A bank runs a fraud-detection model whose accuracy decays as fraudsters change tactics, but fraud patterns shift unpredictably rather than on a fixed weekly or monthly cadence. The team wants retraining to fire only when the model is genuinely degrading, to avoid wasting compute on needless runs. Which retraining policy best fits this situation?
- ASchedule a retraining run every night so the model is always built on the freshest labelled transactions available.
- BRetrain manually whenever an analyst happens to notice that several fraud cases were missed during a review.
- CTrigger retraining when monitored prediction performance or input drift crosses a defined threshold, so runs happen only on genuine degradation. Correct
- DRetrain on a fixed monthly calendar schedule because most regulated banking processes operate on monthly reporting cycles.
Why A is wrong: Nightly scheduling is tempting because fresh data sounds safer, but it ignores the stated goal of avoiding needless runs and retrains even when performance is stable, wasting compute.
Why B is wrong: Manual ad hoc retraining feels responsive, but it relies on human spotting, reacts slowly and inconsistently, and is not a defined automated policy tied to measured degradation.
Why C is correct: A performance- or drift-threshold trigger fires exactly when the model degrades, which matches unpredictable concept drift and the goal of retraining only when accuracy actually falls.
Why D is wrong: A monthly calendar feels orderly and aligns with reporting, but unpredictable drift can degrade the model mid-month, leaving it stale until the next fixed run arrives.