How do you handle concept drift in credit scoring models?
Hey everyone, I'm working on a credit scoring model and we're seeing some performance degradation over the past few quarters. I suspect it's due to concept drift driven by macroeconomic changes. What are your go-to strategies for detecting and mitigating this? Any specific monitoring metrics or retraining triggers you'd recommend?
Great question, Alice. We use Population Stability Index (PSI) and Characteristic Stability Index (CSI) on all input variables and the model score. If PSI exceeds 0.25, it triggers an alert for investigation. For retraining, we have a challenger model running in parallel on recent data. If it consistently outperforms the champion model, we promote it.
Adding to Bob's point, adaptive windowing (ADWIN) is another great technique for drift detection. It's more dynamic than fixed retraining schedules. Also, consider using models that are inherently more robust to drift, like online learning models, if your infrastructure supports it.