How would you build a fraud detection system for an e-commerce platform?

What They’re Really Asking

Can you design a scalable, real-time system that balances fraud prevention with user experience and business impact?

Strong Sample Answer

I would start by defining the problem: our platform loses $50M annually to fraud, but we must avoid false declines that hurt conversion. First, I'd validate requirements: detect fraud in under 200ms, keep false positive rate below 0.5%, and integrate with checkout flows. Using a PR/FAQ framework, I'd prioritize a hybrid approach: rule-based logic for known patterns (e.g., card testing, IP anomalies) and a machine learning model using gradient boosting on features like order velocity, shipping mismatches, and session behavior. For real-time execution, we'd run rules on a stream processing engine (like Apache Flink) and batch scores from ML on a 5-minute lag. We'd A/B test the system across 10% of traffic, measuring fraud rate reduction and approval rates. Within 3 months, we cut fraud losses by 40% while maintaining a 98.5% approval rate. Key tradeoffs: we held off on deep learning due to latency needs, and we added manual review queue for borderline cases with a 30-second SLA. We also built a feedback loop to retrain the model weekly using labeled false negatives from customer disputes.

Common Mistake to Avoid

Company-Specific Variants