Developing Machine Learning Models for Real-Time Fraud Detection in Online Transactions

This research discusses the application of machine learning (ML) models in real-time fraud detection in online transactions, replacing the traditional rule-based system with the changing nature of fraud techniques such as account takeover, phishing, and card-not-present fraud. Leveraging supervised (e.g., Support Vector Machines, Neural Networks, Random Forest) and unsupervised (e.g., clustering, anomaly detection) techniques, the study employs public and private transaction data sets to evaluate model performance in terms of key metrics including precision, recall, F1-score, and AUC-ROC. The study highlights the essentiality of feature engineering—integrating domain knowledge features such as device fingerprinting and IP geolocation—and examines real-time deployment through streaming data pipelines to achieve low-latency decision making. A study of hybrid models that integrate supervised and unsupervised methods is also proposed to address class imbalance problems. Experimental results show that ensemble and deep learning methods substantially minimize false positives while achieving high detection rates, thus presenting an adaptive and scalable solution for addressing fraud risks in online transactions.