Online Payment Fraud Detection using Machine Learning in Python (KEY IJP************355)

• Dipak Nitin Narkhede

Abstract

AbstractThe proliferation of online transactions has brought unprecedented convenience to consumers worldwide, but it has also given rise to a significant challenge: online fraud in payment transactions. This research paper delves into the multifaceted nature of online fraud in payment transactions, examining its various forms, including identity theft, account takeover, and card-not-present fraud. Drawing on a review of existing literature and case studies, this paper explores the underlying mechanisms of online fraud and identifies key vulnerabilities in current payment systems. It discusses the role of technology in fraud detection and prevention, highlighting the importance of machine learning algorithms, biometric authentication, and anomaly detection techniques. Furthermore, this paper examines the regulatory landscape surrounding online payment security, analysing the effectiveness of current regulations and standards in combating fraud. It also explores the challenges faced by law enforcement agencies and financial institutions in investigating and prosecuting online fraudsters. In conclusion, this research paper proposes a holistic approach to combatting online fraud in payment transactions, emphasizing the need for collaboration between stakeholders, the adoption of advanced technology, and the implementation of robust regulatory frameworks. By addressing these challenges, we can enhance the security of online payment systems and foster trust in the digital economy.