Unveiling the Optimal Approach for Credit Card Fraud Detection: A Thorough Analysis of Deep Learning and Machine Learning Methods

Abstract

To identify credit card fraud, this study looked at three kind of datasets with various data manipulations, machine learning algorithms, and cross-validation techniques. In both simulated and real datasets, the Random Forest Classi�er with Repeated K-Fold Cross-Validation consistently outperformed competing models. Although deep learning algorithms were investigated, the Random Forest Classi�er continued to be the best option. A hybrid model of the Random Forest Classi�er and Arti�cial Neural Networks (ANN) was also unable to outperform the Random Forest Classi�er on its own. Thus this study suggests the Random Forest Classi�er with Repeated K-Fold Cross-Validation as the robust reliable method for detecting credit card fraud in balanced considered datasets, providing useful insights for enhancing security precautions and �nancial system defense against various banking sector frauds.