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Core Concepts

Model uncertainty

Uncertainty arising from limited data, assumptions, estimation, parameter choices, structural simplification, future conditions, and model selection. It can be assessed, communicated, monitored, and mitigated but not always eliminated.

Last updated: Last reviewed by: Model Risk Directory editorial team

How the term is used in model risk management

Uncertainty arising from limited data, assumptions, estimation, parameter choices, structural simplification, future conditions, and model selection. It can be assessed, communicated, monitored, and mitigated but not always eliminated. The exact implementation varies by institution, model type, risk rating, and governing framework. Use the linked regulatory pages and buyer guides below for scope-specific requirements.

Related glossary terms

Model limitations and assumptionsSensitivity analysisModel risk

Guides and frameworks that use this term

GuideModel Risk ValidationModel risk validation is the independent testing of a specific model's conceptual soundness, ongoing performance, and outcomes, performed by staff or a third-party firm genuinely separate from that model's development. It is the 'effective challenge' function required under SR 26-2 and SR 11-7, distinct from broader model risk management program design.GuideModel Risk ManagementModel risk management (MRM) is the discipline of identifying, measuring, and controlling the risk that a bank's quantitative models produce incorrect or misused results. An MRM advisory firm helps build or run that discipline: model inventory, tiering, governance, and independent validation, rooted in Federal Reserve SR 26-2 (formerly SR 11-7) and OCC guidance.FrameworkSR 26-2SR 26-2, issued jointly by the Federal Reserve, OCC, and FDIC on April 17, 2026, is the current US interagency guidance on model risk management. It supersedes SR 11-7 (2011) and SR 21-8 (2021), preserving core disciplines like effective challenge and independent validation while shifting to a risk-based approach tailored to an institution's model risk profile, size, and complexity.FrameworkPRA SS1/23PRA SS1/23 is the Bank of England Prudential Regulation Authority's supervisory statement setting out five model risk management principles for UK banks with internal model approval for credit, market, or counterparty credit risk. It took effect May 17, 2024 and explicitly extends to risks from AI and machine learning modeling techniques.