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Yu, L. et al. (2009). A Least Squares Fuzzy Support Vector Machine Model with Variable Penalty Factor based on Kernel Principal Component Analysis and Its Application in Credit Classification. Systems Science and Mathematics, 10, 1311-1326.

has been cited by the following article:

• TITLE: Research on Credit Risk Rating of Commercial Banks Based on Support Vector Machine—Data from China’s Listed Commercial Banks

  AUTHORS: Shunquan Zhu

  KEYWORDS: Chinese Listed Commercial Banks, Support Vector Machine, Credit Risk Rating, Data Supporting

  JOURNAL NAME: Journal of Financial Risk Management, Vol.14 No.1, February 28, 2025

  ABSTRACT: In the era of big data and artificial intelligence, machine learning is one of the hot issues in the field of credit rating. On the basis of combing the literature on credit rating methods at home and abroad, this paper uses the support vector machine (SVM) method of machine learning to set up a three-classification credit rating model for Chinese listed commercial banks, which is compared with the existing methods of credit rating, the data show that the model classification accuracy on the test set is 80%. The support vector machine (SVM) method can identify the credit rating of Chinese-listed commercial banks, and it has wide applicability and good promotion value. The paper enriches the traditional credit rating method and has important significance in standardizing the healthy development of the financial market.