Decision-Layer Architecture for AI Governance in Financial Systems: A Formal Framework for Risk, Uncertainty, and Authority-Aware Decision Design

This paper introduces a formal decision-layer architecture for AI governance in financial systems. The framework restructures traditional machine learning pipelines by separating prediction, decision-making, and governance into distinct system layers. The proposed model defines a unified decision function that integrates risk dynamics, temporal evolution, uncertainty modeling, and authority constraints as first-class system components. Unlike conventional AI approaches that focus primarily on predictive accuracy, this work emphasizes deterministic decision traceability, regulatory compliance, and system-level auditability. The architecture is designed for regulated financial environments where decisions must be reproducible, explainable, and governed under explicit authority constraints. A key contribution of this work is the formalization of "authority" as a constraint operator rather than a model feature, enabling strict separation between model inference and governance enforcement. The framework is applicable to credit risk systems, liquidity management, financial infrastructure design, and AI governance layers in institutional environments. It aligns with emerging standards in open financial ecosystems and contributes to the development of transparent and interoperable AI systems in finance.