Fixed Retraining Schedules Fail; Machine Learning Models Need Shock Detection Instead

Researchers analyzing 555,000 fraud transactions found calendar-based model retraining schedules ineffective, with performance metrics worse than baseline. They propose shock-detection methodology to trigger retraining when data patterns suddenly shift, offering a practical alternative to fixed maintenance cycles in production systems.