DATA-DRIVEN VALUE-AT-RISK MODELING FOR ROBUST FINANCIAL FRAUD PREDICTION USING MACHINE LEARNING

Abstract

The rapid digitization of financial transactions has led to an exponential rise in fraudulent activities, posing significant challenges for institutions attempting to detect and prevent financial fraud effectively. Traditional statistical methods often struggle to maintain accuracy in skewed or imbalanced datasets, where legitimate transactions far outnumber fraudulent ones. To address this limitation, the proposed study introduces a data-driven Value-at-Risk (VaR) modeling framework integrated with machine learning algorithms for robust financial fraud prediction. The approach utilizes VaR as a quantitative risk metric to assess the potential financial impact of fraudulent events and combines it with advanced classification techniques such as ensemble learning and cost-sensitive modeling to enhance detection sensitivity in rare-event conditions. By incorporating feature importance ranking, resampling strategies, and anomaly detection mechanisms, the model effectively minimizes bias and improves prediction stability. Experimental analysis demonstrates that the integration of VaR with machine learning not only enhances the interpretability of risk-based decisions but also increases the detection rate of high-risk transactions without inflating false positives. This hybrid framework provides a comprehensive, adaptive, and explainable solution for financial institutions, ensuring more reliable fraud prevention in real-world, imbalanced financial data environments.