Product design question

How would you improve fraud detection at Stripe without disrupting legitimate payments.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to balance a precision and recall tradeoff in a real fraud system, where false positives directly cost merchants revenue.

Frame the core tradeoff: catching more fraud usually means blocking more legitimate transactions, costing merchants revenue and trust.
Identify the levers: a risk scoring model using transaction, device and behavioral signals, rather than a single hard rule.
Propose tiered response by risk score: auto approve low risk, step up verification for medium risk, block only high risk.
Address merchant specific tuning, since fraud patterns and false positive tolerance differ by industry.
Define success with paired metrics: fraud loss rate and false decline rate, since optimizing one alone is misleading.
Propose a feedback loop using merchant reported chargebacks and disputes to continuously retrain the model.

States the precision and recall tradeoff explicitly and designs around it instead of treating fraud detection as binary.
Uses step up verification for medium risk transactions, preserving legitimate conversion while adding friction only where needed.
Names both fraud loss rate and false decline rate as paired metrics, showing awareness that optimizing one alone harms merchants.

Proposing a stricter blanket rule that reduces fraud but ignores the false decline cost to merchants.
Measuring only fraud caught, without tracking legitimate transactions wrongly blocked.

How would you tune the model differently for a high risk merchant category like digital goods?
How would you handle a false decline complaint from a large merchant?