Decision support system on credit operation using linear and logistic regression

• Published in: Expert systems 2021-09, Vol.38 (6), p.n/a
• Main Authors: Teles, Germanno, Rodrigues, Joel J. P. C., Kozlov, Sergei A., Rabêlo, Ricardo A. L., Albuquerque, Victor Hugo C.
• Format: Article
• Language: English
• Subjects: credit scoring; Decision support systems; finance; Independent variables; linear regression; logistic regression; machine learning; Prediction models; Regression analysis; Regression models
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.12578

The act of lending is based on trust in the borrower to honour the obligation of paying back the lender. Greater spreads on credit operations may help predict the expected recovery of the credit, based on the sufficiency and liquidity of the guarantee. This study aims to understand how predictive models can provide different estimations of expected recovery based on the same data sets. It classifies credit by the formulation of a rule that describes the values of a categorical variable according to some specified definition. It finds that a simple logistic regression model can easily be extended to a multiple logistic regression model by integrating more than one prediction variable, which indicates increasing difficulty in obtaining multiple observations with an increasing number of independent variables. It compares the efficiency of the logistic regression with that of a linear regression in predicting whether recovery is due in a credit operation, and, thus, identifies the best model for this purpose.