Building VendorFlow: Fraud Detection in React

VendorFlow was designed to revolutionize vendor payment processing, but with great scale comes great responsibility—especially when handling financial transactions. The initial system could process thousands of payments daily, but we had a critical problem: fraud detection was happening post-transaction, leaving vulnerabilities open.

We needed to build a real-time fraud detection system that could: - Analyze transactions instantly without blocking legitimate payments - Learn from historical data and adapt to new fraud patterns - Provide immediate alerts to our security team - Scale horizontally to handle peak traffic

Our fraud detection system is built on a multi-layered architecture:

**Frontend Layer (React)** - Real-time transaction monitoring dashboard - Live fraud alerts with WebSocket integration - Historical analysis and reporting

**Backend Layer (Node.js + Express)** - Transaction processing and validation - Pattern analysis engine - API endpoints for dashboard integration

**Machine Learning Layer** - Isolation Forest algorithm for anomaly detection - Random Forest for pattern classification - Real-time model updates based on labeled data

**Data Pipeline** - Event streaming with message queues - Time-series data storage - Batch processing for model retraining

The system processes each transaction through three stages: validation, analysis, and decision.

**1. Real-Time Processing with WebSockets**

We implemented WebSocket connections to push fraud alerts instantly to our dashboard:

// Server-side WebSocket handler
io.on('connection', (socket) => {
  socket.on('subscribe-transactions', (vendorId) => {
    socket.join(`vendor-${vendorId}`);
  });
});

**2. Machine Learning Integration**

We trained multiple models on historical transaction data:

// Isolation Forest for anomaly detection
const model = new IsolationForest({
  numTrees: 100,
  sampleSize: 256,
  randomState: 42
});

// Features extracted from transactions const features = [ transaction.amount, transaction.velocity, // transactions per hour transaction.geoDistance, // distance from last transaction transaction.merchantCategory, transaction.deviceFingerprint ];

**3. Pattern Recognition**

We built a sophisticated pattern matching system:

async function analyzeTransactionPattern(transaction, userHistory) {
  const patterns = {
    velocityPattern: checkVelocityAnomaly(transaction, userHistory),
    geoPattern: checkGeographicAnomaly(transaction, userHistory),
    devicePattern: checkDeviceAnomaly(transaction, userHistory),
    behaviorPattern: checkBehavioralShift(transaction, userHistory)
  };

**4. React Dashboard Integration**

Our real-time dashboard displays active fraud detection:

export function FraudMonitoringDashboard() {
  const [alerts, setAlerts] = useState([]);
  const [isConnected, setIsConnected] = useState(false);

useEffect(() => { const socket = io(API_URL);

socket.on('connect', () => setIsConnected(true)); socket.on('fraud-alert', (alert) => { setAlerts(prev => [alert, ...prev].slice(0, 50)); });

return () => socket.disconnect(); }, []);

**Key Metrics After Implementation:**

• • **Fraud Detection Rate**: 94% of fraudulent transactions caught in real-time
• • **False Positive Rate**: Reduced from 8% to 2% after model refinement
• • **Processing Time**: Average 150ms per transaction analysis
• • **System Uptime**: 99.97% with failover mechanisms
• • **Cost Reduction**: 45% decrease in fraud-related chargebacks

**Real-World Impact:**

Over a 6-month period, our fraud detection system: - Prevented $2.3M in fraudulent transactions - Saved 150+ vendor accounts from compromise - Reduced customer support load related to fraud by 60% - Maintained 98.5% customer satisfaction despite proactive blocking

The system successfully adapted to evolving fraud tactics through continuous model retraining, catching new patterns within 24-48 hours of emergence.

**1. Latency vs. Accuracy Trade-off**

Challenge: Machine learning inference was taking too long (~500ms), delaying transactions.

Solution: We implemented model quantization and deployed lighter models to edge servers, reducing latency to 150ms while maintaining 92% accuracy.

**2. Model Drift**

Challenge: The fraud patterns evolved faster than we could retrain models.

Solution: Implemented automated retraining pipelines triggered when detection metrics drift beyond thresholds, with A/B testing for new models before production deployment.

**3. Handling Imbalanced Data**

Challenge: Fraudulent transactions are rare (0.1-0.3%), making training data highly imbalanced.

Solution: Used SMOTE (Synthetic Minority Over-sampling Technique) and weighted loss functions to properly train models on minority fraud class.

**4. False Positives Impact**

Challenge: Legitimate transactions being blocked hurt customer experience.

Solution: Implemented a confidence-based blocking strategy—only automatically blocking extremely high-confidence fraud (>95%), routing medium-confidence cases to human review.

**5. Real-time Data Processing**

Challenge: Processing thousands of concurrent transactions with complex feature engineering.

Solution: Pre-computed features using stream processing (Apache Kafka) and cached frequently accessed data in Redis for sub-100ms lookups.

1. **Start Simple, Iterate**: Our first model was a simple rule-based system. We evolved it incrementally based on real fraud data rather than trying to build the perfect system upfront.

2. **Monitor Model Performance**: We track dozens of metrics beyond accuracy—false positive rate, false negative rate, precision, recall—to understand real-world impact.

3. **Combine Multiple Signals**: The best fraud detection comes from combining multiple weak signals (velocity, geography, device, behavior) rather than relying on any single indicator.

4. **Human-in-the-Loop**: Even with advanced ML, human oversight is crucial. Our security team's feedback directly improves model performance.

5. **Transparency Matters**: When we block transactions, users need to understand why. Clear communication prevents support tickets and maintains trust.

6. **Think About Adversarial Behavior**: Fraudsters adapt. We built systems specifically to detect evolving fraud patterns and automated model updates.

7. **Scale from Day One**: Build with horizontal scaling in mind. Our WebSocket architecture and distributed processing setup made scaling pain-free.

We're excited about upcoming improvements:

**Graph Neural Networks**: Moving beyond feature-based detection to relationship-based fraud rings detection.

**Federated Learning**: Allowing partner vendors to collaboratively train models without sharing raw transaction data.

**Explainable AI**: Implementing LIME and SHAP for more interpretable fraud decision explanations.

**Real-time Adaptation**: Moving from batch model retraining to online learning algorithms that adapt to new patterns instantly.

**Multi-Modal Analysis**: Incorporating transaction image data, voice patterns from customer service calls, and other modalities for deeper analysis.

The future of fraud prevention isn't just about better algorithms—it's about creating an ecosystem where legitimate users flow smoothly while malicious actors face an increasingly sophisticated defense system.