American Journal of Engineering and Applied Sciences

Machine Learning-Based Detection of Credit Card Fraud: A Comparative Study

Zainab Khamees Alkhateeb and Abeer Tariq Maolood

DOI : 10.3844/ajeassp.2019.535.542

American Journal of Engineering and Applied Sciences

Volume 12, Issue 4

Pages 535-542

Abstract

One of the fastest-growing problems with a high impact on the financial sector is financial fraud. Recently, data mining has been identified as one of the effective ways of detecting fraudulent credit card transactions. As a data mining problem, the detection of fraudulent credit card transaction is a challenging task due to the following reasons: (i) The frequent changes in the patterns of normal and fraudulent activities and (ii) the high level of skewness related with credit card fraud datasets. The aim of this article is to review the existing techniques for fraudulent transactions detection in credit cards, with more focus on the techniques that are Machine Learning (ML) based and nature inspired-based. The recent trend in the detection of credit card fraud was also presented in this article. Furthermore, the limitations and usefulness of the existing techniques for fraudulent transaction detection in credit cards were also outlined. The necessary fundamental information for further studies in this area was also provided. This review will also guide individuals and financial institutions seeking for effective techniques for credit card fraud detection, especially those that are based on ML and nature-inspired algorithms.

Copyright

© 2019 Zainab Khamees Alkhateeb and Abeer Tariq Maolood. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.