A Novel Credit Evaluation Model Based on the Maximum Discrimination of Evaluation Results

Abstract

ABSTRACT This paper proposes a novel model for establishing a credit evaluation system, including a system of indicators, indicator weights, and credit scores. A credit evaluation system whose evaluation results have significant discrimination is good. Based on this standard, we construct an objective programming model with the maximum discrimination of credit scores as the objective function. The main constraint condition is that the indicator weights sum to 1, and weight is a decision variable. After we delete indicators whose weight is 0, we design a system of indicators, and then obtain credit scores with the maximum discriminatory power. Our empirical study of China’s 3,045 small businesses confirms that this model is both easy to use and reasonable. The empirical results show that, compared to logistic regression and CHAID decision trees, our model has greater accuracy based on F, AUC, and KS tests.