Abstract
Financial institutions face a critical paradox: while Generative Large Language Models (GLLMs) unlock transformative possibilities for forecasting and decision-making, their deployment through cloud-based APIs creates insurmountable barriers for institutions bound by data sovereignty requirements. This structural exclusion disproportionately affects small and medium-sized enterprises (SMEs) who lack both the capital for premium cloud services and the regulatory latitude to externalize sensitive data. We address this inequality by developing a framework for Cognitive Sovereignty—the institutional capacity to deploy advanced reasoning capabilities locally, thereby expanding access to frontier AI across the competitive landscape.
Our innovation lies in reframing AI infrastructure as an inclusive optimization problem rather than a binary "cloud versus legacy" choice. Drawing on Dual Process Theory, we differentiate between efficient "System 1" (Instruct) architectures suitable for routine tasks and sophisticated "System 2" (Think) architectures that enable deeper structural reasoning. Using expert-validated ground truth from Chartered Financial Analysts analyzing crude oil markets, we demonstrate that judicious model selection opens new possibilities: institutions can achieve human-expert-level performance while maintaining full data control and regulatory compliance.
We introduce the concept of Time-for-Memory Arbitrage—a strategic innovation that allows resource-constrained institutions to trade computational latency for capital efficiency, effectively lowering the barrier to entry for sovereign AI adoption. Our risk-adjusted decision framework reveals that the optimal on-premises solution costs 95% less than premium GPU workstations while maintaining 60%+ accuracy on complex supply-demand analysis. This finding fundamentally expands the possibility space for AI adoption, proving that data sovereignty and analytical sophistication are not mutually exclusive. By providing a transparent, auditable methodology that satisfies regulatory requirements while democratizing access to advanced capabilities, this research charts a pathway toward a more inclusive and innovative financial ecosystem where competitive advantage derives from strategic resource allocation rather than scale alone.