DESIGN AND IMPLEMENTATION OF A CREDIT CARD FRAUD DETECTION SYSTEM

ABSTRACT In today's globalized economy, the use of credit cards has become the most accepted and convenient mode of payment for both online and offline transactions. The ubiquity of credit cards has facilitated the widespread adoption of cashless policies, enabling consumers to make purchases effortlessly at various retail outlets across the country. This method of payment extends beyond shopping to include the payment of utility bills and other financial obligations, further cementing its role in the everyday financial activities of individuals. The credit card fraud detection system achieves an accuracy of 79.8% with a precision of 85.0% in the logistics regression model and 88.0% in the random forest model after test running 120 datasets. Despite the convenience and efficiency offered by credit cards, their widespread use has also led to a significant increase in the risks associated with credit card fraud. Fraudulent transactions not only result in substantial financial losses for consumers and issuing authorities but also undermine trust in the financial system. Traditional manual credit card fraud detection methods are often reactive rather than proactive, identifying fraudulent transactions only after they have been completed. This delayed detection makes it challenging to mitigate the associated losses promptly, placing a burden on both consumers and financial institutions. Recognizing the limitations of existing fraud detection systems, this research proposes the design and implementation of an advanced credit card fraud detection system. The proposed system leverages modern web technologies, specifically HTML, CSS, and PHP, to create a robust framework for real-time detection and prevention of fraudulent transactions. By integrating these technologies, the system aims to provide an efficient and scalable solution that can promptly identify suspicious activities and mitigate potential losses. The primary objective of this research is to enhance the security of credit card transactions by developing a system that can detect fraud at an early stage, thereby preventing fraudulent transactions before they are completed. The proposed system will employ a combination of rulebased and machine learning techniques to analyze transaction patterns and identify anomalies indicative of fraud. This proactive approach will not only improve the detection accuracy but also significantly reduce the time required to identify and respond to fraudulent activities