DocumentCode :
2893531
Title :
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
Link To Document :
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