Artificial intelligence (AI) is significantly changing the landscape of banking and financial markets, improving predictive precision, operational productivity, and decision-making speed. However, underlying these improvements is an underexplored structural vulnerability: the risk of systemic AI convergence. As financial institutions become more dependent on similar foundational models, cloud infrastructure, data suppliers, and AI middleware platforms, diverse firms may begin to interpret and respond to market signals in similar ways. This paper presents the notion of model monoculture risk—a systemic fragility that arises when model similarities, market synchronisation, and infrastructure concentration align. Rather than focusing solely on traditional firm-level model risk management, this paper introduces the M³ Framework (Model–Market–Middleware) to clarify how shared AI architectures can transform localised optimisations into widespread amplification. The Model layer encapsulates the convergence of foundational architectures and training interdependencies; the Market layer reflects synchronised adjustments in portfolios, credit, and liquidity; and the Middleware layer emphasises the concentration of cloud services and vendors that quickens the spread. Collectively, these layers create multiplied exposure to correlated failures. To put this concept into practice, the paper proposes a qualitative Model Monoculture Risk Index (MMRI) to evaluate cross-layer alignment and the erosion of diversity within AI-driven financial systems. By redefining AI governance as a challenge of structural diversification rather than just a validation task, this contribution highlights cognitive diversity as an essential element of financial stability in the era of AI.