A New Approach for Jointly Evaluating the "Six Cs" of Loan Analysis

By Zinkhan, F. Christian | Akron Business and Economic Review, Spring 1990 | Go to article overview

A New Approach for Jointly Evaluating the "Six Cs" of Loan Analysis


Zinkhan, F. Christian, Akron Business and Economic Review


A New Approach For Jointly Evaluating The "Six Cs" of Loan Analysis(*)

Most financial management textbooks discuss the five Cs of credit analysis--capacity, capital, character, collateral, and conditions--in relationship to the evaluation of a given firm's credit risk. A number of efforts have been undertaken to quantify and summarize two of these five Cs for the purpose of estimating the bankruptcy risk of firms: capacity and capital. Notable among these efforts are the Z-score model of Altman[2], the Zeta analysis model of Altman, Haldeman, and Narayanan[3], and the application of a discriminant analysis model to small businesses by Edmister[5]. In addition, a credit-scoring model was developed by Chesser[4] to determine the creditworthiness of a potential business loan customer. These above-mentioned quantitative models not only ignore the character, collateral, and conditions dimensions of credit risk analysis but also do not incorporate a measurement of expected return.

In this paper, a flexible technique for quantifying and combining the five dimensions of credit analysis with a sixth C of loan evaluation, customer profitability analysis, is proposed. The purpose of this technique is to jointly evaluate the objective and subjective estimations of the six Cs in order to generate an overall indicator of the relative attractiveness of a given potential business loan. Loan attractiveness in this model is evaluated relative to the utility function of the financial institution's director loan committee. The indicator of loan attractiveness is intended to serve as supplemental information for the loan approval-disapproval decision-making process and can also be used by a loan or credit officer to help screen, evaluate, and possibly restructure potential deals prior to consuming loan committee time with the application.

After proposing a hierarchical model of the business loan evaluation process in the next section, conjoint analysis--the quantitative technique that can be used to jointly analyze the six Cs--will be discussed. Then, a step-by-step approach for applying this technique at a given financial institution will be suggested. Concluding remarks are provided in the final section.

A HIERARCHICAL MODEL OF THE BUSINESS LOAN EVALUATION PROCESS

Table 1 contains a hierarchical model of the business loan evaluation process. Appendix A includes a description of the various credit risk- and expected customer profitability-related variables. The descriptions in Appendix A are purposely somewhat vague as far as the definitions of the various variables are concerned. Each financial institution that utilizes this technique has the opportunity to tailor the definitions to its unique situation. In reality, each financial institution might choose to adjust the definitions so that they mesh with the categories used for management or reporting purposes.

It should be noticed in Table 1 that credit risk is assumed to be a function of four dimensions, while expected customer profitability is assumed to be a function of two dimensions. Conjoint analysis, the mathematical technique used to jointly analyze the individual credit risk and expected customer profitability dimensions, is reviewed in the next section.

CONJOINT ANALYSIS

The joint analysis of the six Cs is made possible through the use of conjoint analysis. Conjoint analysis is a measurement technique developed by researchers in the fields of mathematical psychology and psychometrics and commonly applied in marketing research[6]. It is concerned with the measurement of the joint effect of two or more independent variables on the ranking of a dependent variable. One of the most common applications of conjoint analysis is the measurement of the relative importance of consumer product attributes. Such information is especially useful in the design of new products or the redesign of existing products. As far as financial applications are concerned, the technique was used by Teas and Dellva[7] in order to measure consumer preferences of alternative financial services, by Zinkhan[9] to design security issues, and by Zinkhan and Zinkhan[10] to design financial services. …

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