Credit Risk Assessment for Small and Microsized Enterprises Using Kernel Feature Selection-Based Multiple Criteria Linear Optimization Classifier: Evidence from China

• Main Authors: Yimeng Wang, Yunqi Zhang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2020-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2020/2394948

Credit risk assessment has gained increasing marked attention in the recent years by researchers, financial institutions, and banks, especially for small and microsized enterprises. Evidence shows that the core of small and microsized enterprises’ credit risk assessment is to construct a scientific credit risk indicator system, and the key is to establish an effective credit risk prediction model. Therefore, we analyze the factors that influence the credit risk of Chinese small and microsized enterprises and then construct a comprehensive credit risk indicator system by adding behaviour information, supervision information, and policy information. Furthermore, we improve the multiple criteria linear optimization classifier (MCLOC) by introducing the one-norm kernel feature selection and thereby establish the kernel feature selection-based multiple criteria linear optimization classifier (KFS-MCLOC). As for experiments, we use real business data from a Chinese commercial bank to test the performance of these models. The results show that (1) the proposed KFS-MCLOC has greater advantages in predictive accuracy, interpretability, and stability than other models; (2) the KFS-MCLOC selects 10 features from 53 original features and gives selected features their weight automatically; (3) the features selected by the KFS-MCLOC are further verified and compared by the features selected by the logistic regression model with stepwise parameter, and the indicators of “quick ratio; net operating cash flow; enterprises’ abnormal times of water, electricity, and tax fee; overdue days of enterprises’ loans; and mortgage and pledge status” are proved to be the most influencing credit risk factors.