Open Internet by MindsNet

Model Drift in Real-Time Fraud Detection

A deployed fraud model's accuracy dropped significantly due to distribution shift, and common solutions like retraining or rolling back were not feasible. The model's accuracy decreased from ~93% to ~45%, causing a substantial loss in performance. This situation highlights the need for a more adaptive approach to handling model drift in real-time.

Computing & Technology, Computer Science, Machine Learning