You inherited a binary fraud model with extreme class imbalance (positives ≈2%). Current performance on a temporally separated validation set: AUC=0.61, precision@recall=0.90 is only 0.05. You have one day to meaningfully improve recall at fixed review capacity. 1) Describe how you would quickly diagnose underfitting vs. overfitting (learning curves, calibration plots, PR vs. ROC trade-offs, leakage checks). 2) Propose three targeted interventions that can be implemented in a day (e.g., class-weighted loss, monotonic gradient boosting with categorical encoders, threshold moving with cost-sensitive utility) and justify why each should help. 3) Show how you would choose a decision threshold that maximizes expected utility given: FP cost=$2, FN cost=$50, review capacity=0.5% of traffic; write the utility formula and outline the validation-time procedure. 4) List the minimal logging/monitoring you’d add at deployment to detect drift and data quality issues within a week.