Open Access
Issue |
SHS Web Conf.
Volume 218, 2025
2025 2nd International Conference on Development of Digital Economy (ICDDE 2025)
|
|
---|---|---|
Article Number | 02017 | |
Number of page(s) | 9 | |
Section | Finance Tech Advances: Impacts and Innovations | |
DOI | https://doi.org/10.1051/shsconf/202521802017 | |
Published online | 03 July 2025 |
- B. Cruz, Credit card fraud 2021 annual report: prevalence, awareness, and prevention. Securityorg, 2023. Available at: https://www.security.org/digital-safety/credit-card-fraud-report/ [Google Scholar]
- R. J. Bolton, Hand, D. J., Provost, F., Breiman, L. Statistical fraud detection: a review. Statistical Science 17, 235–255 (2002) [CrossRef] [Google Scholar]
- S. Chauhan, L. Vig, Anomaly detection in ECG time signals via deep long short-term memory networks. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), October 2015 [Google Scholar]
- I. D. Mienye, N. Jere, Deep learning for credit card fraud detection: a review of algorithms, challenges, and solutions. IEEE Access 12, 96893–96910 (2024) [CrossRef] [Google Scholar]
- C. Whitrow, D. J. Hand, P. Juszczak, D. Weston, N. M. Adams, Transaction aggregation as a strategy for credit card fraud detection. Data Min. Knowl. Discov. 18, 30–55 (2008) [Google Scholar]
- S. Bhattacharyya, S. Jha, K. Tharakunnel, J. C. Westland, Data mining for credit card fraud: a comparative study. Decis. Support Syst. 50, 602–613 (2011) [CrossRef] [Google Scholar]
- J. M. Bachmann, Credit fraud || dealing with imbalanced datasets. Kagglecom, 2019. Available at: https://www.kaggle.com/code/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets. [Google Scholar]
- Nilson Report, Card fraud losses worldwide. Nilson Report, 2022. Available at: https://nilsonreport.com/articles/card-fraud-losses-worldwide-2/. [Google Scholar]
- M. Castillo, Why credit card fraud alerts are rising, and how worried you should be about them. CNBC, September 12, 2024. Available at: https://www.cnbc.com/2024/09/12/why-credit-card-fraud-alerts-are-rising.html. [Google Scholar]
- B. Lundgren, How software developers can fix part of GDPR’s problem of click-through consents. AI Soc. (2020) [Google Scholar]
- N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002) [CrossRef] [Google Scholar]
- H. He, E. A. Garcia, Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21, 1263–1284 (2009) [CrossRef] [Google Scholar]
- S. Lundberg, S.-I. Lee, A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874 (2017) [Google Scholar]
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, Attention is all you need. arXiv preprint arXiv:1706.03762 (2017) [Google Scholar]
- J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, A. Bouchachia, A survey on concept drift adaptation. ACM Comput. Surv. 46, 1–37 (2014) [CrossRef] [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.