Work

Credit Card Fraud Detection Using AdaBoost and Majority Voting

Year: 2018

Type: article

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.

Source: IEEE Access

DOI: 10.1109/access.2018.2806420

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

Institutions: University of Malaya, Tunku Abdul Rahman University of Management and Technology, Swinburne University of Technology Sarawak Campus, Deakin University, Tongji University, Brunel University of London

Language: English

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

OpenAlex ID: W2785637175

FWCI: 18.84

Cites: 26

Cited by: 483

Related to: 10