Model Monoculture Risk: Systemic AI Convergence in Banking and Financial Markets

Abstract

As financial institutions become increasingly reliant on similar foundational AI models, cloud infrastructure, data providers, and AI middleware platforms, diverse firms may begin to interpret and respond to market signals in increasingly similar ways. This paper examines model monoculture risk as a potential source of system-level exposure in banking and financial markets. Drawing on a structured, purposive conceptual review of the academic and regulatory literature, it develops the M3 Framework—Model, Market, and Middleware—to integrate three interacting mechanisms: Model Similarity, Market Synchronisation, and Middleware Concentration. Rather than treating these as a fixed causal sequence, the framework identifies their independent and cross-layer effects and distinguishes AI-mediated synchronisation from correlation arising from common exposures, conventional herding, and shared macroeconomic shocks. The paper further proposes the Model Monoculture Risk Index (MMRI) as a prototype index architecture and conceptual supervisory screening framework for characterising configurations of AI-related convergence, rather than as a calibrated quantitative measure of realised systemic risk. An illustrative application demonstrates how exposure classifications vary across alternative M3 configurations and attribution assumptions. The paper argues that governance should prioritise cross-institutional monitoring, dependency mapping, meaningful substitutability, and targeted stress testing rather than diversification by default. The framework provides a structured basis for future empirical research on AI-driven convergence and financial stability.