Due to the growing consumer credit market and, therefore, insolvency indices, companies are seeking to improve their credit analysis by incorporating objective judgments. Multivariate techniques have been used to construct credit models. These models, based on consumer registration information, allow the identification of behavior standards concerning insolvency. The objective of this work is to propose a methodology for the construction of credit risk models and to evaluate prediction performance using three specific models: discriminant analysis, logistic regression, and neural networks. The proposed method (entitled PRC Model) embraces six steps: (i) population definition, (ii) sampling, (iii) preliminary analysis, (iv) model development, (v) model selection, and (vi) implementation steps. The PRC Model was applied to a sample of 17,005 customers of an organization which manages its own credit system and controls a pool of drugstores. The results for this specific database show slight superiority of neural networks over the other two techniques, which can be attributed to its non-linear approach when dealing with the combined effect of explanatory variables
Credit analysis; Discriminat analysis; Logistic regression; Neural networks
