This work explores a governance-first approach for AI-supported decision systems operating in high-impact financial environments. The proposed framework treats decision-making as an end-to-end lifecycle involving data reliability, evidence management, risk assessment, governance constraints, decision generation, replay verification and audit preservation. The validation approach evaluates architectural behavior under controlled synthetic scenarios, including evidence failures, risk threshold conditions, authorization controls and reproducibility checks. The research emphasizes reliability, accountability and controlled deployment principles for AI-assisted financial decision infrastructures.

This paper presents AICOS, a governance-oriented decision infrastructure architecture designed for reliable AI-assisted financial decision processes. The study focuses on evidence validation, risk evaluation, governance controls, replay-based verification, explainability and auditability mechanisms for critical decision environments. AICOS is designed to support human-final decision processes by improving transparency, traceability and operational control rather than replacing human judgment. The paper introduces the conceptual architecture, mathematical decision framework and validation methodology for controlled AI-supported decision systems.