• DocumentCode
    2653903
  • Title

    A Risk Early-warning System for Individual Housing Loan Credit Based on Artificial Immune Net

  • Author

    Ze-jun, WU ; He, JI ; Yi-wen, LIANG

  • Author_Institution
    Zhongnan Univ. of Econ. & Law, Wuhan
  • fYear
    2007
  • fDate
    20-22 Aug. 2007
  • Firstpage
    1764
  • Lastpage
    1768
  • Abstract
    Presently, due to joining it by all the commercial banks, individual housing loan is becoming a competitive market. With its rapid developing, it also appears defaulting on a loan, and some deceptive mortgage in the course of loaning. The bank has to incur the enormous loss. However, those traditional methods are not against the complicate environment. So it is crucial to set up a system to cope with these problems. This paper, combining the Artificial Immune System with the Artificial Neural Networks, proposes a risk early-warning system for the individual housing loan credit. Many features of them, such as self-learning, self-adaptability, multilayered distributability, could solve those covert and dynamic uncertain problems, thus it will offer a novel approach for the risk alarm.
  • Keywords
    artificial immune systems; bank data processing; credit transactions; mortgage processing; neural nets; real estate data processing; risk analysis; artificial immune system; artificial neural network; commercial bank; competitive market; individual housing loan credit; mortgage; multilayered distributability; risk alarm; risk early-warning system; self-adaptability; self-learning; Artificial immune systems; Artificial neural networks; Conference management; Diversity reception; Educational institutions; Engineering management; Environmental economics; Helium; Loans and mortgages; Risk management; antibody network; artificial immunology; neural network; risk early-warning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2007. ICMSE 2007. International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-7-88358-080-5
  • Electronic_ISBN
    978-7-88358-080-5
  • Type

    conf

  • DOI
    10.1109/ICMSE.2007.4422096
  • Filename
    4422096