You have deployed a binary classification model into a live advisor workflow. The challenge is to monitor whether performance holds up after launch and to improve both predictive quality and operational efficiency as data, user behavior, and business constraints change over time.
Core model metrics: accuracy, precision, recall, F1, AUCCalibration and predicted versus actual positive rateFeature drift and score distribution driftOperational metrics such as queue size and advisor capacity usageA daily batch model scores clients for outreach prioritization in Ameriprise Advisor Center. Scores above a threshold are routed to advisors, so both model quality and threshold choice directly affect business value.