Evaluating Loan Decisions

Outlining key commercial credit risk metrics and thresholds

By Tim McPeak

As most community bankers will tell you, their personal knowledge of their borrowers helps them to better understand the story behind the numbers they see on tax returns and other financial statements. Customers will also tell you how much they value the personal service and local decision making they receive from their local institution.

In many ways, this relationship-based lending approach highlights the value and appeal of community banks.

However, to foster and maintain a solid credit culture within commercial lending operations, community banks must balance this relationship-based approach with more objective measures of credit worthiness to understand the whole picture of a commercial borrower’s credit standing. Below is a closer look at several commonly used key commercial credit risk metrics:

– Business credit scores—Business credit scores can provide an objective measure of a business entity’s historical credit performance. They are typically a product of proprietary statistical models developed by credit bureaus, and factor in trade information provided by suppliers and lenders, legal filings from local and state levels, as well as other publically available information.

– Personal credit scores—Personal credit scores, also known as FICO scores, provide a standardized measure of consumer credit risk. Much like business credit scores, they are also based on proprietary models incorporating actual payment performance as well as on factors such as the mix of types of credit, length of history and total debt. Both business and personal credit scores are valuable data points for credit analysis due to their objective and standardized nature, but are best viewed as only a piece of the overall credit puzzle.

– Probability of default metrics—A probability of default metric is a numerical estimate of the likelihood of a borrower default for a given time frame (typically 12 months or less). Probability of default metrics are less commonly used than credit scores but are similar in their objectivity. As these financial models are dependent on a number of underlying variables, lenders should understand the assumptions and limitations involved as they incorporate these metrics into their analysis.

– Debt service coverage ratios (DSCR)—One of the most commonly used metrics in credit analysis, the DSCR is the ratio of cash flow available for a consumer or business to service debt payments (typically including both principal and interest). Higher ratios (greater than 1.0x cash flow divided by debt service) indicate more comfort for the borrower to meet debt service obligations. Often, lenders will establish a minimum DSCR threshold as part of their overall credit policy. Though widely used and easy to understand, care should be taken to confirm that an accurate debt service figure is being used and that the available cash flow is dependable and regularly occurring.  

– Loan-to-value ratios—Another widely used metric, loan to value provides the ratio of the loan amount to the total available collateral, typically as a percentage. The loan-to-value ratio is a critical data point in credit analysis and is also a vital measure of credit quality on an ongoing basis. As such, accurate collateral values, both before origination and throughout the life of the loan, are key. Much like the DSCR, lenders will often set loan-to-value thresholds as part of loan policy with the thresholds varying by collateral type.

– Overall ratio analysis—Ratio analysis is a critical component of effective credit analysis. Ratios provide a standardized way of looking at the financial health of a borrower and are especially useful in comparing borrowers against one another. However, ratio analysis in a vacuum can be misleading as variables such as industry type, geography and others can widely impact what constitutes a “good” ratio. Peer data and industry benchmarks should be incorporated into the analysis for comparison and to give context to the numbers.

– Global cash flow analysis—At a high level, global cash flow analysis is simply a process to incorporate all related entities in a borrowing relationship into the credit analysis. Related entities commonly included in a global cash flow analysis are other business entities, pieces of related real estate and any personal guarantors.

As always, the devil is in the details as this process can be quite complex. The end goal should be to identify how related entities provide, as well as use, cash flow to determine a global cash flow available to service debts. Well-documented policies and models that are consistently applied across the institution are critical to ensure good loan decisions.

– Uniform Credit Analysis (UCA) cash flow analysis—UCA cash flow analysis is a more detailed statement of cash flows than the more traditional direct and indirect methods as prescribed by GAAP. The UCA format cash flow statement is often preferred by bankers as it provides a more accurate estimate of the cash flow available for debt service.

As each community bank’s portfolio and risk appetite varies, there is no right way to apply the credit risk metrics detailed above. Starting at the board of directors level and working down through the senior lending staff, well-defined policies and thresholds must be established across the institution.

With these policies in place, your community bank can balance its personal knowledge of its commercial customers along with more quantitative metrics to make better lending decisions.

Tim McPeak is director for the financial institutions division at Sageworks Inc., a commercial credit analysis and management software provider in Raleigh, N.C.