Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach

Yizhe Dong, Nana Cai, Baofeng Shi, Yue sun

Research output: Contribution to journalArticlepeer-review

Abstract

Corporate credit-rating assessment plays a crucial role in helping financial institutions make their lending decisions and in reducing the financial constraints of small enterprises. This paper presents a new approach for small industrial enterprises’ credit-rating assessment using fuzzy decision-making methods, and tests it using real bank loan data from 1,820 small industrial enterprises in China. The procedure of the proposed rating approach includes (1) employing triangular fuzzy numbers to quantify the qualitative evaluation indicators; (2) conducting a correlation analysis, univariate analysis and stepping backwards feature selection method to select the input features; (3) combining the entropy weight method, the fuzzy c-means algorithm and the technique for order of preference by similarity to ideal solution (TOPSIS) technique to classify small enterprises into rating classes; and (4) using the lattice degree of nearness to predict a new loan applicant’s rating. We also conduct a 10-fold cross-validation to evaluate the predictive performance of our proposed approach. The results demonstrate that our proposed data processing and feature selection approaches have better accuracy than the alternative approaches in predicting default, offering a valuable new system to bank experts, to assist their decision making.
Original languageEnglish
JournalInternational Journal of Forecasting
Publication statusSubmitted - 2021

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