• DocumentCode
    1898257
  • Title

    Research of SVM Applying in the Risk of Bank´s Loan to Enterprises

  • Author

    Ma, Yinxiao ; Liu, Hong

  • Author_Institution
    Zhejiang Gongshang Univ., Hangzhou, China
  • fYear
    2010
  • fDate
    25-26 Dec. 2010
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    this paper presents a classification based on support vector machine (SVM) to carry out comprehensive analysis of the ability of enterprises paying debt,reduce the risk of bank to provide a loan. First this paper introduces the main principle of support vector machines to establish data classification model, using historical data for classification. Then collect the financial indices of 80 enterprises in China in 2001 to create support vector machine model for the credit risk classification for banks loan to enterprise; Finally, compared with the result of classification in the traditional Logistic regression model or the BP neural network evaluation model, this paper points out that the classification based on SVM is better than the traditional model and it could improve the classification accuracy.
  • Keywords
    banking; credit transactions; pattern classification; risk management; support vector machines; BP neural network evaluation model; SVM; bank risk; credit risk classification; data classification model; enterprise loan; logistic regression model; support vector machine; Accuracy; Artificial neural networks; Business; Classification algorithms; Kernel; Support vector machines; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Engineering and Computer Science (ICIECS), 2010 2nd International Conference on
  • Conference_Location
    Wuhan
  • ISSN
    2156-7379
  • Print_ISBN
    978-1-4244-7939-9
  • Electronic_ISBN
    2156-7379
  • Type

    conf

  • DOI
    10.1109/ICIECS.2010.5678225
  • Filename
    5678225