Credit Card Transaction Fraud Using Machine Learning Algorithms
Available Online January 2020.
- https://doi.org/10.2991/icesed-19.2020.14How to use a DOI?
- Credit card fraud, Z scale, Feature selection, Machine learning algorithms.
- Credit cards offered significant advantages over all forms of money: they’re pocket size, easily portable, relatively secure and have no intrinsic value themselves. However, payment fraud is an ideal use case for machine learning algorithms and has a long track record of successful use. Machine learning has just been invented, or just been applied to payments fraud for the first time. This paper focuses on the main function of the feature selection in supervised model. The methods used to support the topic are neural net, boosted tree, random forest etc. and the material is credit card transaction data. The conclusion of the research is that banks should deny about 3% clients for balance the profits and loss of goods. A month was spent doing this research with the author’s partners and professor for getting the results as accurate as possible.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jiayi Huang PY - 2020/01 DA - 2020/01 TI - Credit Card Transaction Fraud Using Machine Learning Algorithms BT - Proceedings of the 2019 International Conference on Education Science and Economic Development (ICESED 2019) PB - Atlantis Press SP - 273 EP - 282 SN - 2352-5428 UR - https://doi.org/10.2991/icesed-19.2020.14 DO - https://doi.org/10.2991/icesed-19.2020.14 ID - Huang2020/01 ER -