Product design question

How would you prevent fraud in an online lending platform?

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

Trust and safety product design for a high stakes financial category, balancing fraud prevention against legitimate borrower friction.

Clarify scope: identity fraud, income misrepresentation, or bot driven fake applications, since mitigations differ.
Identify the core tension: stronger fraud checks add friction that can also push away legitimate, credit worthy borrowers.
Segment risk signals: identity verification, device and behavioral signals, and cross referencing income data sources.
Prioritize identity verification and income cross checks first, since they address the highest dollar value fraud risk.
Propose a tiered approach: light friction for low risk applicants, deeper verification triggered only for flagged high risk profiles.
Define success with fraud loss rate reduction alongside a guardrail on legitimate applicant approval rate.

Names the real core tension, fraud prevention versus legitimate borrower friction, and designs specifically around balancing it.
Proposes a tiered, risk based verification approach rather than applying maximum friction to every single applicant.
Sets two paired metrics, fraud loss reduction and legitimate approval rate, so one is not optimized at the other's expense.
Names concrete signal types, device and behavioral data, appropriate for a real fraud detection system.

Proposes uniformly heavy verification for all applicants, ignoring the cost to legitimate borrower conversion.
Has no guardrail metric to catch legitimate applicants being wrongly blocked.

How would you handle a false positive that blocks a legitimate borrower?
How would you keep fraud patterns from evolving faster than your detection system?
How would you measure the tradeoff between fraud loss and lost legitimate revenue?