Select Loan Default Model Under Constraints

Business Context

NorthStar Bank wants a model to predict whether a personal loan applicant will default within 12 months. The risk team needs a solution that balances predictive performance with strict interpretability and low-latency batch scoring for daily underwriting.

Dataset

• Size: 120K historical loan applications, 36 features
• Target: Binary — default within 12 months (1) vs no default (0)
• Class balance: 14% positive, 86% negative
• Missing data: 9% missing in employment and income fields, 4% missing in credit bureau attributes

Success Criteria

A good solution should achieve strong ranking performance while remaining explainable enough for compliance review. Target at least AUC-ROC >= 0.82, F1 >= 0.58, and recall >= 0.70 at an operational threshold chosen with business costs in mind.

Constraints

• Predictions must be explainable to risk analysts and auditors
• Batch inference for 20K applications/day should complete in under 5 minutes
• Training must fit on a standard CPU machine; no large-scale GPU infrastructure
• The bank prefers stable models over small gains from highly complex methods

Deliverables

1. Compare at least three candidate models for this classification problem
2. Justify which model you would choose under the stated constraints
3. Build a reproducible training and evaluation pipeline
4. Show how you handle missing values, categorical variables, and class imbalance
5. Recommend an operating threshold and explain the tradeoff between performance and interpretability