DocumentCode :
2736395
Title :
An Empirical Study of Credit Scoring Model for Credit Card
Author :
Yeh, Hui-Chung ; Yang, Min-Li ; Lee, Li-Chuen
Author_Institution :
Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung
fYear :
2007
fDate :
5-7 Sept. 2007
Firstpage :
216
Lastpage :
216
Abstract :
The purpose of this paper is to propose an optimal credit scoring model to reassess the default risk of credit card holders for credit card issuing banks in Taiwan. This paper adopted four credit scoring models which are the linear discriminant analysis, decision tree, back-propagation neural network, and a hybrid method to evaluate the default risk. By comparing the evaluation results of these models, it shows that the decision tree method has the best classification performance in terms of accuracy and sensitivity. These results of this empirical study will be provided to credit card issuing banks for achieving efficient automatically credit reassessment of default risk.
Keywords :
backpropagation; decision trees; finance; neural nets; risk management; back-propagation neural network; credit card; credit reassessment; credit scoring model; decision tree; default risk; linear discriminant analysis; Business; Classification tree analysis; Credit cards; Decision trees; Government; Legislation; Linear discriminant analysis; Neural networks; Risk management; Statistical analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
Conference_Location :
Kumamoto
Print_ISBN :
0-7695-2882-1
Type :
conf
DOI :
10.1109/ICICIC.2007.138
Filename :
4427861
Link To Document :
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