An Empirical Study on Credit Scoring Model for Credit Card by Using Data Mining Technology

Author

Li, Wei ; Liao, Jibiao

Author_Institution

Manage. Dept., Dongguan Univ. of Technol., Dongguan, China

fYear

2011

fDate

3-4 Dec. 2011

Firstpage

1279

Lastpage

1282

Abstract

This paper investigates the credit scoring accuracy of five data mining technologies for bank credit cards: C5.0 decision tree, neural network, chi-squared automatic interaction detector, stepwise logistic model and classification and regression tree. Firstly, we extract a comprehensive variable from the raw data by using principle component analysis to indicate the customers´ default or not. Then we build the credit scoring models using data mining technologies and compare forecasting effects of the five models. Finally, we discuss how to classify non-defaulting applicants by using stepwise logistic model extensively.

Keywords

data mining; decision trees; finance; neural nets; pattern classification; principal component analysis; regression analysis; C5.0 decision tree; chi-squared automatic interaction detector; credit card; credit scoring model; data mining; neural network; principle component analysis; regression tree; stepwise logistic model; Credit cards; Data mining; Data models; Forecasting; Logistics; Predictive models; Training; Credit Card; Credit Scoring; Data mining; default; probability of non-default;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Security (CIS), 2011 Seventh International Conference on

Conference_Location

Hainan

Print_ISBN

978-1-4577-2008-6

Type

conf

DOI

10.1109/CIS.2011.283

Filename

6128238