LendWise, a mid-size digital lender processing ~120K personal loan applications per month, wants to improve its default-risk model before the next underwriting policy update. The current model is a regularized logistic regression, but performance has plateaued and the credit risk team wants a structured hyperparameter tuning approach that improves ranking quality without sacrificing interpretability.
| Feature Group | Count | Examples |
|---|---|---|
| Applicant demographics | 6 | age, employment_length, home_ownership |
| Credit bureau signals | 11 | fico_score, revolving_utilization, delinquencies_2y |
| Loan attributes | 7 | loan_amount, term_months, interest_rate, purpose |
| Banking behavior | 8 | avg_monthly_balance, overdraft_count_90d, inflow_volatility |
| Derived ratios | 6 | debt_to_income, credit_used_ratio, payment_to_income |
A successful solution should improve model quality over the current baseline and produce a repeatable tuning workflow. Good enough means achieving ROC-AUC >= 0.80, PR-AUC >= 0.42, and F1 >= 0.48 at a threshold chosen on validation data, while keeping the final model explainable to risk analysts.