. There are a few recommendations to keep in mind when finalizing reports.

1. Start with reports assessing the high-risk portion of the portfolio. Institutions under $10 billion that aren’t required to stress test may not have comprehensive reports that outline and aggregate weaknesses in every segment of the portfolio. Until that can be achieved, regulatory bodies recommend that reporting and analysis should focus on vulnerabilities, such as non-owner occupied CRE and sub-prime lending portfolios. 

2. Reports should be institution-specific. The content of stress testing reports should incorporate the institution’s unique portfolio and market risks. For example, institutions with significant agriculture portfolios may have different stress scenarios than institutions that don’t. Similarly, market risk may have a bigger influence on some institutions. It’s for this reason that regulators don’t support one, single model for stress testing.

3. Reports should include narrative. While the quantitative outputs of any stress testing model are important, the final report should include more than just numbers. The results, as shared with the board of directors or examiners, should transparently describe the assumptions and calculations used for the stress test and should outline the institution’s plan should such events occur. The reports shouldn’t require interpretation.

4. Stress testing reports should be compiled and shared often. Generally, stress testing in some variety along with accompanying reports for the board should occur on a quarterly basis. 

Sean Delehanty is the senior credit officer of OmniBank, N.A., which has been planning and developing a system for stress testing since around 2008. A good portion of OmniBank, N.A.’s portfolio includes real estate loans, and it began exploring stress testing as executives heard more about the push for clarity on banks’ CRE portfolios. 

Delehanty said that once OmniBank had gathered substantial data, it began deciding what kind of reports would be most useful. He said that’s the time for the bank president and the credit department to be asking: Do we want to find out what loans or what borrowers are struggling? What industries seem to be struggling? Knowing what we know about our market, do we want to stress those industries that may be having a problem in order to target future problems?

Finding the reports that make the information as simple as possible but also useful can be challenging, Delehanty said, but examiners have provided input on the process.

“I sat down with the examiners … and showed them a segment of our portfolio – the income-producing properties – and said, ‘This is what we’ve focused on for about the past three or four months; we’re about 90 percent done with our input and here are some reports and how we plan to use them in the future,’” Delehanty said. “They seemed to be, for one, pretty impressed that … our bank was this far along in the process and that we were thinking about that.”

To learn more about factors in stress tests and these different methodologies, download this new whitepaper, “Stress Testing: Who, What, When and Why”. Or, watch a demo to see how financial institutions are using Sageworks Stress Testing, a portfolio stress testing solution, to systematize their data collection and analysis.

About the Author


Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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