Start by framing missingness as a business and regulatory issue in LGD modeling, not just a technical one, then systematically compare imputation methods against model performance and interpretability. Emphasize that the choice depends on missingness mechanism (MCAR/MAR/MNAR), data volume, and whether missingness itself carries predictive signal. Conclude with a validation strategy that tests each approach against out-of-time and out-of-sample data.
Pro tip: In credit risk, missingness often encodes default or workout status—e.g., missing recovery data for defaults still in progress—so always ask whether the missingness is informative before imputing. Documenting the missingness mechanism and its business cause will impress risk reviewers more than any single imputation technique.
Quantify missing rates by variable and segment, and classify the mechanism as MCAR, MAR, or MNAR using tests and business knowledge. Check whether missingness correlates with the target (LGD) or with default status.
Compare multiple imputation (MICE), model-based imputation (e.g., regression or random forest), and business rule fills (e.g., regulatory floors, peer averages) on bias, variance, and downstream LGD model performance.
Create binary indicator variables for missingness where it may be predictive, and test whether they add lift in the LGD model. This preserves information that imputation might erase.
If missingness is MNAR and informative, consider leaving it as a separate category or using a model that handles missingness natively (e.g., XGBoost with missing direction). Validate that this does not violate regulatory interpretability requirements.
Backtest each approach on out-of-time samples, compare LGD predictions and rank-ordering, and document the rationale for regulators. Ensure the chosen method is stable and reproducible.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.