Credit Card Fraud Detection Using AdaBoost and Majority Voting

Randhawa, Kuldeep and Loo, Chu Kiong and Seera, Manjeevan and Lim, Chee Peng and Nandi, Asoke K. (2018) Credit Card Fraud Detection Using AdaBoost and Majority Voting. IEEE Access, 6. pp. 14277-14284. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2018.2806420

Abstract

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.

Item Type: Article
Uncontrolled Keywords: AdaBoost; classification; credit card; fraud detection; predictive modelling; voting
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 16 Apr 2019 02:19
Last Modified: 16 Apr 2019 02:19
URI: http://eprints.um.edu.my/id/eprint/20919

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