Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01013
Number of page(s) 8
DOI https://doi.org/10.1051/epjconf/202534101013
Published online 20 November 2025
  1. M. Fiore, F. De Santis, F. Perla, P. Zanetti, and A. Palmieri, "Using generative adversarial networks for improving classification effectiveness in credit card fraud detection," Information Sciences, vol. 479, pp. 448-455, Apr. 2019. [Online]. Available: https://doi.org/10.1016/j.ins.2018.02.060 [Google Scholar]
  2. M. Bahnsen, D. Aouada, and B. Ottersten, "Feature engineering strategies for credit card fraud detection," Expert Systems with Applications, vol. 51, pp. 134-142, June 2016. [Online]. Available: https://doi.org/10.10167j.eswa.2015.12.030 [CrossRef] [Google Scholar]
  3. J. Devlin, M. Chang, K. Lee, and K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proc. NAACL-HLT, 2019, pp. 4171-4186. [Online]. Available: https://arxiv.org/abs/1810.04805 [Google Scholar]
  4. Vaswani et al., "Attention is All You Need," in Proc. NeurIPS, 2017, pp. 5998-6008. [Online]. Available: https://arxiv.org/abs/1706.03762 [Google Scholar]
  5. J. Shah, "Online Payment Fraud Detection Dataset," Kaggle, 2022. [Online]. Available: https://www.kaggle.com/datasets/jainilcoder/online-payment-fraud-detection [Google Scholar]
  6. R. B. and S. K. K. R., "Credit card fraud detection using artificial neural network," Global Transitions Proceedings, vol. 2, pp. 35-41, 2021. Available: www.elsevier.com/locate/gltp. [Google Scholar]
  7. W. Lin, L. Sun, Q. Zhong, C. Liu, J. Feng, X. Ao, and H. Yang, "Online Credit Payment Fraud Detection via Structure-Aware Hierarchical Recurrent Neural Network," in Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21), 2021. [Google Scholar]
  8. S. Vimal, K. Kayathwal, H. Wadhwa, and G. Dhama, "Application of Deep Reinforcement Learning to Payment Fraud," arXiv preprint arXiv:2112.04236, 2021. [Online]. Available: https://arxiv.org/abs/2112.04236. [Google Scholar]
  9. S. Al Balawi and N. Aljohani, "Credit-card Fraud Detection System using Neural Networks," The International Arab Journal of Information Technology, vol. 20, no. 2, pp. 194-200, Mar. 2023. [Google Scholar]
  10. C. R. Bhat and M. Nelson, "Artificial Intelligence Based Credit Card Fraud Detection for Online Transactions Optimized with Sparrow Search Algorithm," International Journal of Performability Engineering, vol. 19, no. 9, pp. 624-632, Sep. 2023. DOI: 10.23940/ijpe.23.09.p7.624632. [Google Scholar]
  11. Khalid, A.R.; Owoh, N.; Uthmani, O.; Ashawa, M.; Osamor, J.; Adejoh, J. Enhancing Credit Card Fraud Detection: An Ensemble Machine Learning Approach. Big Data Cogn. Comput. 2024, 8, 6. https://doi.org/10.3390/bdcc8010006 [Google Scholar]
  12. A. Almazroi and N. Ayub, "Online Payment Fraud Detection Model Using Machine Learning Techniques," in IEEE Access, vol. 11, pp. 137188-137203, 2023, doi: 10.1109/ACCESS.2023.3339226. [Google Scholar]
  13. H. Abbassi, S. El Mendili, and Y. Gahi, "Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion," HighTech and Innovation Journal, vol. 5, no. 1, pp. 185, Mar. 2024. ISSN: 2723-9535. Available: www.hightechjournal.org. [Google Scholar]
  14. Nazeer, K. D. V. Prasad, P. Bahadur, V. Bapat, and K. M. J., "Synchronization of AI and Deep Learning for Credit Card Fraud Detection," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 5s, pp. 52-59, 2023. ISSN: 2147-6799. Available: www.ijisae.org. [Google Scholar]
  15. M. Lokanan, "Predicting Mobile Money Transaction Fraud using Machine Learning Algorithms," Qeios, 2022. doi: 10.32388/ELVM4L. [Google Scholar]
  16. Singh, Jashandeep & Kaur, Prabhjot. (2023). Fraud Detection in Online Transactions Using Machine Learning. 10.13140/RG.2.2.29971.66088. [Google Scholar]
  17. M. Farouk, N. S. Ragab, D. Salama, and O. E., "Fraud_Detection_ML: Machine Learning Based on Online Payment Fraud Detection," Journal of Computing and Communication, vol. 3, no. 1, pp. 116-131, 2024. [Google Scholar]
