Leveraging Machine Learning for Fraud Prevention in Digital Payment Systems

Abstract

In the digital era, the rise of online financial transactions has made digital payment systems a major target for fraud. Traditional methods of fraud detection are becoming inadequate due to the sophisticated nature of fraud schemes. This article explores the application of Machine Learning (ML) algorithms in the detection and prevention of fraud in digital payment systems. By using historical transaction data, ML models can identify patterns and anomalies that indicate potential fraudulent activity. This article examines various ML techniques, such as decision trees, neural networks, and anomaly detection models, that are being implemented to enhance the security of digital payment systems. We will also discuss the challenges and future directions in deploying ML-based solutions for fraud prevention.