Evaluate Stripe Capital Lending Strategy

Stripe is evaluating an expansion of Stripe Capital, its lending offering for merchants already using the platform. Merchants who meet eligibility criteria see a pre-qualified working-capital loan offer. If the merchant accepts, repayments are automatically deducted as 12% of daily processed revenue until the principal and a fixed fee are fully repaid.

Imagine you are the data scientist responsible for this product. You can use historical merchant data including payment volume, refunds, disputes/chargebacks, industry, geography, business tenure, seasonality, and past loan performance. Assume product profit can be approximated as:

$\text{Profit} = \text{fee revenue} - \text{cost of capital} - \text{expected credit losses} - \text{servicing/operational costs}$

Answer the following:

1. Dashboard design: Which metrics would you put on a Stripe Capital dashboard? Cover merchant acquisition/adoption, loan performance, repayment behavior, credit risk, merchant outcomes, and unit economics. Identify which metrics are leading versus lagging indicators, and describe how you would segment or cohort them.

2. Early risk signals: How would you decide that Stripe should not extend a pre-qualified loan to a merchant, or that a current loan is becoming riskier? What early signals and predictive features would you rely on? How would you approach thresholds, calibration, false positives versus false negatives, and fairness or bias concerns?

3. Single offer vs. multiple offers: Stripe is weighing whether to show merchants one recommended loan amount or multiple loan options. What are the product, risk, operational, and measurement trade-offs of each approach?

4. Profit decline diagnosis: Suppose Stripe Capital profit has fallen over the past two quarters. How would you diagnose the root cause? Lay out a structured analysis plan, including how you would isolate changes in demand, underwriting quality, repayment behavior, pricing, portfolio mix, and macro conditions.

Overview: This question tests data science skills in product analytics, credit risk modeling, monitoring and instrumentation for a merchant lending product, including dashboard metric design, early-warning signal engineering, offer-structuring trade-offs, and portfolio-level profit attribution.