Credit Card Fraud Detection Using AdaBoost and Majority Voting
Document Type
Article
Publication Date
1-1-2018
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.
Keywords
AdaBoost, classification, credit card, fraud detection, predictive modelling, voting
Divisions
fsktm
Publication Title
IEEE Access
Volume
6
Publisher
Institute of Electrical and Electronics Engineers