Next-generation Fraud Detection in U.S Financial Systems: Evaluating Hybrid AI and Rule-based Models for Real-time Threat Mitigation

This paper explores next-generation fraud detection within American financial systems by assessing hybrid architectures that integrate rule-based logic and the artificial intelligence (AI) models to reduce threats in real-time. Inspired by rising loss rates and the rising complexity of fraud vectors (such as synthetic identities and deepfakes), the analysis builds an assessment framework conceptual framework that trades off detection performance, cost of operation, regulatory disclosure, and resilience to adversaries. Based on the current empirical literature, guidance by regulators, and industry-specific reports, the paper presents (1) a synthesis of strengths and weaknesses of hybrid approaches; (2) suggests quantifiable evaluation criteria and economic tradeoffs to institutions; and (3) a tested research agenda (data needs, validation, human-in-the-loop oversight, and policy recommendations). The essence of the argument is that properly designed hybrid frameworks, when structured on the back of robust model risk management and a sustained adversarial testing regime, can significantly decrease the losses to fraud and comply with U.S. regulatory requirements and retain customer confidence.