Credit Card Fraud Detection in Payment Using Machine Learning Classifiers

Authors

  • Maad M. Mijwil Computer Techniques Engineering Department, Baghdad College of Economic Sciences University Baghdad, Iraq http://orcid.org/0000-0002-2884-2504
  • Israa Ezzat Salem Computer Techniques Engineering Department, Baghdad College of Economic Sciences University Baghdad, Iraq

DOI:

https://doi.org/10.24203/ajcis.v8i4.6449

Keywords:

Fraud Detection, Machine Learning, Payment, Predict, Classifiers, Credit Card

Abstract

The fraud detection in payment is a classification problem that aims to identify fraudulent transactions based individually on the information it contains and on the basis that a fraudster's behaviour patterns differ significantly from that of the actual customer. In this context, the authors propose to implement machine learning classifiers (Naïve Bayes, C4.5 decision trees, and Bagging Ensemble Learner) to predict the outcome of regular transactions and fraudulent transactions. The performance of these classifiers is judged by the following ways: precision, recall rate, and precision-recall curve (PRC) area rate. The dataset includes more than 297K transactions via credit cards in September 2013 and November 2017 that have been collected from Kaggle platform, of which 3293 are frauds. The performance PRC ratio of machine learning classifiers is between 99.9% and 100%, which confirms that these classifiers are very good at identifying binary classes 0 in the dataset. The results of the tests have proved that the best classifier is C4.5 decision trees. This classifier has the best accuracy of 94.12% in prediction of fraudulent transactions.

Author Biography

Maad M. Mijwil, Computer Techniques Engineering Department, Baghdad College of Economic Sciences University Baghdad, Iraq

Maad M. Mijwil received B.Sc. degree in Software Engineering from Software Engineering Department at Baghdad College of Economics Sciences University, Iraq in 2008/2009 and M.Sc. degree in Wireless sensor network of computer science from University of Baghdad, Iraq in 2015. Currently he is working Assistant Lecturer at Baghdad College of Economics Sciences University.

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Published

2020-12-29

How to Cite

Mijwil, M. M., & Salem, I. E. (2020). Credit Card Fraud Detection in Payment Using Machine Learning Classifiers. Asian Journal of Computer and Information Systems, 8(4). https://doi.org/10.24203/ajcis.v8i4.6449