  18. N. Pachhala, M. D. S. Sai, P. Prudhvi, G. M. N. V. S. Gopi, I. G. N. S. Ram, and M. R. Sandeep, "Online Payment Fraud Detection," International Journal of Innovative Science and Research Technology, vol. 8, no. 10, pp. 1191-1195, Oct. 2023. [Google Scholar]
  19. Sharma, G. R. B. R., B. Ramamurthy, and H. B. R., "Credit Card Fraud Detection Using Deep Learning Based on Auto-Encoder," ITM Web of Conferences, vol. 50, art. no. 01001, 2022. [Google Scholar]
  20. Borketey, B. (2024) Real-Time Fraud Detection Using Machine Learning. Journal of Data Analysis and Information Processing, 12, 189-209. https://doi.org/10.4236/jdaip.2024.122011 [Google Scholar]
  21. Hove, D., Olugbara, O., & Singh, A. (2024). Bibliometric analysis of recent trends in machine learning for online credit card fraud detection. Journal of Scientometric Research, 13(1), 43-57. https://doi.org/10.5530/jscires.13.L4 [Google Scholar]
  22. Liu, B., Chen, X., & Yu, K. (2023). Online transaction fraud detection system based on machine learning. Journal of Physics: Conference Series, 2023(1), 012054. https://doi.org/10.1088/17426596/2023/1/012054 [Google Scholar]
  23. Mangathayaru, N., Ravi Kumar, N., & Rajesh Kumar, G. (2023). Fraudulent transaction detection by machine and deep learning algorithms. Journal of Physics: Conference Series, 30(14), 446-453. [Google Scholar]
  24. Tang, Y. (2023). Automatic fraud detection in e-commerce transactions using deep reinforcement learning and artificial neural networks. International Journal of Advanced Computer Science and Applications (IJACSA), 14(7), 1047. Retrieved from www.ijacsa.thesai.org [Google Scholar]
  25. Marazqah Btoush EAL, Zhou X, Gururajan R, Chan KC, Genrich R, Sankaran P. 2023. A systematic review of literature on credit card cyber fraud detection using machine and deep learning. PeerJ Computer Science 9:e1278 https://doi.org/10.7717/peerj-cs.1278 [Google Scholar]
  26. Qayoom A, Khuhro MA, Kumar K, Waqas M, Saeed U, ur Rehman S, Wu Y, Wang S. 2024. A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme. PeerJ Computer Science 10:e1998 https://doi.org/10.7717/peerj-cs.1998 [Google Scholar]
  27. Hajek, P., Abedin, M.Z. & Sivarajah, U. Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework. Inf Syst Front 25, 1985-2003 (2023). https://doi.org/10.1007/s10796-022-10346-6 [Google Scholar]
  28. Benchaji, I., Douzi, S., El Ouahidi, B. et. al. Enhanced credit card fraud detection based on attention mechanism and LSTM deep model. J Big Data 8, 151 (2021). https://doi.org/10.1186/s40537-021-00541-8 [Google Scholar]
  29. Vanini, P., Rossi, S., Zvizdic, E. et. al. Online payment fraud: from anomaly detection to risk management. Financ Innov 9, 66 (2023). https://doi.org/10.1186/s40854-023-00470-w [Google Scholar]
  30. Bin Sulaiman, R., Schetinin, V & Sant, P. Review of Machine Learning Approach on Credit Card Fraud Detection. Hum-Cent Intell Syst 2, 55-68 (2022). https://doi.org/10.1007/s44230-022-00004-0 [Google Scholar]
  31. Sai, Chaithanya Vamshi and Das, Debashish and Elmitwally, Nouh and Elezaj, Ogerta and Islam, Md Baharul, Explainable Ai-Driven Financial Transaction Fraud Detection Using Machine Learning and Deep Neural Networks. Available at SSRN: https://ssrn.com/abstract=4439980 or https://doi.org/10.2139/ssrn.4439980 [Google Scholar]
  32. Aslam and A. Hussain, "A Performance Analysis of Machine Learning Techniques for Credit Card Fraud Detection," J. Artif. Intell., vol. 6, no. 1, pp. 1-21. 2024. https://doi.org/10.32604/jai.2024.047226 [Google Scholar]
  33. Sanober, Sumaya, Alam, Izhar, Pande, Sagar, Arslan, Farrukh, Rane, Kantilal Pitambar, Singh, Bhupesh Kumar, Khamparia, Aditya, Shabaz, Mohammad, An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication, Wireless Communications and Mobile Computing, 2021, 6079582, 14 pages, 2021. https://doi.org/10.1155/2021/6079582 [Google Scholar]
  34. https://www.kaggle.com/datasets/jainilcoder/online-payment-fraud-detection/data [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.