Study on Hybrid Augmented Machine Learning Algorithm for Evaluating Small and Medium-Sized Enterprises Credit Risk in Rare Earth Supply Chain Finance AITranslate

Basic Information:

DOI：

Chinese Library Classification Number:F224.9[经济管理—国民经济]

Citation Information:

Rare earths serve as key raw materials to support high-end technological innovation and the development of new energy industry.Thus,it is imperative to address the financing difficulties faced by small and medium-sized enterprises(SMEs)in the rare earth supply chain,strengthen China′s rare earth industry chain,and better safeguard the national strategic interests.As an innovative financing method,supply chain finance has become a major means to realize SME financing credit,but the credit risk issue remains one of the most critical issues that need to be solved in financing decision.Therefore,this paper proposed a hybrid augmented machine learning algorithm.First,the support vector machine(SVM)algorithm was optimized using the dynamic lens imaging inverse learning improved marine predator algorithm(IMPA),and then the optimized SVM was integrated using the AdaBoost algorithm to build an AdaBoost-IMPA-SVM model.The model was employed to evaluate the financial risk of the supply chain and re-establish the financial risk system indicators of the supply chain.Then,special effects were selected through correlation analysis,and 140 samples from 52 Chinese listed SMEs in the computer communication and other manufacturing industries during 2019—2021 were selected and input into the model as characteristic variables.The results of simulation experiments verify that the model has better classification and identification perfor-mance compared with other credit risk evaluation models.