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DOI: 10.14569/IJACSA.2018.091155
PDF

Handling Class Imbalance in Credit Card Fraud using Resampling Methods

Author 1: Nur Farhana Hordri
Author 2: Siti Sophiayati Yuhaniz
Author 3: Nurulhuda Firdaus Mohd Azmi
Author 4: Siti Mariyam Shamsuddin

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 11, 2018.

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Abstract: Credit card based online payments has grown intensely, compelling the financial organisations to implement and continuously improve their fraud detection system. However, credit card fraud dataset is heavily imbalanced and different types of misclassification errors may have different costs and it is essential to control them, to a certain degree, to compromise those errors. Classification techniques are the promising solutions to detect the fraud and non-fraud transactions. Unfortunately, in a certain condition, classification techniques do not perform well when it comes to huge numbers of differences in minority and majority cases. Hence in this study, resampling methods, Random Under Sampling, Random Over Sampling and Synthetic Minority Oversampling Technique, were applied in the credit card dataset to overcome the rare events in the dataset. Then, the three resampled datasets were classified using classification techniques. The performances were measured by their sensitivity, specificity, accuracy, precision, area under curve (AUC) and error rate. The findings disclosed that by resampling the dataset, the models were more practicable, gave better performance and were statistically better.

Keywords: Credit card; imbalanced dataset; misclassification error; resampling methods; random undersampling; random oversampling; synthetic minority oversampling technique

Nur Farhana Hordri, Siti Sophiayati Yuhaniz, Nurulhuda Firdaus Mohd Azmi and Siti Mariyam Shamsuddin, “Handling Class Imbalance in Credit Card Fraud using Resampling Methods” International Journal of Advanced Computer Science and Applications(IJACSA), 9(11), 2018. http://dx.doi.org/10.14569/IJACSA.2018.091155

@article{Hordri2018,
title = {Handling Class Imbalance in Credit Card Fraud using Resampling Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.091155},
url = {http://dx.doi.org/10.14569/IJACSA.2018.091155},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {11},
author = {Nur Farhana Hordri and Siti Sophiayati Yuhaniz and Nurulhuda Firdaus Mohd Azmi and Siti Mariyam Shamsuddin}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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