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Design a Real-time Fraud Detection System

Build an ML-powered fraud detection system processing 10B+ daily transactions with <100ms scoring latency, <0.8% false positive rate, and 97%+ fraud detection accuracy.

System Scale: 10B transactions/day, 120K TPS peak, <100ms latency, 97%+ detection rate with <0.8% false positives. Must handle evolving fraud patterns and regulatory compliance.

Q: What's the scale and volume of transactions we need to handle?
A: Process 10 billion transactions daily with peak loads of 120K TPS during shopping events. 99% are legitimate transactions, 1% fraudulent.
💡 Massive scale drives architecture decisions: need distributed processing, aggressive caching, and hybrid rule/ML approach. The 99:1 ratio means even 0.5% false positives would block 50x more legitimate users than fraudsters.
Q: What are the latency and accuracy requirements?
A: Real-time scoring in <100ms for payment authorization. Target 97%+ fraud detection rate with <0.8% false positive rate to maintain user experience.
💡 Sub-100ms requirement eliminates heavy deep learning models for all transactions. Need fast rule engine for obvious cases + selective ML inference for suspicious patterns. False positive optimization is critical for business.
Q: What types of fraud patterns should we detect?
A: Account takeover, stolen credit cards, synthetic identity fraud, transaction laundering, coordinated bot attacks, and emerging attack vectors.
💡 Different fraud types require different features and models. Need behavioral analytics for account takeover, velocity checks for stolen cards, and graph analysis for coordinated attacks. Must adapt quickly to new patterns.
Q: What compliance and explainability requirements exist?
A: Regulatory compliance (PCI DSS, PSD2) requires audit trails, data retention, and explainable decisions. Must support dispute resolution and regulatory reporting.
💡 Explainability vs accuracy trade-off: simpler models easier to explain but less accurate. Need feature importance, decision paths, and rule explanations for regulatory compliance and customer disputes.
Q: How should we handle model updates and fraud evolution?
A: Fraud patterns evolve rapidly (weekly/monthly). Need continuous learning, A/B testing, champion-challenger model updates, and rapid response to new attack vectors.
💡 Continuous adaptation is key competitive advantage. Need automated drift detection, incremental learning, real-time feedback loops from fraud investigations, and safe deployment pipelines for model updates.
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Interview Practice Questions

Practice these open-ended questions to prepare for system design interviews. Think through each scenario and discuss trade-offs.

1

Real-Time Payment Fraud Detection: Design a fraud detection system for payment processing handling 100K+ transactions per second with sub-50ms latency requirements. Address feature engineering, model serving, false positive reduction, and regulatory compliance.

2

E-commerce Account Takeover Prevention: Build a system detecting account takeover attempts across login, shopping, and checkout flows. Handle behavioral biometrics, device fingerprinting, velocity checks, and seamless user experience for legitimate users.

3

Multi-Channel Fraud Detection Platform: Design a unified fraud detection system across mobile apps, web, in-store, and call center channels. Address cross-channel correlation, consistent risk scoring, and channel-specific attack vectors.

4

Cryptocurrency Exchange Fraud Prevention: Build fraud detection for crypto trading platforms handling wash trading, market manipulation, money laundering, and regulatory compliance. Address blockchain analysis, transaction graph features, and real-time monitoring.

5

Insurance Claims Fraud Detection: Design a system for detecting fraudulent insurance claims across auto, health, and property domains. Handle document analysis, provider networks, claim patterns, and investigation workflow integration.

6

Banking Wire Transfer Monitoring: Build a system monitoring wire transfers for money laundering, sanctions violations, and suspicious activity reporting. Address entity resolution, network analysis, regulatory reporting, and investigator tools.