• DocumentCode
    3597244
  • Title

    The research on the early-warning system model of Operational Risk for commercial banks based on BP Neural Network analysis

  • Author

    Cao, Li-jie ; Liang, Li-jun ; Li, Zhi-xiang

  • Author_Institution
    Sch. of Econ. & Manage., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
  • Volume
    5
  • fYear
    2009
  • Firstpage
    2739
  • Lastpage
    2744
  • Abstract
    Operational Risk management is an important part of risk management. Facing to today´s increasingly frequent operational risk of commercial banks, commercial banks need to develop practical operational risk control procedures and relevant measures, The establishment of Early-warning systems of operational risk would play a very important role in the future risk management. By means of the BP Neural Network analysis and nonlinear model methods, the paper would analyze and resolve the nonlinear relationship between several Key Risk Indicators (KRI) factors of commercial banks operational risk and risk results. According to establish the Early-warning system model of operational risk management for commercial banks, the paper expect that the research could provide some important references to prevent and control effectively operational risk for commercial banks.
  • Keywords
    alarm systems; backpropagation; banking; neural nets; risk management; BP neural network analysis; commercial banks; early-warning system model; key risk indicators factors; nonlinear model methods; nonlinear relationship; operational risk control procedures; operational risk management; Banking; Biological neural networks; Conference management; Control systems; Feedforward neural networks; Monitoring; Multi-layer neural network; Neural networks; Risk analysis; Risk management; BP Neural Network analysis; Early-warning system; Key Risk Indicator; Operational risk;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2009 International Conference on
  • Print_ISBN
    978-1-4244-3702-3
  • Electronic_ISBN
    978-1-4244-3703-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2009.5212096
  • Filename
    5212096