SecureSwipe Enhancing Card Transactions Through Gradient Boosted Fraud Detection

Credit cards have emerged as a dominant mode of payment, both in traditional physical establishments and the realm of online transactions, thanks to advancements in electronic commerce systems and communication technology. However, this surge in popularity has also given rise to an increase in fraudulent activities associated with credit card transactions. Businesses and individuals face significant financial losses each year due to fraudulent credit card transactions, as criminals continuously devise new methods to exploit vulnerabilities. Researchers encounter substantial challenges in identifying credit card theft, as fraudsters exhibit quick thinking and resourcefulness. The datasets provided for credit card fraud detection pose an additional obstacle, being severely unbalanced and complicating the development of effective detection systems. Responsible and secure credit card usage offers numerous benefits, but engaging in fraudulent behaviour can have detrimental effects on one’s credit and financial well-being. Addressing the escalating issue of credit card theft has prompted the proposal of several solutions. The widespread adoption of electronic payments has heightened the importance of efficient and effective methods for identifying fraudulent transactions. In this study, a machine learning approach, specifically the Gradient Boosting Classifier, is recommended as an intelligent means of detecting fraud in credit card transactions.