What question did this study set out to answer?

This research aims to improve credit card fraud detection using machine learning techniques and hybrid methods.

January 1, 2018Open Access

Credit Card Fraud Detection Using AdaBoost and Majority Voting

KRKuldeep Kaur RandhawaSwinburne University of Technology Sarawak Campus CLChu Kiong LooUniversity of Malaya MSManjeevan SeeraUniversiti Sains Islam Malaysia

Key Points

This research aims to improve credit card fraud detection using machine learning techniques and hybrid methods.
Utilized standard machine learning models for initial fraud detection.
Applied hybrid techniques incorporating AdaBoost and majority voting on real-world data.
Evaluated model efficacy using a publicly available credit card dataset and a financial institution's dataset.
Majority voting method showed high accuracy rates in detecting fraud cases.
Performance of algorithms was assessed under varying noise conditions, demonstrating their robustness.

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.

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