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4 Tips for Creating an Effective Credit Policy

May 27, 2015
Read Time: 0 min

Some view banks’ credit policies as somewhat of a necessary evil; many comprise hundreds of difficult-to-decipher pages intended to establish numerous policies for evaluating and undertaking risk. These credit policies outline banks’ appetite for risk, establish banks’ credit culture, determine the extent of decision-making delegation and describe how loan portfolio risk is measured, monitored and reported.

Christopher Webbe, Senior Consultant at CEIS Review, provided a detailed overview of how banks can create an effective credit policy in a recent issue of The CEIS Quarterly. Here are four of his tips:

1. Focus on creating policies, not procedures

Webbe notes that many policies contain detailed “how-to” instructions on the performance of routine details, resulting in unwieldy documents. In addition, these instructions may be more likely to be changed, but a bank’s credit policy should be changed as infrequently as possible. “The best solution is to put the ‘how-to’ instructions in a separate manual and cross-reference where appropriate.” Webbe also states, “Try to ensure that the policy is aimed at “what” rather than ‘how.’”

2. Avoid vague language

Effective credit policies should avoid words like “generally” or “usually.” Not using exact, concrete terms will only lead to confusion among staff and make policy enforcement more difficult and less likely to be adopted quickly. It could also lead to inefficiency when there are differing opinions about the application of the policy. Webbe states, “No policy can cater to every conceivable set of circumstances, and it is certainly not part of the purpose of credit policy to prevent the bank from booking acceptable risk. Instead of allowing flexibility, utilize the exception process.” The exception process should be managed, tracked and reviewed periodically to determine if changes to the overall policy are needed.

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3. Remove outdated material.

Banks are often focused on ensuring their credit policies include new regulatory guidance and cover new lines of business. However, Webbe points out that many fail to remove outdated material. “Guidance on products the bank no longer offers, regulatory or legal requirements that have been withdrawn or superseded and references to functions and departments in the bank that have been changed or no longer exist frequently linger on past their due date.” Outdated content adds length to the document and could confuse those using the policy – especially newer employees who are not familiar with the institution’s policies and may not realize that certain elements no longer apply.

4. Keep it organized.

Webbe makes two points about organization. The first suggests staff should only have access to the latest version of the policy. He recommends keeping the latest version on the bank’s network and discouraging staff from printing or downloading copies to their local machines. Webbe’s second point involves organizing the document itself. He suggests making the policy searchable and including a comprehensive index to allow staff to more easily locate information. He adds that it is also good practice to “have every page of the policy carry the version and date under which it was issued so that no argument can’t arise as to which part of which policy came into force on what date.” Plus, management can easily account for pages that have been in use the longest and update accordingly.

For additional tips on how to create an effective credit policy, including how to manage access, amendments and exceptions, access the full article in The CEIS Quarterly.

To learn more about credit culture, access the Abrigo webinar, Credit Risk Readiness: One Decade After the Recession.

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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