Secure Payment Detection System

Abstract

The rapid expansion of digital payment systems has significantly enhanced transactional convenience but has also led to a corresponding surge in online payment fraud. This paper provides a comprehensive analysis of the evolving landscape of online payment fraud, encompassing prevalent attack vectors such as identity theft, account takeovers, and card-not-present (CNP) fraud. Through an extensive review of current literature and real-world case studies, the research identifies critical vulnerabilities within modern payment infrastructures. Furthermore, it emphasizes the role of advanced technological interventions in fraud detection and prevention. Particular focus is given to the application of machine learning algorithms—specifically classification models and anomaly detection methods such as Isolation Forests. The study also underscores the value of biometric authentication and robust feature engineering in enhancing model performance. Evaluation metrics including accuracy, precision, recall, and F1 score are employed to assess the efficacy of the proposed models. This research aims to contribute to the development of more secure and intelligent payment systems through the integration of data analytics and machine learning techniques.