Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model

Ge Gao, Hongxin Wang and Pengbin Gao
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Ge Gao: School of Business Administration, Liaoning Technical University, Huludao 125105, China
Hongxin Wang: School of Business Administration, Liaoning Technical University, Huludao 125105, China
Pengbin Gao: School of Economics and Management, Harbin Institute of Technology at Weihai, Weihai 264209, China

Risks, 2021, vol. 9, issue 11, 1-12

Abstract: In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions.

Keywords: small and medium-sized enterprises; credit risk evaluation; machine learning; soft voting (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2021
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