Combined Feature Set with Logistic Regression Model to Detect Credit Card Frauds in Real Time Applications

Online payment methods are gaining popularity and are widely used, both in-store and online. Because to the Internet and smart mobile devices, conducting such transactions is quick, simple, and stress-free. However, online payment fraud is common due to the open nature of the internet, which allows criminals to use techniques such as eavesdropping, phishing, infiltration, denial-of-service, database theft, and man-in-the-middle assault. Online payment fraud is on the rise, and it is a big contributor to global economic losses. Financial services, healthcare, insurance, and other industries have long been plagued by fraud. Online fraud has developed in tandem with the use of digital payment systems such as credit/debit cards, PhonePe, Gpay, and Paytm. Furthermore, fraudsters and criminals are adept at evasion strategies, allowing them to steal more. Developing a secure system for client authentication and fraud protection is tough since there is always a workaround. This means that fraud detection systems play an important role in preventing financial crimes. Over time, victims of internet transaction fraud have incurred tremendous financial losses. The growth of cutting-edge technologies and global connection has led to a surge in online fraud. To reduce these expenses, it is critical to develop effective fraud detection systems. Machine learning and statistical tools make detecting dishonest money deals much easier. The scarcity of data, the sensitive nature of the data, and the uneven class distributions make it challenging to implement efficient fraud detection models. Given the delicate nature of the information, it is difficult to draw conclusions and construct more accurate models. This study offers a Linked Feature Set with Combined Feature Set with Logistic Regression (CFS-LoR) Model for accurate detection of online payment frauds. In comparison to extant models, the proposed model exhibits a highly accurate detection capability.