Select Features for Loan Default | Dataford Interview Questions

Your question is Select Features for Loan Default. Take a moment with it on the right.

Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).

Business Context

Northstar Lending is building a binary classification model to predict whether a personal loan applicant will default within 12 months. The risk team wants a feature selection approach that improves generalization, reduces overfitting, and keeps the final model interpretable for compliance review.

Dataset

You are given a historical underwriting dataset with applicant, credit, and behavioral variables collected at application time.

Feature Group	Count	Examples
Applicant demographics	8	age, employment_length, home_ownership, region
Financial attributes	14	annual_income, debt_to_income, revolving_utilization, open_credit_lines
Credit history	11	fico_band, delinquencies_2y, inquiries_6m, public_records
Application metadata	7	channel, loan_purpose, requested_amount, term_months
Engineered candidates	20	income_per_open_line, utilization_x_inquiries, log_income, missingness flags

Size: 120K loan applications, 60 candidate features
Target: default_12m — defaulted within 12 months (1) vs not defaulted (0)
Class balance: 18% positive, 82% negative
Missing data: 12% missing in employment and income-related fields; 3% missing in bureau variables

Success Criteria

A good solution should:

Achieve ROC-AUC >= 0.78 on the held-out test set
Reduce the feature set from 60 candidates to a smaller, defensible subset without materially hurting performance
Produce a feature selection process that is reproducible and explainable to risk and compliance stakeholders

Constraints

The final model must remain interpretable enough for model risk management review
Training runs should finish within 30 minutes on a standard CPU machine
Feature selection must avoid leakage and be performed only using training folds

Deliverables

Build a baseline classification pipeline using all candidate features.
Implement at least two feature selection techniques and compare them.
Select a final feature set and justify why it is appropriate for this dataset.
Report evaluation metrics on validation and test data.
Summarize tradeoffs between predictive power, stability, and interpretability.