Analysis and Design of Fraud Detection and Prevention Techniques In Card Based Financial

Abstract

Due to the exponential surge in fraudulent activities brought on by the growing use of card-based financial transactions, individuals, businesses, and banking firms have sustained enormous financial losses. As a result, methods for identifying and avoiding fraud are becoming crucial components of the financial ecosystem. The study and design of fraud detection and prevention strategies in cardbased financial systems are the main topics of this research.The outline of the many forms of fraud that may happen in card-based financial systems, such as skimming, phishing, counterfeiting, and identity theft, is provided in the first section of the article. The second section covers the several methods for detecting and preventing fraud, including rule-based systems, anomaly detection, machine learning, and deep learning. The next section of the study offers a detailed review of a few of the most efficient fraud detection and prevention strategies, such as support vector machines, decision trees, and neural networks. It also looks at the many aspects of data quality, feature choice, and model choice that influence the precision and effectiveness of these strategies. The need of creating a thorough fraud prevention system that includes a variety of detection and prevention measures is covered in the paper's last section. In order to assure the system's efficiency in thwarting fresh and evolving cybercrimes, it also highlights the necessity of routine system monitoring and update.