Business Context
NorthStar Bank wants a default-risk model for unsecured personal loans. The risk team does not want the single highest-scoring model by default; they want a solution that is accurate, stable, and explicit about where each model fails.
Dataset
You are given a historical loan-origination dataset for model comparison and selection.
| Feature Group | Count | Examples |
|---|
| Applicant demographics | 6 | age, employment_status, residential_status |
| Financial attributes | 9 | annual_income, debt_to_income, revolving_utilization, delinquencies_12m |
| Credit history | 7 | credit_score, oldest_trade_age, inquiries_6m, open_accounts |
| Loan attributes | 5 | loan_amount, term_months, interest_rate, channel |
| Derived flags | 4 | thin_file_flag, recent_missed_payment, high_utilization_flag |
- Size: 120,000 loans, 31 input features
- Target: Binary label indicating whether the customer defaulted within 12 months of origination
- Class balance: 14% default, 86% non-default
- Missing data: 8% missing in employment-related fields, 5% missing in bureau variables for thin-file applicants
Success Criteria
A good solution should achieve ROC-AUC >= 0.82, PR-AUC >= 0.45, and recall >= 0.70 at precision >= 0.40 on the held-out test set. The candidate must also explain why a simpler model may be preferred over a more complex one in production.
Constraints
- Decisions must be explainable to the bank's risk analysts and compliance reviewers
- Batch scoring must finish in under 10 minutes for 500,000 applications
- Retraining is allowed monthly, not daily
- The final recommendation must discuss model limitations, not just performance
Deliverables
- Train and compare at least three models: logistic regression, decision tree, and random forest or gradient boosting.
- Build a preprocessing pipeline for missing values, categorical encoding, and scaling where needed.
- Evaluate models with appropriate classification metrics and threshold analysis.
- Explain the limitations of each model and recommend one for production.
- Describe how you would monitor model drift and degradation after deployment.
