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AI productstretch
How would you set up a human review process for an AI system that flags fraudulent transactions?
How to approach it
Decide which cases a person must see by combining the model's confidence with the cost of being wrong, since blocking a good customer and missing fraud both hurt.
How a strong answer opens
“I'd decide which cases a person must see by combining the model's confidence with the cost of being wrong. Blocking a good customer and missing fraud both hurt, in different ways.”
Outline of a strong answer
Try answering out loud for a few minutes before you open this.
- Name both errors and their costs: false blocks anger customers, missed fraud loses money
- Auto-approve low-risk cases, auto-block very high-confidence fraud, send the middle band to reviewers
- Give reviewers context: past behavior, why the model flagged it, a time target
- Feed reviewer decisions back to improve the model
- Let customers appeal quickly, and audit for unfair patterns across groups
- Track fraud losses, false block rate, review queue time and reviewer agreement
Where this question usually goes wrong
Sending every flag to human review, which doesn't scale, or auto-blocking everything flagged and angering good customers.
Learn the method
Risk, safety and trust in Module 7, AI product interviews.
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