Leveraging Machine Learning Algorithms for Real-Time Fraud Detection in Digital Payment Systems

Abstract

Digital payment systems have become ubiquitous in modern financial transactions, offering convenience and speed to users worldwide. However, this rapid growth has also led to an increase in fraudulent activities, posing significant challenges to financial institutions and consumers alike. This research paper explores the application of machine learning algorithms for real-time fraud detection in digital payment systems. We investigate various supervised and unsupervised learning techniques, including logistic regression, decision trees, random forests, support vector machines, and deep learning models. The study analyzes large-scale transaction data to identify patterns and anomalies indicative of fraudulent behavior. We propose a novel ensemble approach that combines multiple algorithms to enhance detection accuracy while minimizing false positives. Our findings demonstrate that machine learning-based fraud detection systems can significantly improve the security of digital payment platforms, potentially saving billions of dollars annually for the financial industry.