I started talking about retraining and the interviewer kind of let me run with it before gently steering toward the covariate shift angle, which I'd glossed over.
Acknowledge the model's limited training domain and propose a systematic approach to extend it, starting with data exploration to understand differences in the new population. Then outline methods to adapt the model, such as reweighting, transfer learning, or building a new model, while emphasizing validation and monitoring for performance and fairness.
Pro tip: Highlight the importance of reject inference and the potential for selection bias in the historical data, showing awareness of the fundamental challenge in credit risk modeling.
Examine historical data for applicants below 650, even if they were rejected, to understand feature distributions and potential outcomes. Identify any existing data on their repayment behavior, perhaps from other sources or pilot programs.
Test the existing model's performance on the new population using available data, checking for degradation. Analyze feature importance and whether relationships hold for lower-score applicants.
Consider options like reweighting the training data to include more low-score examples, using transfer learning to fine-tune the model, or building a separate model for the new segment. Evaluate trade-offs in complexity, data needs, and interpretability.
Use techniques like cross-validation, out-of-time testing, and reject inference to validate the adapted model. Set up monitoring for performance drift and fairness metrics once deployed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.