A Framework for Agentic AI-Driven Financial Fraud Detection, Investigation, and Risk Mitigation in Real-Time Payment Ecosystems

Abstract

Instant payment rails have changed what fraud looks like. When money moves in seconds and settlement is irreversible, the old rhythm of batch review and overnight reconciliation no longer protects anyone. This manuscript sets out a framework that treats fraud defense not as a single classifier bolted onto a payment switch, but as a society of cooperating software agents that sense, reason, decide, act, and learn within the same narrow window in which a transaction clears. We organize the framework into five layers: real-time ingestion, a feature and graph store, a detection and reasoning core, an agentic orchestration tier, and a governance layer that keeps a human analyst in the loop. Each fraud alert is carried through a closed perception-action cycle in which a triage agent ranks risk, an investigator agent assembles evidence from transaction graphs and device signals, and a response agent chooses a proportionate action such as a soft hold, a step-up challenge, or release. Drawing on prior work in big-data analytics, explainable machine learning, reinforcement-learning-based defense, graph-based anomaly detection, and management information systems, we argue that the value of an agentic design lies less in any single model and more in the orchestration: the way detection, explanation, and action are stitched into one auditable loop. We illustrate the framework with a layered reference architecture, an isometric view of the processing pipeline, an empirical-style comparison of candidate detection models, and a residual-risk map across common fraud typologies. We close with a candid discussion of the limitations of autonomous action in a regulated financial setting and the governance scaffolding such a system would require before deployment.