Machine Learning Based Fraud Risk Scoring for Financial Transactions Using a Comparative Study of Random Forest and Support Vector Regression Models

Abstract

Financial fraud poses a significant threat to global economic stability, with losses reaching trillions of dollars annually. Traditional rule-based fraud detection systems, constrained by reactive approaches and limited real-time processing capabilities, are increasingly inadequate against sophisticated modern fraud techniques. This research explores the implementation Using machine learning and artificial intelligence technology to detect financial transaction fraud in real time. The research examines key transaction parameters, including transaction size, transaction time, and fraud risk scoring algorithms, to develop robust detection algorithms. Two primary machine learning approaches are evaluated: random forest regression, which uses ensemble methods to reduce prediction variance through multiple decision trees, and support vector regression, which balances accuracy and model complexity by minimizing prediction errors. Cloud computing integration enhances AI-driven fraud detection by providing the computational scalability and real-time processing capabilities required for comprehensive financial analysis. While AI offers significant potential to improve detection accuracy and operational efficiency, challenges remain in algorithm transparency, interpretability, and data privacy compliance. The research addresses the ethical and regulatory considerations necessary for secure implementation. The results demonstrate that fraud detection systems with AI capabilities can drastically speed up response times and lower false positives, and strengthen financial security frameworks, ultimately protecting both financial institutions and consumers from fraud threats.