Real-Time Fraud Detection for Digital Banking Platform

The Challenge

The fintech company faced critical fraud challenges:

• Scaling issues - Rule-based system couldn't handle transaction growth
• High false positives - 8% of legitimate transactions flagged (customer friction)
• Evolving fraud patterns - Static rules easily evaded by fraudsters
• Real-time requirements - Decisions needed in <100ms
• Regulatory compliance - FinCEN and state money transmitter requirements

Our Solution

Phase 1: Discovery & Architecture (Weeks 1-3)

Designed scalable fraud detection architecture:

• Analyzed 18 months of transaction history
• Identified fraud patterns and feature requirements
• Designed low-latency serving infrastructure
• Established baseline metrics and success criteria

Phase 2: Model Development (Weeks 4-8)

Built ensemble fraud detection models:

• Supervised models - Gradient boosting on labeled fraud
• Unsupervised models - Anomaly detection for novel patterns
• Network analysis - Graph features for connected fraud rings
• Behavioral profiling - User spending pattern analysis

Features engineered:

• Transaction velocity and amounts
• Device and location fingerprinting
• Merchant category patterns
• Time-of-day and day-of-week patterns
• Historical fraud rates by attributes
• Network centrality measures

Phase 3: Infrastructure (Weeks 9-12)

Built production-grade serving platform:

Transaction Event
    ↓
Feature Engineering (real-time)
    ↓
Model Inference (ensemble)
    ↓
Rule Engine (business logic)
    ↓
Risk Score + Decision
    ↓
Downstream Systems

Architecture:

• Event streaming: Kafka for transaction events
• Feature store: Redis for real-time features, S3 for batch
• Model serving: Custom Python service on EKS
• Monitoring: DataDog, custom drift detection
• Database: DynamoDB for decisions, RDS for investigations

Phase 4: Deployment & Optimization (Weeks 13-16)

Rolled out with canary deployment and A/B testing:

• 5% traffic → 25% → 50% → 100% over 3 weeks
• Continuous monitoring of fraud rates and false positives
• Implemented feedback loop for model improvement
• Built investigation tools for fraud operations team

Technical Implementation

Real-Time Feature Engineering

# Example feature computation
async def compute_features(transaction):
    features = {}
    
    # Velocity features (last 24h)
    features['txn_count_24h'] = await redis.get(
        f"count:{user_id}:24h"
    )
    features['txn_amount_24h'] = await redis.get(
        f"amount:{user_id}:24h"
    )
    
    # Device fingerprint
    features['new_device'] = not await redis.sismember(
        f"devices:{user_id}", device_id
    )
    
    # Location anomaly
    features['location_distance'] = calculate_distance(
        current_location, typical_location
    )
    
    return features

Model Ensemble

Combined multiple models for robust detection:

1. XGBoost - Primary model (70% weight)
2. Isolation Forest - Anomaly detection (15% weight)
3. Graph Neural Network - Network patterns (15% weight)

Automated Retraining

Daily retraining pipeline:

• Fetch previous day's transactions and fraud labels
• Compute new features
• Train updated models
• Validate on holdout set
• Deploy if performance meets thresholds

Results

Fraud Reduction

• 67% decrease in fraud losses ($2.8M saved annually)
• 43% reduction in false positive rate
• 85% precision on fraud detection
• 91% recall on high-value fraud

Performance

• <50ms p95 latency for scoring
• 100K+ transactions/day capacity
• 99.99% uptime over 6 months
• Zero customer-facing outages

Operational Impact

• Fraud review queue reduced by 60%
• Investigation time per case cut by 40%
• Customer friction complaints down 55%
• Regulatory compliance audit passed

Advanced Capabilities

1. Explainable Predictions

Implemented SHAP values for model explainability:

• Top contributing features for each decision
• Investigation team insights
• Regulatory compliance for model decisions

2. Adaptive Thresholds

Dynamic thresholding based on:

• Time of day (higher thresholds during peak hours)
• Transaction type (different profiles for P2P vs. merchant)
• User segment (new users vs. established)

3. Fraud Network Detection

Graph analytics to identify fraud rings:

• Shared devices across accounts
• Circular money flows
• Coordinated account creation patterns

Ongoing Innovation

Post-launch enhancements:

• Behavioral biometrics - Typing patterns and mouse movements
• Social network analysis - Connection patterns for mule account detection
• Advanced NLP - Transaction description analysis
• Federated learning - Privacy-preserving model training across institutions