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.
[1]
James A. Ohlson.
FINANCIAL RATIOS AND THE PROBABILISTIC PREDICTION OF BANKRUPTCY
,
1980
.
[2]
Nello Cristianini,et al.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
,
2000
.
[3]
Nello Cristianini,et al.
Kernel Methods for Pattern Analysis
,
2003,
ICTAI.
[4]
Christopher J. C. Burges,et al.
A Tutorial on Support Vector Machines for Pattern Recognition
,
1998,
Data Mining and Knowledge Discovery.
[5]
Vladimir Vapnik,et al.
Statistical learning theory
,
1998
.