AI Tiering

AI tiering refers to the practice of classifying AI systems into risk levels, such as high, limited, or minimal, and applying governance requirements in proportion to each level. The EU AI Act is the most prominent example, setting obligations for documentation, human oversight, and testing that scale with how much harm a system could cause. For compliance and risk teams, a tiering exercise is usually the first step in building an AI inventory, since it determines which systems need formal review, legal sign-off, or outright restriction.

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ResearchGlobal2026-11-01

Financial Model Risk Frameworks Miss Most AI Systems, Research Finds

A peer-reviewed study in the Journal of Banking Regulation finds that financial institutions' model risk management programs contain systematic gaps when applied to AI systems. The research identifies three core weaknesses. These are: model definitions that exclude many AI tools, tiering criteria that don't reflect AI-specific failure modes, and vendor governance protocols that haven't kept pace with third-party AI procurement. The authors propose targeted fixes for each gap, illustrated through market risk modeling, enterprise chatbots, and AI use-case discovery.

model risk management financial services vendor risk AI tiering model inventory regulatory examination OCC bank supervision