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
Link To Document