RTP-FAI: A Real-Time AI Framework for Unified Fraud and AML Intelligence in Digital Payment Systems

The rapid adoption of real-time payment systems has significantly increased the exposure of financial institutions to instant fraud and money laundering threats. Traditional rule-based monitoring systems fail to scale under high transaction velocity and lack cross-domain intelligence between fraud detection and anti–money laundering (AML) workflows. This paper presents RTP-FAI (Real-Time Payment – Financial Intelligence), an end-to-end AI-driven framework that integrates real-time transaction scoring, graph-based AML detection, and unified case management within a single operational platform. The system combines machine learning risk models, behavioral feature engineering, and transaction network analytics to deliver sub-30 ms decision latency with high precision and explainability. A functional prototype is evaluated using simulated real-time payment streams, demonstrating strong performance across fraud detection accuracy, AML typology identification, and operational efficiency. The results validate RTP-FAI as a scalable, production-ready intelligence system for modern digital financial infrastructures.