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Credit Card Fraud Detection Using AdaBoost and Majority Voting

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Version 2 2024-06-06, 08:08
Version 1 2018-07-12, 16:06
journal contribution
posted on 2024-06-06, 08:08 authored by K Randhawa, CK Loo, M Seera, Chee Peng Lim, AK Nandi
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.

History

Journal

IEEE Access

Volume

6

Pagination

14277-14284

Location

Piscataway, N.J.

Open access

  • Yes

ISSN

2169-3536

eISSN

2169-3536

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC