Evaluate Stripe Capital Lending Strategy

Stripe is considering expanding **Stripe Capital**, a lending product for existing merchants on the platform. Eligible merchants receive a **pre-qualified working-capital loan** offer. If a merchant accepts, repayment is collected automatically as **12% of the merchant's daily processed revenue** until the principal plus a fixed fee is fully repaid. Assume you are the data scientist supporting this product. You have access to historical merchant data such as payment volume, refunds, disputes/chargebacks, industry, geography, business tenure, seasonality, and prior loan performance. Assume product profit can be approximated as: **Profit = fee revenue - cost of capital - expected credit losses - servicing/operational costs** Answer the following: 1. **Dashboard design:** What metrics would you include on a dashboard for Stripe Capital? Include metrics across merchant acquisition/adoption, loan performance, repayment behavior, credit risk, merchant outcomes, and unit economics. Explain which metrics are leading vs. lagging indicators, and how you would segment or cohort them. 2. **Early risk signals:** How would you determine that Stripe should **not** offer a pre-qualified loan to a merchant, or that an existing loan is becoming risky? What early signals and predictive features would you use? How would you think about thresholds, calibration, false positives vs. false negatives, and fairness or bias concerns? 3. **Single offer vs. multiple offers:** Stripe is considering whether to present merchants with **one recommended loan amount** or **multiple loan options**. What are the product, risk, operational, and measurement pros and cons of each approach? 4. **Profit decline diagnosis:** Suppose Stripe Capital profit has declined over the last two quarters. How would you diagnose the root cause? Provide a structured analysis plan, including how you would separate changes in demand, underwriting quality, repayment behavior, pricing, portfolio mix, and macro conditions.