[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.13106/jidb.2022.vol13.no10.1

Study of Personal Credit Risk Assessment Based on SVM

LI, Xin (College of Business Administration, Henan University of Science and Technology)
XIA, Han (College of Business Administration, Henan Finance University)

Publication Information

The Journal of Industrial Distribution & Business / v.13, no.10, 2022 , pp. 1-8 More about this Journal

Abstract

Purpose: Support vector machines (SVMs) ensemble has been proposed to improve classification performance of Credit risk recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. To deal with this problem, this paper designs a support vector machines (SVMs) ensemble method based on fuzzy integral, which aggregates the outputs of separate component SVMs with importance of each component SVM. Research design, data, and methodology: This paper designs a personal credit risk evaluation index system including 16 indicators and discusses a support vector machines (SVMs) ensemble method based on fuzzy integral for designing a credit risk assessment system to discriminate good creditors from bad ones. This paper randomly selects 1500 sample data of personal loan customers of a commercial bank in China 2015-2020 for simulation experiments. Results: By comparing the experimental result SVMs ensemble with the single SVM, the neural network ensemble, the proposed method outperforms the single SVM, and neural network ensemble in terms of classification accuracy. Conclusions: The results show that the method proposed in this paper has higher classification accuracy than other classification methods, which confirms the feasibility and effectiveness of this method.

Keywords

Credit index; Credit evaluation; Support vector machines ensemble; customer to customer; bagging; Fuzzy integral;

Citations & Related Records

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