A Comparative Study of Machine Learning Models for Credit Card Fraud Detection

Abstract

Currently, credit card fraud detection is a unique problem in the financial sector, with both institutions and consumers facing increasingly significant losses. Despite the growing application of machine learning (ML) techniques in this domain, existing methods often struggle with issues such as high false-positive rates, imbalanced data, and the complexity of evolving fraud patterns. This paper investigates the comparative performance of various machine learning models in credit card fraud detection, focusing on traditional models (such as Support Vector Machine, Decision Tree), ensemble methods (Random Forest, XGBoost,deep learning models (Multilayer Perceptron, Artificial Neural Networks). Three distinct datasets, including both balanced and imbalanced sets, are used to evaluate these models. The results indicate that ensemble models like Random Forest and XGBoost demonstrate superior performance, particularly in terms of accuracy, precision, recall, and F1 score, when compared to other models. However, models such as Support Vector Machine and Artificial Neural Networks exhibit lower recall in imbalanced datasets, suggesting potential limitations in their application to real-world fraud detection scenarios. This study also identifies key challenges, such as the difficulty in adapting to dynamic fraud strategies and the need for real-time monitoring. Future research directions are proposed, including the integration of deep learning architectures and adaptive learning mechanisms to enhance the detection system’s real-time response and accuracy. The findings provide a robust foundation for further development of credit card fraud detection systems and offer practical insights for financial institutions seeking to mitigate fraud-related risks.