PREDICTIVE FINANCIAL FRAUD DETECTION USING RISK MODELING AND ARTIFICIAL INTELLIGENCE

Abstract

The increasing adoption of digital banking, online transactions, and electronic payment systems has significantly increased the risk of financial fraud across global financial networks. Fraudulent financial activities such as unauthorized transactions, money laundering, identity theft, and credit card fraud cause substantial economic losses and threaten the security of financial institutions and customers. Detecting fraudulent transactions is challenging due to the highly imbalanced and skewed nature of financial datasets, where fraudulent activities represent only a small portion of overall transaction records. Traditional fraud detection techniques often fail to identify hidden fraudulent patterns effectively in such complex data environments. This paper presents a predictive financial fraud detection framework using risk modeling and artificial intelligence to improve fraud identification accuracy in skewed financial data. The proposed system integrates Value at Risk (VaR) analysis with machine learning techniques to evaluate transaction risk levels and detect suspicious financial behavior. Advanced preprocessing and data balancing methods are applied to handle class imbalance and improve model robustness. Multiple machine learning and deep learning algorithms are utilized to analyze transaction patterns, customer behavior, and risk indicators for accurate fraud classification. Experimental results demonstrate that the proposed framework achieves high detection accuracy, reduced false positive rates, and improved scalability compared to conventional statistical and rule-based methods. The integration of VaR with AI-driven predictive analytics enhances financial risk assessment and enables early detection of fraudulent transactions in real time. Overall, the proposed model provides an intelligent, scalable, and reliable solution for modern financial fraud detection and risk management applications.