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Article Details

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.

Detection of Credit Card Fraud using a Hybrid Ensemble Model

Author 1: Sayali Saraf
Author 2: Anupama Phakatkar

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130953

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: The rising number of credit card frauds presents a significant challenge for the banking industry. Many businesses and financial institutions suffer huge losses because card users are reluctant to use their cards. A primary goal of fraud detection is to identify prior transaction patterns to detect future fraud. In this paper, a hybrid ensemble model is proposed to combine bagging and boosting techniques to distinguish between fraudulent and legitimate transactions. During the experimentation two datasets are used; the European credit card dataset and the credit card stimulation dataset which are highly imbalanced. The oversampling method is used to balance both datasets. To overcome the problem of unbalanced data oversampling method is used. The model is trained to predict output results by combining random forest with Adaboost. The proposed model provides 98.27 % area under curve score on the European credit cards dataset and the stimulation credit card dataset gives 99.3 % area under curve score.

Keywords: Credit card; hybrid ensemble model; bagging; boosting; data imbalance

Sayali Saraf and Anupama Phakatkar, “Detection of Credit Card Fraud using a Hybrid Ensemble Model” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130953

@article{Saraf2022,
title = {Detection of Credit Card Fraud using a Hybrid Ensemble Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130953},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130953},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Sayali Saraf and Anupama Phakatkar}
}


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