Model Risk Directory emblemModel Risk Directory
Data & PerformanceInput driftPopulation drift

Data drift

A change in the distribution or characteristics of model inputs over time. Drift does not automatically mean a model has failed, but it can trigger investigation, performance testing, recalibration, restrictions, or redevelopment.

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

How the term is used in model risk management

A change in the distribution or characteristics of model inputs over time. Drift does not automatically mean a model has failed, but it can trigger investigation, performance testing, recalibration, restrictions, or redevelopment. 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

Ongoing monitoringPopulation stability indexModel performance

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 SoftwareModel risk management software centralizes a bank's model inventory, tracks validation status and lifecycle events, and automates governance workflows like tiering, revalidation scheduling, and audit-trail documentation. It replaces manual spreadsheet tracking, which becomes unmanageable once an institution has more than a handful of tiered models.FrameworkOSFI E-23OSFI E-23 is Canada's enterprise-wide model risk management guideline for federally regulated financial institutions. The final 2025 guideline takes effect on 1 May 2027 and applies a risk-based approach across model identification, inventory, risk ratings, lifecycle governance, independent review, monitoring, third-party models, and decommissioning.FrameworkMAS AI MRMThe MAS AI MRM paper reports good practices observed in a thematic review of selected banks. It covers AI governance, inventories, materiality assessment, data, development, independent validation, deployment, monitoring, change, generative-AI risks, and third-party models. It is supervisory guidance and observed practice, not a general statutory MRM rule.