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Data & Performance

Data quality

The fitness of data for a model's intended use, commonly assessed through completeness, accuracy, timeliness, consistency, representativeness, and controlled transformation. Weak data can invalidate otherwise sound methods.

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

How the term is used in model risk management

The fitness of data for a model's intended use, commonly assessed through completeness, accuracy, timeliness, consistency, representativeness, and controlled transformation. Weak data can invalidate otherwise sound methods. 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

Data lineageConceptual soundnessModel limitations and assumptions

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 Management FrameworkA model risk management framework is the documented set of policies, roles, and processes an institution uses to identify, tier, govern, and validate its models. Under SR 26-2, a sound framework covers model development and use, independent validation, and governance and controls, scaled to the institution's size and model risk profile rather than applied uniformly.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.FrameworkHKMA CA-G-4HKMA CA-G-4 sets validation expectations for internal-ratings-based credit-risk systems used by authorized institutions. The current 2025 version covers governance, data, model design, quantitative and qualitative validation, use tests, stress testing, overrides, validation frequency, independence, findings, and supervisory review. It is model-specific rather than an enterprise-wide MRM standard.FrameworkEIOPA Internal Model GuidelinesEIOPA's Guidelines on the use of internal models support consistent Solvency II supervision of insurers using full or partial internal models. They address application and approval, model changes, the use test, governance, documentation, data, validation, expert judgment, external models and data, group models, and supervisory communication.InsightAI model risk under SR 26-2: what is in scope and what is notSR 26-2 applies its model risk principles to traditional statistical, quantitative, and non-generative, non-agentic AI models. Generative and agentic AI are outside this guidance's model scope, but they are not outside bank risk management.