Identifying supply chain credit risk in China’s textile industry using an ensemble explainable artificial intelligence model

Duqun Lu, Huosong Xia and Jiang Wu

Abstract

The development of artificial intelligence and financial technology has introduced new growth opportunities as well as creating complex challenges in credit risk identification, communication and control for supply chain enterprises. To address these, this study focuses on data mining of financial credit risk information and knowledge in the supply chain from the perspective of explainable artificial intelligence (XAI). By applying scientific and quantitative research methods we develop an XAIPSO- XGBoost-Logistic (XAI-PXL) model, which combines XAI using the particle swarm optimization, extreme gradient boosting and logistic regression algorithms. Our research shows that the combined XAI-PXL model has a good risk identification and classification performance and it outperforms the individual XGBoost and logistic regression algorithms in identifying credit risks in supply chain finance. The explanatory power of the model is analyzed using the Shapley additive explanations method and the order of explanatory power obtained for the characteristic variables input into the model. This study contributes to the literature on risk identification from the perspective of XAI, demonstrating strong theoretical and practical significance in identifying supply chain financial credit risks, acquiring supply chain risk knowledge, supporting decision-making for governments and enterprises.