AI Governance Challenges in Financial Decision-Making Systems: Beyond Regulatory Constraints
Abstract
The rapid integration of autonomous artificial intelligence into global financial infrastructures has outpaced traditional regulatory frameworks, creating a governance vacuum that threatens systemic stability. While existing policy instruments focus primarily on external constraints—such as reporting requirements, capital adequacy, and post-hoc audits—they frequently fail to address the internal, system-level dynamics of agentic AI. This paper explores the profound governance challenges inherent in autonomous financial decision-making systems, moving beyond simple compliance to examine the structural trade-offs of algorithmic architecture. We analyze the socio-technical implications of high-frequency autonomous trading, credit scoring, and risk management through the lens of infrastructure robustness and sustainability. Central to our thesis is the argument that modern financial AI systems exhibit emergent behaviors that cannot be fully mitigated by external oversight alone. Instead, we propose a shift toward governance-by-design, where normative constraints are embedded within the architectural substrate of the model. The discussion encompasses the trade-offs between predictive accuracy and interpretability, the risks of systemic contagion in interconnected agent environments, and the ethical dimensions of algorithmic fairness in automated lending. By synthesizing perspectives from systems engineering, financial economics, and public policy, this research identifies the missing dimensions of current governance models and provides a strategic framework for ensuring the resilience of AI-driven financial ecosystems in an increasingly volatile global landscape.
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