Balance Customer Satisfaction with Fraud Prevention: Key Metrics to Track
Overview
This question evaluates a data scientist's ability to design decision strategies and metric frameworks that balance customer satisfaction with fraud prevention, covering precision–recall trade-offs, economic cost–benefit thresholds, experiment design, and monitoring of leading and lagging indicators.
Balancing Customer Experience and Fraud Prevention
Scenario
In a consumer app with payments (e.g., in‑app purchases, wallet top-ups, withdrawals, creator payouts), Product wants minimal friction for legitimate users, while Risk wants to aggressively block suspicious transactions to reduce fraud losses. These goals can conflict.
Question
How would you balance customer satisfaction with fraud prevention, and which metrics would you track over time to evaluate whether the balance is working?
Please address:
- Decision strategy: How you’d choose when to allow, step‑up (extra verification), manual review, or block; discuss precision–recall and false‑positive/false‑negative trade-offs.
- Economic framing: How you’d translate business costs/benefits into a decision threshold (cost‑benefit).
- Experimentation and rollout: How you’d test and ramp changes safely (A/B tests or alternatives) and manage delayed fraud labels.
- Measurement: The key metrics (leading vs lagging) you’d track to ensure both fraud and customer experience are healthy over time.
Solution
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