Natural-Language Finance Agents: Architectures, Governance, and Human–AI Collaboration for Enterprise-Scale Analytics

Abstract

Enterprise financial analytics faces critical accessibility barriers where complex data systems require specialized technical expertise, creating bottlenecks for non-technical stakeholders seeking analytical insights. This article presents a comprehensive framework for natural-language finance agents that addresses architectural, governance, and collaboration requirements for production deployment in regulated financial environments. Our framework proposes constrained generation mechanisms with financial domain-specific semantic modeling, policy-binding governance structures with automated compliance validation, and structured human-AI collaboration patterns with escalation protocols. We demonstrate framework applicability through illustrative use cases spanning financial forecasting, variance analysis, and executive reporting while maintaining regulatory compliance and audit trail requirements. The proposed architecture defines evaluation frameworks for analytical accessibility while preserving accuracy standards essential for enterprise financial decision-making.