Transact Safe: A Machine Learning Shield against Online Fraud

Online transactions have become a fundamental part of daily life in the current digital age, acting as a crucial conduit for important financial activity. The prevalence of fraudulent activities in online transactions, however, presents a serious risk to the reliability and security of digital financial systems. This survey article provides a thorough examination of modern approaches used to identify online transaction fraud. Through an exploration of the most recent developments in statistical and machine learning methods, we outline a terrain of creative strategies intended to reduce fraudulent activity. In addition, we carefully analyze the built-in constraints and new difficulties that face existing fraud detection techniques, opening the door for further research. We discuss various fraud detection algorithms and compare them, explaining how they perform in terms of important parameters like accuracy, sensitivity, and specificity. In the end, this survey report proves to be a priceless tool that helps practitioners and scholars alike successfully and precisely traverse the complex world of online transaction fraud detection.