DOAJ Open Access 2018

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Kuldeep Randhawa Chu Kiong Loo Manjeevan Seera Chee Peng Lim Asoke K. Nandi

Abstrak

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.

Topik & Kata Kunci

Electrical engineering. Electronics. Nuclear engineering

Penulis (5)

Kuldeep Randhawa

Chu Kiong Loo

Manjeevan Seera

Chee Peng Lim

Asoke K. Nandi

Format Sitasi

APA MLA BibTeX

Randhawa, K., Loo, C.K., Seera, M., Lim, C.P., Nandi, A.K. (2018). Credit Card Fraud Detection Using AdaBoost and Majority Voting. https://doi.org/10.1109/ACCESS.2018.2806420

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Informasi Jurnal

Tahun Terbit: 2018
Sumber Database: DOAJ
DOI: 10.1109/ACCESS.2018.2806420
Akses: Open Access ✓