CREDIT CARD FRAUD DETECTION USING STATE-OF-THE-ART MACHINE LEARNING AND DEEP LEARNING ALGORITHMS

Abstract

redit card fraud continues to pose a significant threat to financial institutions
and consumers worldwide. In recent years, the proliferation of advanced technology
has enabled fraudsters to develop increasingly sophisticated methods for perpetrating
fraudulent transactions. To combat this ever-evolving challenge, this study explores
the application of state-of-the-art machine learning and deep learning algorithms for
credit card fraud detection. This research leverages a comprehensive dataset
containing both legitimate and fraudulent credit card transactions, allowing for the
evaluation of various detection methods. We employ a diverse set of machine learning
and deep learning models, including Random Forest, Support Vector Machine,
Gradient Boosting, and Convolutional Neural Networks (CNNs), among others, to
assess their performance in identifying fraudulent activities. The results of our
experiments demonstrate the efficacy of deep learning techniques, particularly CNNs,
in achieving higher accuracy and improved fraud detection rates when compared to
traditional machine learning algorithms.