Metrics question
What would be your approach to validating an idea for a borrowing and lending product, and how would you go about building it?
- Meta
- Metrics
- Hard
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What this question tests
Metrics and validation thinking: structuring a lean way to test demand for a new fintech product before full build-out.
How to approach it
- Clarify the concept: peer-to-peer or platform-based lending and borrowing, and pick a specific segment like small personal loans.
- Identify the core hypothesis to validate: enough users want better rates or access than banks currently offer.
- Propose a lean test, like a waitlist with a concrete offer, or a manual concierge pilot matching a small group of lenders and borrowers.
- Define validation metrics: signup-to-waitlist conversion, willingness to commit funds, and repayment behavior in the pilot.
- Outline the build path: only invest in full automation and scale once the pilot shows healthy repayment and demand metrics.
What a strong answer includes
- Proposes a manual, low-tech pilot (concierge matching) before building automated infrastructure, showing lean validation instinct.
- Names the real risk in lending products: default and repayment behavior, and tests for it early with real money in the pilot.
- Uses a concrete metric, e.g. requiring under 5 percent default rate in the pilot before scaling.
- Separates validation metrics (demand) from health metrics (repayment), showing depth in a regulated category.
Common mistakes
- Jumping straight to a full automated platform without a lean validation step.
- Ignoring credit risk and repayment behavior as the core risk to test for.
- No clear metric or threshold for deciding whether to scale.
Likely follow-up questions
- How would you assess credit risk in a pilot with limited data?
- What regulatory considerations would you need to address before scaling?
- How would you decide the pilot has succeeded enough to build the full product?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop