AI-Driven Predictive Analytics Framework for Financial Fraud Detection in Digital Transactions

The rapid growth of digital financial systems has significantly increased the risk and complexity of fraudulent activities, necessitating more intelligent and adaptive detection mechanisms. This paper proposes an AI-driven predictive analytics framework designed to identify and prevent financial fraud in real time. The system integrates data preprocessing, feature engineering, and machine learning algorithms to analyze transactional patterns and detect anomalies indicative of fraudulent behavior. Various classification models are evaluated to determine their effectiveness in terms of accuracy, precision, and recall on a synthetic benchmark, and the reported metrics are interpreted with explicit attention to the low fraud prevalence, which bounds attainable precision. The framework also incorporates continuous learning capabilities to adapt to evolving fraud techniques and emerging threats. Experimental results on a six-million-transaction synthetic dataset show that the proposed ensemble attains a recall of 0.9184 at a false-positive rate of 0.0186, reducing the false-positive rate by a factor of approximately 2.6 relative to a rule-based baseline while improving recall from 0.7120 to 0.9184; precision at this operating point is 0.3312, reflecting the intrinsic difficulty of the 0.5%-prevalence regime. The proposed approach offers a scalable and efficient solution for strengthening security and trust in modern financial ecosystems.