Explain Loan Default with Logistic Regression

Business Context

NorthStar Bank reviews roughly 120,000 personal loan applications per month and needs a transparent baseline model for default risk screening. The credit risk team wants a logistic regression model they can explain to auditors, product managers, and operations analysts.

Dataset

You are given an application-level dataset for binary classification: whether a borrower defaults within 12 months of origination.

• Size: 480,000 loan applications, 32 features
• Target: Binary label indicating default within 12 months
• Class balance: 11.4% default, 88.6% non-default
• Missing data: 9% missing in banking features, 3% missing in bureau variables, sparse missingness in employment fields

Success Criteria

A strong solution should produce a well-calibrated logistic regression model with ROC-AUC ≥ 0.78, PR-AUC ≥ 0.38, and clear coefficient-based explanations for the top risk drivers. The model should also support threshold tuning for different approval policies.

Constraints

• The model must be interpretable enough for regulatory review
• Batch scoring should complete in under 5 minutes for 120,000 applications
• Feature transformations must be reproducible in production
• The bank prefers a stable baseline over a complex black-box model

Deliverables

1. Explain logistic regression in practical terms, including the sigmoid function, log-odds, coefficients, and why it is used for binary classification.
2. Build a production-ready logistic regression pipeline for default prediction.
3. Handle missing values, categorical variables, scaling, and class imbalance appropriately.
4. Evaluate the model using threshold-free and threshold-based metrics.
5. Interpret the learned coefficients and discuss when logistic regression is preferable to more complex models.