• DocumentCode
    3390758
  • Title

    The BP neural networks applications in bank credit risk management system

  • Author

    Zhao, Shu-Fang ; Chen, Li-Chao

  • Author_Institution
    Inst. of Comput. Sci. & Technol., Taiyuan Univ. of Sci. & Technol., Taiyuan, China
  • fYear
    2009
  • fDate
    15-17 June 2009
  • Firstpage
    527
  • Lastpage
    532
  • Abstract
    Credit risk management, which is the basic of the credit application, is the most perfect embodiment in the bank credit application and asset supervision. The ultimate purpose of credit risk management is to ensure that credit fund is of safety, profitability and fluidity. At present, it is extremely important of commercial banks to set up an early bank risk warning system. The author who makes great effort on the credit risk and its reason, besides bringing in the western commercial banks experience of bank credit risk management, makes researches on credit risk in a microcosmic view. The author sets up early warning indicators for commercial bank credit risk, and carries out the warning for the credit risk in advance with the help of artificial neural networks. The experiment has proved that this method is objective and effective. So it can provide theoretical basis, which is more scientific and credible for detection and early warning about commercial bank credit risk.
  • Keywords
    bank data processing; neural nets; BP neural networks; artificial neural networks; asset supervision; bank credit risk management system; bank risk warning system; Alarm systems; Artificial neural networks; Asset management; Business; Face detection; Intelligent networks; Neural networks; Profitability; Risk management; Safety; BP neural networks; Commercial bank; Credit risk; Early warning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics, 2009. ICCI '09. 8th IEEE International Conference on
  • Conference_Location
    Kowloon, Hong Kong
  • Print_ISBN
    978-1-4244-4642-1
  • Type

    conf

  • DOI
    10.1109/COGINF.2009.5250682
  • Filename
    5250682