Financial risk prediction in supply chain finance based on buyer transaction behavior

Abstract

Financial risk in supply chain finance (SCF) is defined as the possibility that suppliers fall into liquidity crisis due to delayed payment. Predicting financial risk is important for supply chain stability. In this paper, a financial risk prediction model is developed using XGBoost and then evaluated by applying buyer transaction behavior data. We further construct single and hybrid models, respectively, and compare their performance using receiver operating characteristic curve (ROC), area under the ROC curve (AUC), and F1-Score. Last, feature importance and partial dependence plots (PDPs) are employed for model interpretation. The results show that XGBoost model can effectively predict potential financial risks, and shed lights on managers' payment practice. This paper is one of the few studies that develop new models to examine financial risks in SCF empirically.