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Validation Techniques

Outcomes analysis

Comparing a model's actual outputs and predictions against real subsequent outcomes to check ongoing accuracy and performance. Outcomes analysis, closely related to backtesting, is a core ongoing-monitoring component of independent model validation.

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

How the term is used in model risk management

Comparing a model's actual outputs and predictions against real subsequent outcomes to check ongoing accuracy and performance. Outcomes analysis, closely related to backtesting, is a core ongoing-monitoring component of independent model validation. 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

BacktestingModel validationOngoing monitoring

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.GuideTop model risk management firmsThe right model risk management firm depends on the models in scope, governing framework, independence requirements, technical methods, jurisdiction, and delivery capacity. This guide groups the directory's advisory firms alphabetically and provides a verification checklist. It does not assign rankings, scores, or endorsements without comparable evidence.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.