Online Fraud Payment Detection Using Balanced ML Algorithms

Abstract

The rapid growth of online transactions has provided remarkable convenience to consumers across the globe, but it has also introduced a major concern: fraud in digital payment systems. This research paper examines the complex nature of online payment fraud, exploring its different types such as identity theft, account takeover, and card-not-present fraud. Based on a review of existing studies and real-world cases, the paper investigates the core mechanisms behind such fraudulent activities and highlights critical weaknesses in current payment infrastructures. It further discusses the role of modern technology in detecting and preventing fraud, emphasizing the significance of machine learning models, biometric verification, and anomaly detection methods. Additionally, the paper reviews the regulatory framework related to online payment security, evaluating how effective existing rules and standards are in reducing fraudulent practices. It also considers the difficulties encountered by law enforcement agencies and financial institutions in tracking and prosecuting cybercriminals. In conclusion, this study suggests a comprehensive strategy to tackle online payment fraud, stressing the importance of collaboration among stakeholders, the integration of advanced technologies, and the enforcement of strong regulatory measures. Addressing these aspects can significantly improve the safety of online transactions and strengthen trust in the digital economy.