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Strategic Integration of Machine Learning for Fraud Detection in E-Commerce Transactions
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Author(s): P. Vijayalakshmi (Knowledge Institute of Technology, Salem, India), K. Subashini (Tagore Engineering College, Chennai, India), B. Selvalakshmi (Tagore Engineering College, Chennai, India), G. Sudhakar (Sri Sai Ranganathan Engineering College, Coimbatore, India), Anand Anbalagan (Technical Vocational Training Institute, Addis Ababa, Ethiopia), N. Bharathiraja (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)and Gaganpreet Kaur (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)
Copyright: 2025
Pages: 22
Source title:
Strategic Innovations of AI and ML for E-Commerce Data Security
Source Author(s)/Editor(s): Gaganpreet Kaur (Chitkara University, India), Jatin Arora (Chitkara University, India), Vishal Jain (Sharda University, Greater Noida, India)and Asadullah Shaikh (Najran University, Saudi Arabia)
DOI: 10.4018/979-8-3693-5718-7.ch006
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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.
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