A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling
The existing fraud detection methods present limitations such as imbalanced data, incorrect identification of fraudulent cases, limited applicability to different scenarios, and difficulties processing data in real-time. This paper proposes an ensemble machine-learning model for detecting fraud in c...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666827025000581 |
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| _version_ | 1850103203636445184 |
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| author | Khanda Hassan Ahmed Stefan Axelsson Yuhong Li Ali Makki Sagheer |
| author_facet | Khanda Hassan Ahmed Stefan Axelsson Yuhong Li Ali Makki Sagheer |
| author_sort | Khanda Hassan Ahmed |
| collection | DOAJ |
| description | The existing fraud detection methods present limitations such as imbalanced data, incorrect identification of fraudulent cases, limited applicability to different scenarios, and difficulties processing data in real-time. This paper proposes an ensemble machine-learning model for detecting fraud in credit card transactions. It also integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbor (ENN) to address the problem of the imbalanced datasets. The experimental results show that our approach performs better than the existing methods. Therefore, it will establish an essential framework for the ongoing investigations in developing more robust and flexible systems for fraud detection. |
| format | Article |
| id | doaj-art-d147b938034c44dab1b8e5ac64e05a3a |
| institution | DOAJ |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-d147b938034c44dab1b8e5ac64e05a3a2025-08-20T02:39:35ZengElsevierMachine Learning with Applications2666-82702025-06-012010067510.1016/j.mlwa.2025.100675A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data samplingKhanda Hassan Ahmed0Stefan Axelsson1Yuhong Li2Ali Makki Sagheer3DSV, Stockholm University, Stockholm, Sweden; Computer Science Department, College of Science, University of Sulaimani, Sulaymaniyah, Iraq; Corresponding author.DSV, Stockholm University, Stockholm, SwedenDSV, Stockholm University, Stockholm, SwedenCollege of Computer Science and Information Technology, University of Anbar, Anbar, IraqThe existing fraud detection methods present limitations such as imbalanced data, incorrect identification of fraudulent cases, limited applicability to different scenarios, and difficulties processing data in real-time. This paper proposes an ensemble machine-learning model for detecting fraud in credit card transactions. It also integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbor (ENN) to address the problem of the imbalanced datasets. The experimental results show that our approach performs better than the existing methods. Therefore, it will establish an essential framework for the ongoing investigations in developing more robust and flexible systems for fraud detection.http://www.sciencedirect.com/science/article/pii/S2666827025000581Ensemble modelMachine learningData imbalanceCredit card fraud detection |
| spellingShingle | Khanda Hassan Ahmed Stefan Axelsson Yuhong Li Ali Makki Sagheer A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling Machine Learning with Applications Ensemble model Machine learning Data imbalance Credit card fraud detection |
| title | A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling |
| title_full | A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling |
| title_fullStr | A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling |
| title_full_unstemmed | A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling |
| title_short | A credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling |
| title_sort | credit card fraud detection approach based on ensemble machine learning classifier with hybrid data sampling |
| topic | Ensemble model Machine learning Data imbalance Credit card fraud detection |
| url | http://www.sciencedirect.com/science/article/pii/S2666827025000581 |
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