基于机器学习的供应链成员企业信用风险评估方法及应用研究

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中图分类号：F272 文献标志码：A

Research on Credit Risk Assessment Methods and Applications for Supply Chain Member Enterprises Based on Machine Learning

HUANG Peng （Antai Collge of Economics & Management， Shanghai Jiao Tong University，Shanghai 2Ooo3O，China

Abstract： Supply chain management is the core of moderm business operations，and supply chain finance is an effective way to alleviate financial pressure. Supply chain credit risk willseriously affect the safe and stable operation of the supply chain. Traditional credit risk assessment methods are unable to cope with the challnges brought by large amounts of data.Machine learning is used to improve supply chain credit risk assessmentcapabilities in the face of large amounts ofdata.First，the financial dataof （2 3，704 supply chain companies were obtained from the database，and a data enhancement method was used to balance the distribution of positive and negative samples. Then，machine learning clasification algorithms such as the decision tree，random forest and extreme gradient boosting tree are used to evaluate the credit risk of supply chain enterprises.Finally，the credit risk assessment performance of diferent machine learning classification algorithms is analyzed to find the optimal assessment method. The research provides a valuable reference for improving the level of supply chain credit risk assessment and helps enhance the risk management capabilities of the supply chain.

Key words：machine learning;credit risk; risk evaluation; supply chain

1相关研究

信用风险评估的研究比较丰富，但针对供应链成员企业信用风险评估研究则相对匮乏。（剩余6806字）