Résumé:
This manuscript focuses on the detection of credit card fraud using machine learning techniques. The rapid increase in digital transactions, especially during the COVID-19 pandemic, has heightened the need for robust fraud detection mechanisms. This study explores the various types of bank fraud, particularly credit card fraud, and provides an overview of the evolution of payment cards and electronic payment systems. The research delves into different machine learning algorithms and tools used for fraud detection, including data preprocessing, feature selection, and handling imbalanced data. It also outlines the system architecture for implementing these techniques in real-world applications. The findings underscore the effectiveness of machine learning in enhancing fraud detection and suggest future research directions for improving security measures
