Unveiling the Optimal Approach for Credit Card Fraud Detection: A Thorough Analysis of Deep Learning and Machine Learning Methods
Year: 2024
Type: article
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 Classifier with Repeated K-Fold Cross-Validation consistently outperformed competing models. Although deep learning algorithms were investigated, the Random Forest Classifier continued to be the best option. A hybrid model of the Random Forest Classifier and Artificial Neural Networks (ANN) was also unable to outperform the Random Forest Classifier on its own. Thus this study suggests the Random Forest Classifier 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 financial system defense against various banking sector frauds.
Authors: Ubaida Fatima, Sadia Kiran, Muhammad Fouzan Akhter, Muhammad Kumail, Jaweria Sohail
Institution: NED University of Engineering and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405846945