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

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  • Guotai Chi
  • Shanli Yu
  • Ying Zhou

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.

Suggested Citation

  • Guotai Chi & Shanli Yu & Ying Zhou, 2020. "A Novel Credit Evaluation Model Based on the Maximum Discrimination of Evaluation Results," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(11), pages 2543-2562, September.
    • Handle: RePEc:mes:emfitr:v:56:y:2020:i:11:p:2543-2562
    DOI: 10.1080/1540496X.2019.1643717
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    Cited by:

    1. Yu Li & Fenghao Zhang & Ruifan Li & Hongbing Yu & Yao Chen & Han Yu, 2024. "Comprehensive Ecological Functional Zoning: A Data-Driven Approach for Sustainable Land Use and Environmental Management—A Case Study in Shenzhen, China," Land, MDPI, vol. 13(9), pages 1-19, September.
    2. Yingli Wu & Guangji Tong, 2022. "The evaluation of agricultural enterprise's innovative borrowing capacity based on deep learning and BP neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1111-1123, December.
    3. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.

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