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.
Highlights
Fraud is a wrongful or criminal deception aimed to bring financial or personal gain [1]
A study on credit card fraud detection using machine learning algorithms has been presented in this paper
A publicly available credit card data set has been used for evaluation using individual models and hybrid models using AdaBoost and majority voting combination methods
Summary
Fraud is a wrongful or criminal deception aimed to bring financial or personal gain [1]. A total of twelve machine learning algorithms are used for detecting credit card fraud. The algorithms range from standard neural networks to deep learning models They are evaluated using both benchmark and real-world credit card data sets. K. Randhawa et al.: Credit Card Fraud Detection Using AdaBoost and Majority Voting methods are applied for forming hybrid models. The key contribution of this paper is the evaluation of a variety of machine learning models with a real-world credit card data set for fraud detection.
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