concept drift

Models decay silently as the world shifts, and 'monitor for drift' is too vague. What counts is the actual statistical methods and separating data drift you can detect without labels from concept drift you often cannot.

Three drifts get casually lumped together, but they differ in detectability and in their fixes. What interviewers want is knowing which you can catch without labels and which one genuinely demands a new model.

Fraud is well under 1% of transactions, the labels land weeks late as chargebacks, and the fraudsters deliberately adapt to your model. Optimizing accuracy hands you a model that approves everything. The interview is about dollars-at-risk thresholding, label delay, and a review queue with a fixed headcount.