Strategic Integration of Machine Learning for Fraud Detection in E-Commerce Transactions

Abstract

The rise in internet users has led to an increase in online payments, but this also comes with a surge in online fraud. To combat this, e-commerce firms must adopt device intelligence for fraud detection. Machine learning (ML) is crucial for analyzing large datasets to identify suspicious patterns. This study explores the effective application of ML in detecting fraudulent activities, focusing on various approaches, challenges, and recommendations. It starts with an overview of the prevalence and impact of e-commerce fraud, highlighting the need for robust detection systems. Key ML techniques, including supervised, unsupervised, and semi-supervised learning, are analyzed for their strengths and weaknesses. It emphasizes the importance of continuous monitoring and model adaptation to evolving fraud tactics, advocating for dynamic updates and feedback loops to enhance detection systems. By integrating ML algorithms effectively, e-commerce businesses can improve security, safeguard revenues, and build trust with consumers and partners.