Definition · AI in finance
Model drift
Model drift is the decline in model performance that happens when real-world data changes from the data patterns the model was trained or evaluated on. For model drift, the useful boundary is the data, tools, approvals, human review, evaluation standard, and decision the system may influence.
Also known as concept drift, data drift, AI drift
Why it matters
Understanding model drift matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Because Pluvo computes figures deterministically against connected source data, the numbers don't drift as models change — only the narration does.
In practice
Governance example
Teams use model drift when they evaluate whether an AI-assisted analysis can be trusted. The useful test is whether the output is tied to approved data, repeatable logic, human review, and an audit trail.
Pluvo example
Because Pluvo computes figures deterministically against connected source data, the numbers don't drift as models change — only the narration does.
In practice, teams should define model drift with a clear source, owner, time period, and decision before they use it in reporting, planning, or operating reviews.
Understanding model drift matters because AI-assisted finance work can sound confident even when data, assumptions, or compute paths are wrong. A useful definition keeps the output grounded, reviewable, and accountable. Because Pluvo computes figures deterministically against connected source data, the numbers don't drift as models change — only the narration does.
A strong workflow for model drift separates the definition from the action: first agree what the term means, then decide how it is measured, when it changes, and who is accountable for the next step.
Because Pluvo computes figures deterministically against connected source data, the numbers don't drift as models change — only the narration does.
FAQ
What is model drift?
Model drift is the decline in model performance that happens when real-world data changes from the data patterns the model was trained or evaluated on. For model drift, the useful boundary is the data, tools, approvals, human review, evaluation standard, and decision the system may influence.
What is the difference between data drift and concept drift?
The boundary for model drift differs from related terms by scope, source data, time period, and decision use. In this glossary, it covers what model drift is — degradation of model performance over time as data distributions shift — and how it is detected and managed, so teams should compare those boundaries before using it in reporting or planning.