Credit evaluation model of small construction enterprises based on discriminating between two types of customers

This paper takes 185 loan customers of small and medium construction enterprises in a Chinese regional commercial bank as the sample. It makes a combination of entropy weight, CRITIC method weight and homogeneity of variance weight, by constructing nonlinear goal programming function, the combination coefficient of single empowerment method is deduced. It establishes a credit evaluation model of small construction enterprises that distinguish between default and non default customers. Through the principle of ROC curve, it tests the default judgment ability of different weighting model results. The contributions of this paper are in two aspects. First, it creates the non-linear goal programming function by minimizing the sum of squares between types, and maximizing the sum of squares within each type. The smaller, the smaller the difference between credit scores for customers of the same type. The larger, the larger the difference in average credit scores between types. The smaller, and the larger, the larger the value for the goal programming function. As such, we maximize the value for the goal programming function, to ensure we maximize the difference in credit scores between good and bad customers and are able to clearly differentiate between good and bad customers. Greater mean deviation of default sample from the whole sample lead to bigger deviation from non-default sample as well, and the indicator can easily distinguish default and non-default sample. According to this rule, we assign a larger weight to the index able to identify default state by F value, different form existing index system, which ignores that ability. The results show that the combination weighting model shows better effect than the single weighting model in the capacity of default judgment. Combination weighting model's sensitivity and specificity are 83.33% and 95.95%, and both better than the single weighting model. © 2019, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.