FRAUDULENT TRANSACTION RECOGNITION WITH CUTTING-EDGE MACHINE AND DEEP LEARNING MODELS FOR CREDIT CARD SECURITY

Abstract

Credit 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-theart
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 whencompared to traditional machinelearning algorithms. Additionally, weinvestigate the interpretability of these models and discuss the trade-offs
between model complexity and
performance. this study investigates the
importance of feature engineering,
dimensionality reduction, and hyper
parameter tuning to optimize the
algorithms' performance. We also
explore ensemble techniques, such as stacking and boosting, to harness the strengths of multiple models and enhance overall fraud detection capabilities.