LendWise, a digital consumer lending platform processing ~120K loan applications per month, wants a supervised learning model to predict whether an applicant will default within 12 months. The risk team needs a model that improves approval decisions while remaining explainable enough for audit and policy review.
You are given a historical labeled dataset where each row represents one funded loan and the target indicates whether the borrower defaulted. This is a standard supervised learning problem because the model is trained on input features and known outcomes.
| Feature Group | Count | Examples |
|---|---|---|
| Applicant demographics | 6 | age, employment_status, residence_type |
| Credit history | 8 | credit_score, prior_delinquencies, revolving_utilization |
| Financials | 7 | annual_income, debt_to_income_ratio, existing_loans |
| Loan attributes | 5 | loan_amount, term_months, interest_rate, purpose |
| Behavioral / bureau freshness | 4 | recent_inquiries_30d, days_since_last_delinquency |
A good solution should achieve ROC-AUC >= 0.82, F1 >= 0.58, and recall >= 0.70 for the default class at an operational threshold chosen with business tradeoffs in mind.