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How to Design and Use Credit Decision Scorecards

by | Jun 9, 2022

Using Credit Decision Scorecards in your credit decisioning represents a convenient way to ‘filter’ loan applications. By applying some basic criteria, you can quickly decide if a credit request is straightforward and can be approved or denied as is, or if it needs further analysis, moving it on to the next stage of evaluation.

Why Use Credit Decision Scorecards?

Scorecard lending is not a quick and easy solution to every credit decision. For larger or more complex loan requests, in-depth analysis is, of course, vital. But the use of scorecards as a tool to evaluate loan requests is increasing as lenders realize it offers two main benefits. First, for relatively straightforward loan requests — a smaller loan request from a reliable customer with a solid credit history, for example — applying some basic criteria can be sufficient for you to make a decision quite quickly, allowing your customer to make the business decisions they need to make and also improving your capacity. Second, even more complex credits can be filtered through a scorecard system. By applying the metrics you’ve determined, you may pre-qualify a loan prior to the main credit analysis, or you may realize the extent and depth of the analysis needed. Scorecards, then, can speed up the processing of straightforward credit decisions, and inform the analysis of more complex loan requests.

Your Scorecard

All Credit Decision Scorecards are built on a model, and your institution determines how that looks. Depending on your preference, you may want to build a model from scratch, determining your criteria, then crafting the scorecard from those. Credit analysis solutions like FINPACK also offer models you can adopt or adapt to better meet the needs of your credit policy.  Additionally, you can (and probably should) have multiple models that can be applied to all the scenarios you’d like to score. 

In short, depending on the model you use, the principal(s) concerned, and the nature of the loan request, a well-designed Credit Decision Scorecard allows you to:

  • Determine if an application meets minimum requirements so as to proceed to the next step of the process
  • Determine if the applicant is so strong and the request carries so little risk, that the Scorecard will suffice and no greater credit scrutiny is required

Creating the Model

To design your scorecard, start with the “yes or no” questions you want to use to qualify the credit — “yes” answers representing positive markers. When building the model, you’ll determine how many “no” answers (if any) are acceptable to still qualify the credit.

The second part of the model is to define custom criteria such as credit scores and the limits of each for qualification. Keep in mind, you might decide to use a scorecard as the first step or prequalification when initially evaluating a credit, so this step can be completed without having a great deal of information already entered in the customer’s credit analysis file.

Lastly, the financial criteria and their qualifying limits are established in the model. Again, the qualification criteria and thresholds are determined by credit administration when the model is built.  Additionally, exceptions are allowable in the model, if agreeable per your credit policy.

Summary

Once your Credit Decision scorecard model is built, it can be made available to all users at your institution to streamline the process for approving small, straightforward borrower credit requests, for consumer loan requests, or to pre-qualify a loan request, helping to get the credit analysis off to a good start. The FINPACK Tech Support Team is happy to assist you in the development of the Credit Decision Scorecard models for your institution.

Relationship Manager at FINPACK | 6123011413 | dmaguire@umn.edu | Website | + posts

David Maguire became the FINPACK Relationship Manager in 2021. He helps Ag and Commercial lenders across the country understand the value of using FINPACK for credit analysis and loan management. David has worked in the marketing, training and education fields in Minnesota, Ireland and Germany.

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