Implement Customer Behavioral Feature Store for Real-Time BNPL Risk Assessment

[whitehackr]

Customer Behavioral Feature Store Implementation

Problem Statement

Current BNPL risk prediction relies only on transaction-time features, missing critical customer behavioral patterns that could significantly improve model performance. Historical customer aggregations (transaction frequency, spending volatility, category preferences) cannot be computed in real-time due to <100ms latency requirements.

Proposed Solution

Implement a feature store architecture with daily batch processing and Redis-backed real-time serving to provide customer behavioral features with <1ms lookup latency.

Technical Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Transaction   │    │   Daily Batch    │    │     Redis       │
│     Stream      │───▶│   Processing     │───▶│  Feature Store  │
│                 │    │   (Airflow)      │    │                 │
└─────────────────┘    └──────────────────┘    └─────────────────┘
                                │                        │
                                │                        │ <1ms lookup
                                ▼                        ▼
                       ┌──────────────────┐    ┌─────────────────┐
                       │   BigQuery DWH   │    │   Real-time     │
                       │  (Historical)    │    │   ML Serving    │
                       └──────────────────┘    └─────────────────┘

Implementation Details

1. Customer Feature Categories

A. Transaction Behavioral Features

• Volume patterns: transaction count, amounts, volatility
• Temporal patterns: weekend ratios, time between transactions
• Category preferences: diversity scores, risk ratios
• Device behavior: consistency, trust ratios

B. Risk Evolution Features

• Trend analysis: spending/risk trends
• Recency features: days since last transaction
• Customer lifecycle stage

2. Data Pipeline Architecture

Daily Batch Processing (Airflow DAG)

• Extract customer behavioral features from BigQuery
• Compute 30-day rolling aggregations
• Update Redis feature store with TTL management

Redis Feature Store Integration

• <1ms customer feature lookup
• Automatic TTL-based cleanup
• Graceful fallback for missing customers

3. Real-Time ML Integration

Enhanced prediction pipeline combining:

• Transaction-time features (fast)
• Customer behavioral features (Redis lookup)
• Fallback to transaction-only for new customers

Performance Requirements

Latency Targets

• Feature Lookup: <1ms (Redis GET operations)
• End-to-end Prediction: <100ms (including feature lookup)
• Batch Processing: Complete within 4-hour window (2 AM - 6 AM)

Scalability Requirements

• Customer Volume: Support 10M+ active customers
• Feature Updates: Handle 1M+ daily customer feature updates
• Query Volume: 100K+ predictions per minute during peak traffic

Implementation Phases

Phase 1: Foundation (Sprint 1-2)

• Design Redis schema and data structures
• Implement basic CustomerFeatureStore class
• Create initial Airflow DAG for feature extraction
• Set up Redis cluster with proper configuration

Phase 2: Core Features (Sprint 3-4)

• Implement full customer behavioral feature set
• Add feature versioning and backward compatibility
• Create monitoring and alerting infrastructure
• Load test Redis performance under production volume

Phase 3: Production Integration (Sprint 5-6)

• Integrate feature store with ML serving pipeline
• Implement graceful fallback for missing features
• Add A/B testing framework for model versions
• Create feature store admin tools and dashboards

Phase 4: Advanced Features (Sprint 7-8)

• Implement real-time feature updates via streaming
• Add feature drift detection and auto-retraining triggers
• Create customer segment-specific feature sets
• Optimize memory usage with feature compression

Success Metrics

Business Impact

• Model Performance: Improve discrimination ratio from 3.5x to >4.0x
• Precision: Increase high-risk precision from 35% to >45%
• Coverage: Maintain approval rates while reducing default rates

Technical Performance

• Latency: Maintain <100ms end-to-end prediction latency
• Availability: Achieve >99.9% feature store uptime
• Cost: Keep Redis infrastructure costs <$5K/month

Risk Assessment

Technical Risks

• Redis Memory Limits: Monitor for OOM conditions with large feature sets
• Network Latency: Ensure Redis cluster co-location with ML serving
• Feature Staleness: Handle customer behavior changes between updates

Mitigation Strategies

• Implement feature compression and TTL-based cleanup
• Use Redis clustering and replication for high availability
• Create fallback to transaction-only model for missing features
• Monitor feature drift and model performance continuously

Dependencies

• Redis cluster setup (Infrastructure team)
• Airflow DAG deployment pipeline (Platform team)
• BigQuery access permissions (Data team)
• ML model retraining pipeline (ML Engineering team)