Credit Risk Assessment in Commercial Banks Based on Support Vector Machines

Author

Sun, Wei ; Yang, Chen-guang ; Qi, Jian-Xun

Author_Institution

Sch. of Bus. Adm., North China Electr. Power Univ., Baoding

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2430

Lastpage

2433

Abstract

According to the practical situation of credit risk assessment in commercial banks, a set of index system is established. The index system includes financial indexes and non-financial indexes. Then support vector machines (SVM) algorithm is used for assessment in this research. In the method, training sets are selected by the increasing proportions. Proportions are determined by the number of samples. In order to verify the effectiveness of the method, a real case is given and the experimental results show that the model has high correct classification accuracy

Keywords

bank data processing; credit transactions; pattern classification; risk management; support vector machines; SVM algorithm; commercial banks; credit risk assessment; financial index system; nonfinancial indexes; support vector machines; Artificial intelligence; Business; Cybernetics; Finance; Lagrangian functions; Machine learning; Risk management; Static VAr compensators; Statistics; Sun; Support vector machine classification; Support vector machines; Training data; Classification; Commercial banks; Credit risk assessment; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258774

Filename

4028472