Improving clustering analysis for credit card accounts classification

  • Authors:

  • Yi Peng;Gang Kou;Yong Shi;Zhengxin Chen

  • Affiliations:

  • College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE;College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE;College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE;College of Information Science & Technology, University of Nebraska at Omaha, Omaha, NE

  • Venue:
  • ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part III
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

In credit card portfolio management, predicting the cardholders’ behavior is a key to reduce the charge off risk of credit card issuers. The most commonly used methods in predicting credit card defaulters are credit scoring models. Most of these credit scoring models use supervised classification methods. Although these methods have made considerable progress in bankruptcy prediction, they are unsuitable for data records without predefined class labels. Therefore, it is worthwhile to investigate the applicability of unsupervised learning methods in credit card accounts classification. The objectives of this paper are: (1) to explore an unsupervised learning method: cluster analysis, for credit card accounts classification, (2) to improve clustering classification results using ensemble and supervised learning methods. In particular, a general purpose clustering toolkit, CLUTO, from university of Minnesota, was used to classify a real-life credit card dataset and two supervised classification methods, decision tree and multiple-criteria linear programming (MCLP), were used to improve the clustering results. The classification results indicate that clustering can be used to either as a stand-alone classification method or as a preprocess step for supervised classification methods.