Explainability of Machine Learning in Credit Risk Assessment of SMEs

B Y Huang1,+, F Y Zhao1,+ , M H Tian1,2, D Q Zhang1,2, X T Zhang2 , Z Wang1,2,H L Li1,2,* and B L Chen3
1 Chongqing Institute of Engineering, Chongqing, China.
2 Shu Yi Xin Credit Management Co., LTD, Chongqing, China.
3 Department of Big Data Artificial Intelligence, Guangzhou Nanfang College, Gangzhou, China.

* Corresponding author: Hualin Li (e-mail: hualinli@hotmail.com).
Abstract. Small and medium-sized enterprises (SMEs) have developed rapidly in China, bringing enormous opportunities and challenges. In this study, we aim to investigate methods that can accurately assess credit risks of SMEs, using machine learning algorithms, focus on explainability, customer default forecasting, and delinquency. This study focused on the enterprises’ performance data and used the authorized invoice data of 425 SMEs in Chongqing. Machine learning algorithms, such as logistic regression, random forest, support vector machine, and soft voting ensemble learning methods, were used to establish a prediction classifier that was combined with the SHAP value to explain the feature contribution of a specific output. Therefore, Our study presented a strong correlation between the derived features and future delinquencies, which will enable in forecasting enterprises’ business performance.
Keywords. Credit risk model, SMEs lending, Explainability, Data mining, Machine learning.