• Oct 26, 2018 News!Vol.10, No.5 has been published with online version.   [Click]
  • Aug 06, 2018 News!Vol.9, No.1-Vol.9, No.4 have been indexed by EI (Inspec).   [Click]
  • Sep 03, 2018 News!Vol.10, No.4 has been published with online version.   [Click]
General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2018 Vol.10(4): 105-110 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2018.V10.1208

Predicting Credit Card Defaults with Deep Learning and Other Machine Learning Models

Tsungnan Chou and Mingmin Lo
Abstract—Since the overdue amount of credit cards has been increasing year by year, the rising credit card delinquencies might prevent the commercial banks to allocate more funds in profitable investments. At the same time, the high processing costs of credit card delinquencies through manual verification also affect the competitiveness of credit card issuers. Because the market competition becomes strict in the era of financial technology, to predict correctly whether cardholders will be unable to pay off credit card debt and establish an effective risk prediction model is the major purpose of this study. We first implemented four machine-learning approaches to predict the default cases, however, most models encountered challenges to resolve imbalance problem of delinquency cases in data sets and reported lower predictive accuracy. Two inference strategies including grey incidence analysis and fuzzy decision tree were proposed to improve the predictive performance. The average accuracy for both strategies were increased from 0.82 to 0.86 and 0.89 respectively. In addition, the deep learning approach integrated with various network structures was also incorporated to evaluate model performance. The experiment results indicated the deep neural network performed better in most evaluation metrics and achieved an impressively high accuracy of 0.93 if compare to the machine learning models. Finally, three feature selection methods were employed with the deep learning model, and the results showed similar predictive accuracy as the original deep learning models with slightly better performance being reported by filtering variables with the grey incidence analysis. This research work could be extended to apply more complicated deep learning algorithms to learn and trace the behaviors of the credit card holders and reduce the default risks for banking industries.

Index Terms—Default prediction, machine learning, deep neural network, deep learning.

Tsungnan Chou and Mingmin Lo are with the Dept. of Finance, Chaoyang University of Technology, Taichung County, 41349 Taiwan, R.O.C. (e-mail: tnchou@cyut.edu.tw, mingminlo@cyut.edu.tw).

[PDF]

Cite:Tsungnan Chou and Mingmin Lo, "Predicting Credit Card Defaults with Deep Learning and Other Machine Learning Models," International Journal of Computer Theory and Engineering vol. 10, no. 4, pp. 105-110, 2018.

Copyright © 2008-2018. International Journal of Computer Theory and Engineering. All rights reserved.
E-mail: ijcte@iacsitp.com