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

Real-time detection of fraud is critical to protect online financial transactions from evolving complex threats. In this paper, a comparative analysis of models based on machine learning (ML) aimed at detecting fraud as it happens and reducing associated financial losses and reputation risks is demonstrated. Two of the large models were compared using their respective accuracy, precision, recall, and F1-score metrics shown in the figure below. The first model (blue bars) is doing fairly well (e.g., approximately 88 % accuracy, 90 % precision, 92 % recall, and 88 % F1-score), indicating good detection but room for improvement in the balance between false positives and false negatives. The second model (orange bars) provides better metrics with approximately 94-95% accuracy, 93% precision, 95-96% recall, and 92-93% F1-score, indicating a good ability to distinguish valid transactions from fraudulent transactions with minimal misclassification. Using live data streams and continuous retraining of such models, the timeliness and accuracy of the etection. The whole study shows the capability of sophisticated ML methods in the development of a continuously updating and large-scale fraud detection system to build secure online money environments.