Fraud and failure-scale imbalance, resampling and class weights, why accuracy is meaningless here, and evaluating at the operating point you will run.
3 questions in Machine Learning.
Work backwards from the review capacity. Fix the alert budget the team can clear, evaluate precision and recall at exactly that budget on data at the real prevalence, define the label and its arrival delay before modelling, and never report accuracy, which a do-nothing model scores 99.95% on.
Choose a classifier decision threshold from calibrated probability, false-positive cost, false-negative cost and operational capacity. The threshold changes when costs, prevalence, calibration or alert budget changes. It also connects cost sensitive learning to the point an interviewer is testing.
Class weights, oversampling, undersampling and SMOTE are ways to train on imbalanced data, but none creates new signal. They change loss, sample frequency or geometry, so validation, calibration and threshold choice must be handled carefully. Use this class imbalance answer to show the decision, trade-off, and evidence rather than a memorised definition.