Credit card fraud detection using AdaBoost and majority voting

Randhawa, Kuldeep, Loo, Chu Kiong, Seera, Manjeevan, Lim, Chee Peng and Nandi, Asoke K 2018, Credit card fraud detection using AdaBoost and majority voting, IEEE access, vol. 6, pp. 14277-14284, doi: 10.1109/ACCESS.2018.2806420.

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Title Credit card fraud detection using AdaBoost and majority voting
Author(s) Randhawa, Kuldeep
Loo, Chu Kiong
Seera, Manjeevan
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Nandi, Asoke K
Journal name IEEE access
Volume number 6
Start page 14277
End page 14284
Total pages 8
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2018
ISSN 2169-3536
Keyword(s) AdaBoost
classification
credit card
fraud detection
predictive modelling
voting
Summary Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.
Language eng
DOI 10.1109/ACCESS.2018.2806420
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2018, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30111179

Document type: Journal Article
Collections: Centre for Intelligent Systems Research
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