Handle missing values for LGD modeling
Overview
This question evaluates a data scientist's understanding of missing-data mechanisms and imputation strategies in Loss Given Default (LGD) credit-risk modeling, including recognition of MCAR/MAR/MNAR assumptions, sources of bias and leakage, and validation approaches for performance and stability.
Handling Missing Values for LGD Modeling
Context
You are building a Loss Given Default (LGD) model using account- and borrower-level features captured around the time of default. The dataset contains both continuous and categorical variables with non-trivial missingness due to reporting gaps, system migrations, and process differences across products/regions.
Task
Describe how you would handle missing values in this LGD modeling context. Specifically, compare the following approaches:
- Multiple imputation (e.g., MICE)
- Model-based imputation (e.g., kNN, random forest, regression)
- Business-rule fills (domain-driven heuristics)
- Indicator variables (missingness flags; Unknown category)
- Leaving missingness explicit (letting the model handle NA directly)
For each, explain:
- Assumptions about the missingness mechanism (MCAR, MAR, MNAR)
- Pros, cons, and typical use cases in LGD modeling
- Guardrails to avoid bias and leakage
- How you would validate the choice and measure impact on LGD performance and stability
Solution
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