LendWise, a mid-sized digital lender processing about 120K personal loan applications per month, wants a reliable binary classifier to predict 90-day default risk at application time. The team is less interested in naming optimization libraries abstractly and more interested in whether you can choose, tune, and justify practical optimization tools in a real training workflow.
You are given a historical loan-origination dataset for supervised classification.
| Feature Group | Count | Examples |
|---|---|---|
| Applicant profile | 10 | age, employment_length, annual_income, housing_status |
| Credit bureau | 12 | fico_band, revolving_utilization, delinquencies_2y, inquiries_6m |
| Loan attributes | 8 | loan_amount, term_months, interest_rate, purpose |
| Behavioral / derived | 6 | debt_to_income, income_to_loan_ratio, recent_credit_change |
default_90d — whether the borrower defaults within 90 days of originationA good solution should outperform a regularized logistic regression baseline and achieve strong ranking quality for underwriting decisions. Target ROC-AUC >= 0.84, PR-AUC >= 0.46, and recall >= 0.70 at precision >= 0.35 on the held-out test set.