Design Fraud Detection System #

Problem Statement #

Design a real-time fraud detection system that analyzes transaction streams for suspicious patterns. The system must process massive volumes of financial events, apply ML models for anomaly detection, and trigger alerts while maintaining low false positive rates and ensuring high availability for transaction processing.

Requirements #

Functional Requirements #

• Real-time transaction analysis and scoring
• Machine learning-based anomaly detection
• Rule-based and behavioral fraud prevention
• Alert generation and risk assessment
• Historical analysis and model training
• Integration with payment processing workflows

Non-Functional Requirements #

• Sub-second analysis latency (<50ms per transaction)
• High throughput for transaction processing (100k TPS)
• Low false positive rate (<1%) with high detection accuracy
• Fault tolerance with zero transaction loss
• Scalability to global transaction volumes

Key Constraints & Assumptions #

• Scale assumptions: 10B transactions/day globally, 1M concurrent fraud checks/sec, 99.999% uptime required; fraud rate <1% but critical to catch ^[Assumption: Scale of major payment processors and fraud detection services.]
• SLA: 99.999% availability, p99 processing latency <100ms, >99% fraud detection rate with <0.1% false positive
• Data Sensitivity: PCI DSS compliance, end-to-end encryption, strict data retention policies
• False Positives: Business cost of fraud detection errors measured in dollars per false positive

High-Level Design #

The system employs a streaming architecture with event-driven processing. Transactions are enriched with historical context, scored by ML models, and routed through risk engines. A feedback loop continuously trains models on verified fraud cases.

graph TD
    A[Transaction Events] --> B[Event Ingestion]
    B --> C{Stream Processing}
    C --> D[Enrichment Service]
    D --> E[Historical Context DB]
    D --> F[ML Scoring Engine]
    F --> G[Risk Assessment Rules]
    G --> H{Decision Engine}
    H --> I[Allow/Block/Review]
    I --> J[Alert System]
    K[Verified Outcomes] --> L[Feedback Loop]
    L --> M[Model Training]
    M --> F
    N[Monitoring Dashboard] --> O[Performance Metrics]
    P[A/B Testing] --> Q[Model Comparison]
    Q --> M

^[Mermaid diagram showing streaming fraud detection with ML scoring and continuous learning.]

Data Model #

• Transaction Streams: Real-time event streams with transaction metadata, user details, and device fingerprints
• Historical Context: Time-windowed aggregations of user transaction patterns and behavioral profiles
• ML Features: Feature stores with pre-computed embeddings, transaction graphs, and risk scores
• Model Artifacts: Versioned ML models and rule engines for explainable decisions

API Design #

Event-driven APIs with synchronous scoring:

• POST /api/v1/transactions/score - Score transaction: {"amount": 500, "merchant": "amazon", "user": {...}, "device": {...}} → {"risk_score": 0.85, "decision": "review", "reasons": ["unusual_location"]}
• POST /api/v1/alerts/{alertId}/outcome - Verify fraud outcome: {"outcome": "confirmed_fraud", "notes": "stolen_card"} → feedback loop trigger
• GET /api/v1/models/performance - Get model metrics with false positive/negative rates
• PUT /api/v1/rules/{ruleId} - Update fraud rules: {"condition": "amount > 1000", "action": "block", "priority": 1} → dynamic rule management
• WebSocket /alerts/stream - Real-time alert streaming for monitoring teams

^[APIs use API keys and rate limiting to prevent abuse.]

Detailed Design #

• Stream Processing: Apache Flink/Spark Streaming for real-time event processing with exactly-once semantics
• Feature Engineering: Real-time feature extraction from transaction streams, offline batch features from historical data
• ML Scoring: Ensemble models combining supervised learning (fraud labels) with unsupervised anomaly detection
• Rule Engine: Drools-based rules for business logic combining scores with contextual factors
• Risk Aggregation: Multi-signal approach combining IP reputation, device fingerprinting, and behavioral analysis
• Feedback Loop: Real-time learning from verified fraud cases to update model weights and features
• A/B Testing: Model comparison in production with automated promotion of better-performing models

Scalability & Bottlenecks #

• Horizontal Scaling: Stateless scoring services auto-scale with transaction volume
• Data Partitioning: User/transaction data sharded by account ID, global replication for cross-region access
• Compute Optimization: GPU acceleration for ML inference, optimized data structures for low-latency lookups
• Queue Management: Priority queues for high-risk transactions, rate limiting for DoS protection
• Bottlenecks: ML model serving latency; mitigated by model quantization and edge deployment

Trade-offs & Alternatives #

• Real-time vs Batch Detection: Instant blocking prevents fraud vs. batch analysis enables deeper investigation
• Accuracy vs Speed: Complex ensemble models higher accuracy vs. simple rules lower latency
• Automated vs Manual Review: Automated decisions scale better vs. manual review allows nuance but slower
• Client vs Server Detection: Client-side detection preserves privacy vs. server-side enables richer context

Future Improvements #

• Graph-based fraud detection using transaction networks
• Federated learning for privacy-preserving model training
• Behavioral biometrics integration (keystroke patterns, device usage)
• Real-time synthetic transaction generation for model stress testing
• Crypto currency and digital asset fraud detection

Interview Talking Points #

1. Explain streaming architecture: Real-time processing enables instant fraud decisions vs. batch processing too slow
2. Discuss ML pipelines: Feature engineering transforms raw transactions into meaningful signals for detection
3. Address false positives: Business impact measured in dollars, requires careful threshold tuning
4. Compare supervised vs unsupervised: Supervised learns from known fraud vs. unsupervised finds novel patterns
5. Handle scale: Distributed stream processing and sharded data stores manage global transaction volumes
6. Implement feedback loop: Verified fraud cases continuously improve model accuracy over time
7. Balance detection speed: Multi-stage scoring allows fast initial decisions with detailed analysis for high-risk
8. Ensure compliance: Encryption and audit trails protect sensitive financial data while enabling detection