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The role of expert judgment in risk rating

September 29, 2017
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This post is authored by Alison Trapp, senior consultant at Sageworks.

Imagine the following scenario. An analyst is preparing a risk rating as part of an account’s annual review. She has spread the most recent financial data and updated answers to qualitative questions in the scorecard. She knows this credit well because she has managed it for two years and talks to the CFO regularly. When the risk rating is calculated, however, the scorecard result does not match what she was expecting. What should she do?


The first step may be obvious but worth stating. Check the inputs. If the financials were imported via automation such as Sageworks Electronic Tax Return Reader, the analyst should check that she chose the correct time periods to include in the analysis. If they were input manually, she should check for transposed numbers, “fat finger” errors, and the like. Often the analyst will find a manual input mistake by confirming the calculated net income, total assets and shareholder equity.


The next question is, “Does the analyst know something significant that is not a scorecard input?” Scorecards are built to provide consistency in risk rating. They focus on common elements that will be applicable to nearly all credits. Sometimes there is a unique situation that the scorecard does not recognize even though it will drive the risk rating. For example, the analyst may believe a pending lawsuit is likely to impact the borrower’s ability to repay debt. Her institution’s scorecard does not include pending lawsuits as a factor since it developed the scorecard to cover most accounts and lawsuits are not the norm for its borrowers. In this case, the analyst must use her expert judgment to assign a risk rating that differs from what the scorecard indicates.


The last question is “Does the analyst know something significant that has not yet flowed into financial results?” This question is similar to the question above. Here, the analyst has forewarning of an event that will manifest in a scorecard input eventually. She correctly downgrades the risk rating when she becomes aware of the negative information and does not wait for cash flow to be impacted. For example, the borrower tells the analyst that a large contract was lost and cash flow next quarter will be down significantly. The analyst adjusts the risk rating immediately, even if the scorecard calculates a better rating. As the impact of the lost customer comes to fruition, the scorecard will reflect the same rating that was assigned with expert judgment.


It is important to note that information that will positively impact the risk rating would not be treated the same way. In that case, the analyst would wait to upgrade until the positive event was actually seen in the numbers. The analyst is expected to downgrade on expectations and upgrade on performance.


An institution should have a limit for how often final ratings assigned with expert judgment can differ from the scorecard. Limiting differences results in a more predictive scorecard that analysts and regulators can trust. Still, differences will occur. Reviewing them regularly helps the institution identify when a scorecard needs to be revised. It also provides a control on consistency to ensure that analysts are exerting expert judgment appropriately.


At the end of the day, the scorecard is a tool to drive consistency. However, it is only a tool, and as the OCC states, lending institutions “should use such systems to supplement more traditional tools of credit risk management: credit analysis, risk selection at origination, and individual loan review.”


To learn more risk rating best practices, view the on-demand webinar.

Download the free eBook Commercial Risk Ratings Considerations to learn best practices for building your risk rating system.


Alison Trapp is a Senior Consultant with Sageworks’ Advisory Services team and is focused on credit. Alison joins Sageworks after spending 17 years on the commercial credit risk team at GE Capital and a year consulting with mid-sized commercial banks. She has particular expertise in credit administration and policy implementation.

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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