FRAUDULENT TRANSACTION DETECTION BY MACHINE AND DEEP LEARNING ALGORITHMS

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-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. Additionally, we investigate 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