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IJMLC 2021 Vol.11(1): 34-39 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1011

Application of Credit Card Fraud Detection Based on CS-SVM

Chenglong Li, Ning Ding, Haoyun Dong, and Yiming Zhai

Abstract—With the development of e-commerce, credit card fraud is also increasing. At the same time, the way of credit card fraud is also constantly innovating. Support Vector Machine, Logical Regression, Random Forest, Naive Bayes and other algorithms are often used in credit card fraud identification. However, the current fraud detection technology is not accurate, and may cause significant economic losses to cardholders and banks. This paper will introduce an innovative method to optimize the support vector machine by cuckoo search algorithm to improve its ability of identifying credit card fraud. Cuckoo search algorithm improves classification performance by optimizing the parameters of support vector machine kernel function (C, g). The results demonstrate that CS-SVM is superior to SVM in Accuracy, Precision, Recall, F1-score, AUC, and superior to Logistic. Regression, Random Forest, Decision Tree, Naive Bayes, whose accuracy is 98%.

Index Terms—Credit card fraud, fraud detection technique, SVM, CS.

Chenglong Li and Yiming zhai were with the School of Criminal Investigation and Forensic Science, People's Public Security University of China, China (e-mail: chenglong_li666@163.com, yiming_zhai@126.com).
Ning Ding is with the School of Criminal Investigation and Forensic Science, People's Public Security University of China, China (Corresponding author; e-mail: dingning_thu@126.com).
Haoyun Dong was with the School of Law and Criminology, People's Public Security University of China, China (e-mail: 541054114@ qq.com).

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Cite: Chenglong Li, Ning Ding, Haoyun Dong, and Yiming Zhai, "Application of Credit Card Fraud Detection Based on CS-SVM," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 34-39, 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 Library.
  • E-mail: ijmlc@ejournal.net


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