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How to Weight Your Risk Rating System

December 8, 2015
Read Time: 0 min

In today’s environment, having an effective risk rating system in place helps beyond determining an institution’s credit approval process or loan pricing. These systems also impact broader risk management practices, including setting an institution’s reserve, stress testing, determining risk appetites and strategic planning. With little prescriptive guidance available, banks and credit unions can customize their risk rating system to fit the unique characteristics of their portfolio.

Determining the right risk rating scale – typically from five to nine ratings – is only a portion of the process. To determine the thresholds that comprise each rating, institutions should implement a weighting system to categorize a potential loan more appropriately. In many cases, there are certain risk dimensions that are more closely tied to loan performance and should have more impact on the final rating.

The five Cs of credit are often the basis for determining the risk rating, so the first step is to weight each of the five Cs. The weighting percentages will vary from institution to institution, so documenting why each component is weighted a certain way is important. Here’s an example of how an institution might weight the five Cs of credit:

• Capacity = 35 percent
• Capital = 15 percent
• Collateral = 10 percent
• Conditions = 15 percent
• Character = 25 percent

After weighing each of the five Cs, the next step is to weight the dimensions within each. Banks and credit unions should show priority for more objective criteria, like financial performance, over more subjective criteria, like management experience.

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Continuing with the above example, here’s an example of how an institution could weight dimensions under Capacity:

• Proposed Debt Service Coverage = 20 percent
• Historical Debt Service Coverage = 20 percent
• Sensitivity to Business to Change in Income = 10 percent
• Sensitivity to Interest Rate Risk = 10 percent
• Business Credit Score = 10 percent
• Lease Terms / Customer Base = 30 percent

And, here’s an example of how to weight dimensions under Character:

• Borrower’s Financing Alternatives = 25 percent
• Management of the Business = 25 percent
• Guarantor’s Credit Score = 25 percent
• Tangible Net Worth of Guarantors = 25 percent

After weighing each dimension, the next step is to define the thresholds to be used in rating loans. For example, with historical debt service coverage ratio (DSCR), institutions can assign a certain pass (or criticized) rating based on what the ratio is. In this scenario, perhaps a risk rating of 1 is when historical DSCR is greater than 2.5, a risk rating of 2 is when historical DSCR is between 1.75 and 2.5, a 3 rating is between 1.4 and 1.75, a 4 rating is between 1.2 and 1.4, etc.

Once the risk scores have been calculated for each dimension, institutions will then need to multiply the risk score for each by the pre-determined weight percentage.

risk score calculation

Then, each raw score is added together for a total for each component (i.e., capacity).

The final step to assigning a risk rating is to multiply the raw score of each of the five Cs of credit by their weight to get a raw score for each. Then, the raw scores should be totaled and fit into a corresponding rating range to provide the appropriate risk rating. Here’s an example:

raw score calculation

score scale

In this example, if all the five Cs add up to 312, the loan would receive a risk rating of 4.

Having this standardized process in place will reduce subjectivity and increase defensibility on why loans are rated a certain way, which not only helps conversations with other functional areas but also with examiners and auditors.

To learn more about leveraging risk ratings for the ALLL calculation, view the recorded webinar: The Real Price of Risk.

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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