EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 87 (2026) ITM Web Conf., 87 (2026) 01004 References
Open Access
Issue
ITM Web Conf.
Volume 87, 2026
2nd International Conference on Computing Paradigms (ICCP-2026)
Article Number 01004
Number of page(s) 8
DOI https://doi.org/10.1051/itmconf/20268701004
Published online 30 June 2026
  1. R. Kapale, P. Deshpande, S. Shukla, S. Kediya, Y. Pethe, and S. Metre, "Explainable AI for Fraud Detection: Enhancing Transparency and Trust in Financial Decision Making," in Proc. 2024 2nd Int. Conf. Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI), India, 2024,pp.1–6,doi: 10.1109/IDICAIEI62408.2024.10842874. https://ieeexplore.ieee.org/document/10842874 [Google Scholar]
  2. Y. Chen, C. Zhao, Y. Zhang, Y. Xu, and C. Nie, "Deep Learning in Financial Fraud Detection: Innovations, Challenges, and Applications," Data Science and Management,2025, pp. 1– 6. doi: 10.1016/j.dsm.2025.08.002. https://www.sciencedirect.com/science/article/pii/S2666764925000372 [Google Scholar]
  3. S. Samant, S. Patil, S. Kadam, and P. Patil, "SMOTE Based Credit Card Fraud Detection for Imbalanced Dataset," in Proc. 2024 Int. Conf. Computing, Communication and Intelligent Systems (ICCCIS), Greater Noida, India, 2024, pp. 1–6, doi: 10.1109/ICCCIS60748.2024.10688312. https://ieeexplore.ieee.org/document/10688312/authors#authors [Google Scholar]
  4. R. Rinku, A. K. Dubey, S. K. Narang, and N. Kishore, "A Machine Learning Based Approach for the Fraud Detection in Imbalanced Credit Card Transaction Dataset," SSRG Int. J. Electron. Commun. Eng., vol. 11, no. 8, pp. 244–259, 2024, doi: 10.14445/23488549/IJECE-V11I8P124. https://www.internationaljournalssrg.org/IJECE/paperdetails?Id=665 [Google Scholar]
  5. M. Kavitha and M. Suriakala, "Real Time Credit Card Fraud Detection on Huge Imbalanced Data Using Meta- Classifiers," in Proc. 2017 Int. Conf. Inventive Computing and Informatics (ICICI), Coimbatore, India, 2017, pp. 881–887, doi: 10.1109/ICICI.2017.8365263. https://ieeexplore.ieee.org/abstract/document/8365263 [Google Scholar]
  6. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique," J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002, doi: 10.1613/jair.953. https://www.internationaljournalssrg.org/IJECE/paperdetails?Id=665 [CrossRef] [Google Scholar]
  7. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, 2016, pp. 785–794, doi: 10.1145/2939672.2939785. https://arxiv.org/pdf/1603.02754 [Google Scholar]
  8. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, "Extracting and Composing Robust Features with Denoising Autoencoders," in Proc. 25th Int. Conf. Machine Learning (ICML), Helsinki, Finland, 2008, pp. 1096–1103, doi: 10.1145/1390156.1390294. https://www.cs.toronto.edu/~larocheh/publications/icml-2008-denoising-autoencoders.pdf [Google Scholar]
  9. B. Alshawi, "Utilizing GANs for Credit Card Fraud Detection: A Comparison of Supervised Learning Algorithms," Eng. Technol. Appl. Sci. Res., vol. 13, no. 6, pp. 12264–12270, Dec. 2023, doi: 10.48084/etasr.6434. https://www.etasr.com/index.php/ETASR/article/view/6434 [Google Scholar]
  10. T. J. Berkmans and S. Karthick, "Anomaly detection in online credit card data using optimized multi-view heterogeneous graph neural networks," Knowl.-Based Syst., pp. 1–6, 2025, doi: 10.1016/j.knosys.2025.113767. https://www.sciencedirect.com/science/article/abs/pii/S0950705125008135 [Google Scholar]
  11. V. Jurgovsky, M. Granitzer, S. Ziegler, S. Calabretto, P. Portier, L. He-Guelton, and O. Caelen, "Sequence Classification for Credit-Card Fraud Detection," in Proc. 2018 IEEE Int. Conf. Data Science and Advanced Analytics (DSAA), Turin, Italy, 2018, pp. 1–10, doi:10.1109/DSAA.2018.00017. https://www.sciencedirect.com/science/article/abs/pii/S0957417418300435?via%3Dihub [Google Scholar]
  12. A. Dal Pozzolo, O. Caelen, Y.-A. Le Borgne, S. Waterschoot, and G. Bontempi, "Learned Lessons in Credit Card Fraud Detection from a Practitioner Perspective, "Expert Systems with Applications, vol. 41, no. 10, pp. 4915–4928, 2014, doi: 10.1016/j.eswa.2014.02.001. https://www.sciencedirect.com/science/article/abs/pii/S0957417414000505?via%3Dihub [Google Scholar]
  13. S. Fiore, F. Palmieri, A. Castiglione, and A. DeSantis, "Using Generative Adversarial Networks for Improving Classification Effectiveness in Credit Card Fraud Detection," Information Sciences, vol. 479, pp. 448–455, 2019, doi: 10.1016/j.ins.2018.02.004. https://www.sciencedirect.com/science/article/abs/pii/S0957417414000505?via%3Dihub [Google Scholar]
  14. L. Carcillo, Y.-A. LeBorgne, O. Caelen, and G. Bontempi, "Streaming Active Learning Strategies for Real- Life Credit Card Fraud Detection: Assessment and Visualization," International Journal of Data Science and Analytics, vol. 5, no. 4, pp. 285–300, 2018, doi: 10.1007/s41060-017-0086-4. https://arxiv.org/abs/1804.07481 [Google Scholar]
  15. J. West and M. Bhattachara, "Intelligent Financial Fraud Detection: A Comprehensive Review, "Computers & Security, vol. 57, pp. 47–66, 2016, doi:10.1016/j.cose.2015.09.005. https://www.sciencedirect.com/science/article/abs/pii/S0167404815001261?via%3Dihub [Google Scholar]
  16. S. Roy, A. Sun, R. Mahoney, L. Alonzi, S. Adams, and P. Beling, "Deep Learning Detects Fraudulent Transactions in Credit Card Data," in Proc. 2018 IEEE Int. Conf. Data Mining Workshops (ICDMW), Singapore, 2018, pp. 1–8, doi: 10.1109/ICDMW.2018.00021. https://ieeexplore.ieee.org/document/8637420 [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.