Prevent Loan Default Model Overfitting

Business Context

LendWise, a mid-size digital lender processing about 120K personal loan applications per month, wants a credit risk model to predict 90-day default before underwriting decisions are made. The current model performs well offline but degrades noticeably after deployment, and the risk team suspects overfitting.

Dataset

• Size: 240K historical applications, 45 features
• Target: Binary — defaulted within 90 days of origination (1) vs non-default (0)
• Class balance: 11.5% positive, 88.5% negative
• Missing data: 18% missing in employment_length, 9% in bureau variables for thin-file applicants, <2% elsewhere

Success Criteria

A strong solution should improve generalization on an unseen holdout set and clearly explain how overfitting is detected and prevented. Good enough means achieving ROC-AUC >= 0.80, PR-AUC >= 0.42, and keeping the train-test AUC gap below 0.03.

Constraints

• Predictions must be returned in <50 ms per application
• The risk team needs interpretable drivers for adverse action review
• Retraining is allowed monthly; feature generation must work in batch and online scoring

Deliverables

1. Define overfitting in the context of this default prediction problem.
2. Build a baseline model and a less-overfit model, then compare train/validation/test performance.
3. Show at least three prevention methods (for example: regularization, feature selection, cross-validation, early stopping, or limiting model complexity).
4. Recommend a final production-ready approach and justify the tradeoffs.
5. Provide code to train, evaluate, and diagnose overfitting using appropriate metrics and plots/tables.