Artificial Intelligence for Financial System Stability: A Data-Driven Modeling Approach
Keywords:
Financial Stability, Artificial Intelligence, Data-Driven Modeling, Systemic Risk, Algorithmic Governance, Socio-Technical Infrastructure, Sustainability.Abstract
The transition toward an artificial intelligence-driven financial ecosystem necessitates a fundamental reevaluation of systemic stability and risk management. Traditional econometric frameworks, primarily reliant on linear assumptions and historical stationarity, are increasingly inadequate for navigating the high-dimensional, non-linear dependencies of modern global markets. This paper investigates the implementation of data-driven modeling as a foundational infrastructure for maintaining financial system stability. We conduct a system-level analysis of artificial intelligence architectures, focusing on the structural trade-offs between predictive precision and model interpretability. The discussion extends beyond mathematical optimization to address the socio-technical dimensions of deployment, including the physical infrastructure required for real-time monitoring, the governance challenges of autonomous financial agents, and the systemic risks of algorithmic convergence. Furthermore, this research examines the ethical imperatives of fairness and sustainability, arguing that the energy-intensive nature of large-scale financial AI must be balanced with its potential to enhance market resilience. By synthesizing perspectives from systems engineering, information theory, and financial policy, this paper provides a comprehensive roadmap for integrating relational intelligence into the global financial stability architecture. We emphasize that the stability of the future financial system depends not only on the accuracy of its models but on the robustness of the institutional and technical frameworks that govern their interaction.
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