Fraud Detection in E-commerce Transactions: A Machine Learning Perspective

As the number of online purchases increases at an alarming rate, fraudulent activities have surfaced as a major concern for retailers and consumers alike. Fraudsters frequently face setbacks when attempting to bypass automated rule-based systems that are specifically engineered to identify fraudulent activities. The objective of this research is to investigate methods by which fraudulent online transactions can be detected using machine learning algorithms. To detect fraudulent activities, an analysis is performed on various machine learning methodologies, including supervised, unsupervised, and semi-supervised learning. This research primarily examines the evaluation criteria, feature engineering, and model selection that are distinct to the domain of electronic commerce. Furthermore, obstacles that manifest during the detection of fraudulent activities in electronic commerce must be confronted, including the dissemination of deceptive ideas and manipulated data. By subjecting machine learning models to rigorous testing on real-world datasets, we demonstrate their efficacy in detecting fraudulent activities and underscore their capacity to dynamically adjust to evolving fraud patterns. In conclusion, we discuss the practical obstacles and possible avenues for future research concerning machine learning-based e-commerce fraud detection systems.