Logistic regression and its application in credit scoring

Abstract

Credit scoring is a mechanism used to quantify the risk factors relevant for an obligor’s ability and willingness to pay. Credit scoring has become the norm in modern banking, due to the large number of applications received on a daily basis and the increased regulatory requirements for banks. In this study, the concept and application of credit scoring in a South African banking environment is explained, with reference to the International Bank of Settlement’s regulations and requirements. The steps necessary to develop a credit scoring model is looked at with focus on the credit risk context, but not restricted to it. Applications of the concept for the whole life cycle of a product are mentioned. The statistics behind credit scoring is also explained, with particular emphasis on logistic regression. Linear regression and its assumptions are first shown, to demonstrate why it cannot be used for a credit scoring model. Simple logistic regression is first shown before it is expanded to a multivariate view. Due to the large number of variables available for credit scoring models provided by credit bureaus, techniques for reducing the number of variables included for modeling purposes is shown, with reference to specific credit scoring notions. Stepwise and best subset logistic regression methodologies are also discussed with mention to a study on determining the best significance level for forward stepwise logistic regression. Multinomial and ordinal logistic regression is briefly looked at to illustrate how binary logistic regression can be expanded to model scenarios with more than two possible outcomes, whether on a nominal or ordinal scale. As logistic regression is not the only method used in credit scoring, other methods will also be noted, but not in extensive detail. The study ends with a practical application of logistic regression for a credit scoring model on data from a South African bank. Copyright