Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost

The spread of enterprise credit risk in the supply chain may lead to large-scale bankruptcy and credit crises, which are related to national economic and social stability and financial system security. Therefore, enterprise credit risk in the supply chain context is not only a concern for banking financial institutions, credit rating agencies and enterprise managers but also the focus of governments. This article develops a DTE-DSA (decision tree [DT] ensemble model using the differential sampling rate, Synthetic Minority Oversampling Technique [SMOTE] and AdaBoost) prediction framework integrating supply chain information to predict enterprise credit risk. The empirical test shows that using supply chain information can significantly improve the prediction score. The DTE-DSA model has the best prediction effect in dealing with class imbalance problems. Compared with single classifier models-such as logistic regression, k-nearest neighbours, support vector machine, DT and DT using the SMOTE-as well as ensemble models-such as extremely randomized trees, random forest, rotation forest, extreme gradient boosting, gradient boosting DT and DT ensemble model using AdaBoost-the DTE-DSA model not only has the best prediction score but also has a more stable performance. The comprehensive use of supply chain information and the DTE-DSA model can result in the highest prediction score, with an area under the curve of 0.9016 and a Kolmogorov-Smirnov statistic of 0.7369. Further analysis of the variables of importance enhances the interpretability of the model and obtains relevant management insights.