Estimate Default Rates Using Logistic Regression Model
Overview
This question evaluates competency in statistical modeling and inference for credit risk, covering probabilistic classification, coefficient interpretation (odds ratios), estimation of segment-level default probabilities, confidence interval construction, and communication of uncertainty to non-technical stakeholders.
On-site Statistical Role Play: Estimate Credit-Card Default Probability for a New Customer Segment
Context
You have historical account-level data with a 12‑month default label (default = 1, non‑default = 0) and features: account age (months), utilization (balance/limit), and credit score. A new customer segment is being launched (e.g., defined by marketing criteria), and you must:
- Choose and justify a statistical model to estimate default probabilities (PD) using these predictors.
- Produce a 95% confidence interval (CI) for the segment's default rate.
- Explain the CI in business terms to a non‑technical executive.
Assume you can train on historical data and that for the new segment you either have: (a) a list of prospective accounts with features, or (b) a pilot with n opened accounts and k observed defaults after 12 months.
Tasks
- Select and justify a model to estimate PD using account age, utilization, and credit score. Interpret coefficients (odds ratios).
- Compute a 95% CI for the default rate of the new segment using an appropriate method (e.g., Wald/delta method vs. bootstrap, or binomial proportion if a pilot exists).
- Explain the meaning of the 95% CI to a non‑technical executive.
Solution
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