Product design question
Using AI/ML, how can Amazon detect and prevent fraud done by retailers listed on Amazon e-commerce website
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What this question tests
AI and technical product thinking: can you design a fraud detection system with the right data, model approach, and precision recall trade off.
How to approach it
- Define fraud types to target, assume fake listings and stolen inventory resale by third party sellers.
- Identify signals: seller account age, sudden inventory spikes, price far below market, return rate, and payment method patterns.
- Propose an approach: a supervised model trained on confirmed fraud cases, combined with rule based flags for known patterns while the model matures.
- Discuss the precision recall trade off explicitly, since flagging legitimate sellers as fraud damages trust and revenue.
- Design the response flow: low confidence flags go to human review, high confidence triggers automatic listing suspension.
- Define success as fraud caught before customer harm and false positive rate on legitimate sellers.
What a strong answer includes
- Names specific, checkable signals instead of vaguely citing AI or machine learning as the fix.
- Explicitly weighs precision against recall and picks a side with reasoning, since over flagging sellers is costly.
- Builds in a human review step for uncertain cases rather than fully automating enforcement from day one.
Common mistakes
- Saying just use machine learning without specifying signals or training data.
- Ignoring the cost of false positives on legitimate sellers.
Likely follow-up questions
- How would you get labeled training data for a new fraud pattern?
- How would you measure if the model is still accurate six months after launch?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop