Abstract

The increasing volume of online transactions has heightened the risk of fraud, making real-time frauddetection crucial for safeguarding financial systems. This paper explores the development and application of machinelearning (ML) models for detecting fraudulent activities in real-time online transactions. The study investigates variousML algorithms, including supervised and unsupervised learning techniques, to identify patterns indicative of fraud. Weevaluate the performance of different models based on accuracy, precision, recall, and F1-score. The results show thatensemble methods and deep learning techniques outperform traditional approaches in terms of accuracy and detectionspeed. The study also emphasizes the importance of data preprocessing, feature selection, and real-time modeldeployment for achieving robust fraud detection systems. This research contributes to the ongoing efforts to enhanceonline transaction security and provides insights into implementing effective fraud detection mechanisms usingmachine learning