From Answers to Integrity: Designing Financial AI That Refuses to Mislead

This work introduces a governance-first framework for AI-assisted financial reasoning, addressing a critical failure mode in contemporary AI systems: epistemic overreach. As large language models increasingly participate in financial analysis, advisory-adjacent workflows, and decision-support environments, the dominant risk is no longer factual inaccuracy, but the generation of coherent, confident outputs beyond what available evidence, uncertainty, or regime visibility can justify. The paper formalizes an Epistemic Governance Layer built on three core principles: Cognitive Risk Friction Minimum Analytical Evidence Honesty as a First-Class Output (including refusal and controlled silence) Together, these mechanisms operate before answer generation, enforcing epistemic discipline at the point of reasoning rather than relying on post-hoc disclaimers or output moderation. Through conceptual explanation, comparative analysis, and practical examples, the work demonstrates how governance-first systems differ structurally from conventional financial AI assistants that optimize for responsiveness, fluency, and perceived usefulness. The framework is designed to be applicable in regulated and advisory-adjacent environments, including brokers, banks, asset managers, and research platforms, where controlling false authority, suitability leakage, and action-enabling language is critical. Rather than helping users decide faster, the approach prioritizes decision integrity — ensuring that AI systems know when not to answer, when to downgrade analysis, and when silence is the most responsible output.