AdvancedSystem-Design
45 min
Design a Fraud Detection System
FraudMLStreamingSecurity
Advertisement
Interview Question
Design a fraud detection system for online payments with real-time scoring, feature pipelines, and explainability.
Key Points to Cover
- Feature pipelines with real-time and batch data
- Model serving: scoring latency, ensemble methods
- Data storage: feature store, sliding windows, joins
- Explainability: feature attribution, audit logs
- Feedback loop: false positives, model retraining
Evaluation Rubric
Strong real-time/batch feature pipeline25% weight
Low-latency model serving design25% weight
Explainability and auditing strategy25% weight
Feedback loop and retraining process25% weight
Hints
- 💡Think about balancing precision vs recall.
Common Pitfalls to Avoid
- ⚠️Underestimating real-time data processing complexity and latency requirements.
- ⚠️Failing to adequately manage and version features in the feature store.
- ⚠️Over-reliance on a single model type, leading to blind spots for specific fraud patterns.
- ⚠️Neglecting the importance of explainability for human analysts, leading to trust issues and manual review inefficiencies.
- ⚠️Insufficient monitoring and a lack of a robust feedback loop for continuous model improvement and adaptation.
Potential Follow-up Questions
- ❓How to prevent model drift?
- ❓What about adversarial fraud patterns?
Advertisement