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IJMLC 2021 Vol.11(4): 286-290 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.4.1049

Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques

Md. Golam Kibria and Mehmet Sevkli

Abstract—The increased credit card defaulters have forced the companies to think carefully before the approval of credit applications. Credit card companies usually use their judgment to determine whether a credit card should be issued to the customer satisfying certain criteria. Some machine learning algorithms have also been used to support the decision. The main objective of this paper is to build a deep learning model based on the UCI (University of California, Irvine) data sets, which can support the credit card approval decision. Secondly, the performance of the built model is compared with the other two traditional machine learning algorithms: logistic regression (LR) and support vector machine (SVM). Our results show that the overall performance of our deep learning model is slightly better than that of the other two models.

Index Terms—Artificial intelligence, machine learning, deep learning, credit risk management.

Md. Golam Kibria is with the College of Business and Innovation, The University of Toledo, Toledo, OH 43606-3390 USA and on leave from Independent University, Bangladesh (IUB), Bangladesh (e-mail: mkibria@rockets.utoledo.edu).
Mehmet Sevkli is with the College of Business and Innovation, The University of Toledo, Toledo, OH 43606-3390 USA (e-mail: mehmet.sevkli@utoledo.edu).

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Cite: Md. Golam Kibria and Mehmet Sevkli, "Application of Deep Learning for Credit Card Approval: A Comparison with Two Machine Learning Techniques," International Journal of Machine Learning and Computing vol. 11, no. 4, pp. 286-290, 2021.

Copyright © 2021 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

 

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net


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