What Is Model Drift?
Model drift is the degradation of an AI model's performance over time as the world changes and real-world data diverges from the training data. It is inevitable, and without monitoring, invisible until it causes harm.
Model Drift, the degradation of an AI model's performance over time as the real-world data distribution diverges from the distribution on which the model was trained.
Model drift takes two forms. Data drift occurs when the statistical properties of inputs change (e.g., a fraud model trained pre-pandemic facing post-pandemic transaction patterns). Concept drift occurs when the relationship between inputs and the target itself changes (e.g., what counts as creditworthy changes with macroeconomic conditions). Both require ongoing monitoring and a retraining cadence, APRA's 30 April 2026 letter singled this out, warning that point-in-time, sample-based assurance is not suited to probabilistic models that learn and drift over time.
Source: APRA 30 April 2026 industry letter; Federal Reserve SR 11-7
Types of model drift
EU AI Act Article 72 requires post-market monitoring for high-risk AI, monitoring for model drift is a compliance obligation, not merely a best practice.