研究成果：Forecasting SMEs’ credit risk in supply chain finance with a sampling strategy based on machine learning techniques supervisory leadership on employees’ digital performance

作者：汪刘凯,Jia Fu[T],Chen Lujie[T],Xu Qifa

发表期刊：Annals of Operations Research

发表时间：2023年12月

Exploring the value of multi-source information fusion to predict small and medium-sized
enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging
task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors. The empirical results show that the financial-based information, such as TOC, NIR, is most useful in predicting SMEs’ credit risk in SCF, and multi-source information fusion is meaningful in better predicting the credit risk. In addition, based on the preferred CSL-RF model, which extends cost-sensitive learning to a random forest, we also present the varying mechanisms of key predictors for SMEs’ credit risk by using partial dependency analysis. The strategic insights obtained may be helpful for market participants, such as SMEs’ managers, investors, and market regulators.