A Theoretical Framework for AI and Financial Stability: The Tension Between Micro-Efficiency and Macro-Risk

Abstract

The widespread adoption of artificial intelligence in finance has profound implications for financial stability. This paper develops a dual analytical framework that systematically examines the underlying logic and transmission mechanisms through which AI influences financial stability, focusing on the tension between microlevel efficiency gains and macro-level risk accumulation. The findings reveal three key insights. First, AI does not eliminate financial risks but rather shifts them from traditional balance-sheet and leverage domains to realms characterized by algorithmic dependence and technological vulnerability. Homogeneity, opacity, overfitting tendencies, and technological dependency emerge as novel sources of systemic risk, with micro-level efficiency improvements often coming at the cost of heightened macro-level fragility. Second, the core transmission chain from "individual algorithmic optimization" to "systemic vulnerability" operates as follows: profit-driven financial institutions adopt similar data sources and model architectures, leading to increased model correlation and convergent decision logic at the macro level. When external shocks occur, this convergence triggers nonlinear "algorithmic resonance" and "digital herding effects," amplifying localized market disturbances into systemic crises. The theoretical framework constructed in this paper transcends traditional analytical paradigms centered on institutional balance-sheet interconnectedness, offering a novel perspective for understanding financial stability challenges in the algorithmic age and laying theoretical groundwork for developing macroprudential governance frameworks adapted to AI-driven financial ecosystems.