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Review Article
Credit Card Fraud Detection: A Comprehensive Review of Machine Learning Techniques
Dr. Durgadevi P1
M Adethya2
1 2 Department of Computer Science and Engineering, SRM Institute of Science and Technology Vadapalani, Chennai, Tamilnadu, India.
Published Online: September-December 2025
Pages: 242-248
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403038
References
1. R. Bin Sulaiman, V. Schetinin, and P. Sant, “Review of machine learning approach on credit card fraud detection,” Human-Centric Intelligent Systems, vol. 2, no. 1, pp. 55–68, 2022.
2. J. O. Awoyemi, A. O. Adetunmbi, and S. A. Oluwadare, “Credit card fraud detection using machine learning techniques: A comparative anal- ysis,” in 2017 international conference on computing networking and informatics (ICCNI). IEEE, 2017, pp. 1–9.
3. G. K. Kulatilleke and S. Samarakoon, “Empirical study of machine learning classifier evaluation metrics behavior in massively imbalanced and noisy data,” arXiv preprint arXiv: 2208.11904, 2022.
4. A. Thennakoon, C. Bhagyani, S. Premadasa, S. Mihiranga, and N. Ku- ruwitaarachchi, “Real-time credit card fraud detection using machine learning,” in 2019 9th international conference on cloud computing, data science & engineering (Confluence). IEEE, 2019, pp. 488–493.
5. E. C¸ elik, D. Dal, and F. Bozkurt, “Analysis of the effectiveness of various machine learning, artificial neural network and deep learning methods in detecting fraudulent credit card transactions,” Erzincan University Journal of Science and Technology, vol. 15, no. 1, pp. 144–167, 2022.
6. F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” Ieee Access, vol. 10, pp. 39 700– 39 715, 2022.
7. S. O. Akinwamide, F. Taiwo, and F. B. Ibitayo, “Prediction of fraudulent or genuine transactions on credit card fraud detection dataset using machine learning techniques,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 6, pp. 5061–5071, 2022.
8. S. Maniraj, A. Saini, S. Ahmed, and S. Sarkar, “Credit card fraud detection using machine learning and data science,” International Journal of Engineering Research, vol. 8, no. 9, pp. 110–115, 2019.
9. M. N. Hossain, M. M. Hassan, and R. J. Monir, “Analyzing the classi- fication accuracy of deep learning and machine learning for credit card fraud detection,” Asian Journal for Convergence in Technology (AJCT) ISSN-2350-1146, vol. 8, no. 3, pp. 31–36, 2022.
2. J. O. Awoyemi, A. O. Adetunmbi, and S. A. Oluwadare, “Credit card fraud detection using machine learning techniques: A comparative anal- ysis,” in 2017 international conference on computing networking and informatics (ICCNI). IEEE, 2017, pp. 1–9.
3. G. K. Kulatilleke and S. Samarakoon, “Empirical study of machine learning classifier evaluation metrics behavior in massively imbalanced and noisy data,” arXiv preprint arXiv: 2208.11904, 2022.
4. A. Thennakoon, C. Bhagyani, S. Premadasa, S. Mihiranga, and N. Ku- ruwitaarachchi, “Real-time credit card fraud detection using machine learning,” in 2019 9th international conference on cloud computing, data science & engineering (Confluence). IEEE, 2019, pp. 488–493.
5. E. C¸ elik, D. Dal, and F. Bozkurt, “Analysis of the effectiveness of various machine learning, artificial neural network and deep learning methods in detecting fraudulent credit card transactions,” Erzincan University Journal of Science and Technology, vol. 15, no. 1, pp. 144–167, 2022.
6. F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan, and M. Ahmed, “Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms,” Ieee Access, vol. 10, pp. 39 700– 39 715, 2022.
7. S. O. Akinwamide, F. Taiwo, and F. B. Ibitayo, “Prediction of fraudulent or genuine transactions on credit card fraud detection dataset using machine learning techniques,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 6, pp. 5061–5071, 2022.
8. S. Maniraj, A. Saini, S. Ahmed, and S. Sarkar, “Credit card fraud detection using machine learning and data science,” International Journal of Engineering Research, vol. 8, no. 9, pp. 110–115, 2019.
9. M. N. Hossain, M. M. Hassan, and R. J. Monir, “Analyzing the classi- fication accuracy of deep learning and machine learning for credit card fraud detection,” Asian Journal for Convergence in Technology (AJCT) ISSN-2350-1146, vol. 8, no. 3, pp. 31–36, 2022.
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