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How to Create a Credit Risk Rating System

October 5, 2015
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For banks and credit unions, a popular tool to monitor credit risk is a standardized risk rating system, which can serve several purposes. These systems often determine credit approval processes, covenants placed on the borrower and how loans should be priced. They can also form the basis for broader risk management practices – for instance, setting the reserve, stress testing the loan portfolio, setting risk appetites and strategic planning. Unfortunately, there are no specific requirements for credit risk rating systems, though there are several expectations outlined in OCC guidance. That said, banks and credit unions have the ability to customize a rating system to best fit the unique risk characteristics of their institution.

For most community banks and credit unions, internally-developed risk rating systems are used. These systems typically use a scorecard rating based on the level of risk in Pass and Criticized categories. Some institutions may have one system for all loan types, while others may have different templates for various loan types.

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The goal of a risk rating system should be to assess a borrower’s potential future payment volatility by reviewing several characteristics. For instance, when assessing the current financial health of the borrower’s business, global cash flow, global debt service coverage, global debt to equity, financial statement strength and loan to value and collateral value for the loan should be considered. In addition, financial projections for the business and industry health should be reviewed and incorporated into the grade. The important item to remember here is to focus on future performance, not just historical data.

As noted above, institutions have the flexibility to customize their risk rating system and the number of ratings on the scale. Many institutions use a nine-grade system, though five to ten total ratings is acceptable. The ratings are typically divided into two categories – Pass and Criticized. Within Pass, one to five ratings are typically used depending on the level of granularity needed. The ratings can vary from essentially risk-free to modest risk to additional review required. If an institution has three Pass categories, but see the loans pooling in one of them, that’s a good time for the institution to break down or more granularly define pass loans into four or five risk grades instead of three.

Within the Criticized portion of the risk rating system, regulatory guidance on rating credit risk is more specific and requires the use of four categories to identify different degrees of credit weakness:

  • Special Mention – The loan has a potential weakness that could lead to future credit deterioration without appropriate monitoring.
  • Substandard – The loan has identified weakness(es) that will likely lead to loss without appropriate action.
  • Doubtful – The loan has identified weakness(es) that makes full repayment unlikely. In this category, the loan is placed on nonaccrual status.
  • Loss – The loan is uncollectible, usually because the borrower has declared bankruptcy, discontinued payments or closed the business.

The below example of a risk rating system is based on a nine-rating scale. Again, banks and credit unions are not required to use a nine-rating scale. A scale that makes the most sense for each institution, based on their individual risk profile, should be used.

Risk rating scale

It is also important to remember that there are limits to credit risk rating systems – the numbers may look good, but institutions should use caution under certain circumstances. In instances where there is substantial risk in the industry, inconsistency of income and/or non-diversified cash flow, additional scrutiny may be necessary.

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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