Industrial chain finance is a financial innovation that can effectively integrate the financial needs
of the upstream and downstream enterprises. The rapid development of industry chain finance
has put forward higher requirements for corresponding risk management. Among them, how to
evaluate the credit risk of industrial chain receivable accounts is a key link of industrial chain
financial risk management. For the small sample size in a single industry chain, a support vector
machine (SVM) suitable for small sample learning is introduced to assess the credit risk. The
credit risk evaluation index system for the industrial chain accounts receivable is constructed by
principal component analysis of the financial data. V-SVR (Support Vector Regression) model is
employed to evaluate principal component of the receivable accounts in telecom industry chain.
By comparing the evaluation results with historical data, it is found that the model can well
predict the credit risk of commercial banks, and has higher prediction accuracy than Logistic
model.