Credit Card Fraud Detection Using AdaBoost and Majority Voting

TLDR

Credit card fraud causes billions of dollars in annual losses, yet research on real‑world data is scarce due to confidentiality constraints. The study applies machine learning algorithms to detect credit card fraud. The authors first applied standard models, then hybrid AdaBoost and majority voting methods, evaluating them on a public dataset and a real‑world dataset from a financial institution, and added noise to assess robustness. Results show that majority voting achieves high accuracy in detecting credit card fraud.

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.