Online Payment Fraud Detection Using Machine Learning Techniques

Online payment systems have become an integral part of modern financial transactions. However, the rapid growth of digital payments has also increased the risk of fraudulent activities, leading to financial losses. Traditional rule-based fraud detection methods are inefficient in detecting complex and evolving fraud patterns. This paper proposes an online payment fraud detection system using machine learning techniques to classify transactions as fraudulent or legitimate. The system involves data preprocessing, feature selection, and classification using machine learning algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbors. Experimental results indicate that machine learning-based approaches improve detection accuracy and reduce false positives. The proposed system provides an efficient and scalable solution for secure online payment transactions.