A Study of Fraud Detection in E-Commerce: An Application of Machine Learning

The given research paper is aimed at investigating how machine learning can be applied to detect and prevent fraud in e-commerce transactions. As online commerce grows at a tremendous pace, the frauds have also evolved at an equally high rate, becoming a serious source of risk to the business as well as consumers in terms of financial as well as reputational risks. However, conventional rule-based fraud identification approaches sometimes fail because of their low flexibility and false-positive results. The research examines the machine learning methods, such as supervised, unsupervised, and deep learning models and their efficiency in detecting frauds of different types, like credit card fraud, account takeover, and payment fraud. By closely examining the aspect of feature engineering techniques, imbalanced dataset management, and practical case practices of the major platforms (such as Amazon and PayPal), the study sheds light on the advantages and limitations of implementing the machine learning-powered fraud detection models into the ever-changing e-commerce landscapes. These results highlight the necessity of the constant model improvement, ethical aspects, and alignment with regulations in fraud detection to increase its accuracy and prevent the decline of consumer confidence.