Data & Feature Engineering
Data sources, transformations, and engineered features powering the AI engine
OverviewData & FeaturesModels & AlgorithmsDecisioning PipelineReal-time ArchitectureExplainabilityFeedback LoopMetrics & Impact
Data Sources
Customer Profile
- Demographics (age, occupation)
- Location (state, zip, urban/rural)
- Contact preferences
- Household composition
Risk Attributes
- Vehicle details (make, model, year)
- Property characteristics
- Business operations (for commercial)
- Prior claims history
Coverage Inputs
- Requested limits
- Deductible preferences
- Endorsements needed
- Multi-policy bundles
Historical Quote Data
- Price by carrier
- Quote timestamps
- Competitive positioning
- Win/loss outcomes
Carrier Metadata
- Appetite rules
- Underwriting guidelines
- Commission rates
- Response time SLAs
External Enrichment
- Property data (hazard scores)
- Driving records (MVR)
- Geospatial risk indices
- Weather/catastrophe exposure
Payment Behavior
- On-time payment history
- Autopay enrollment
- Premium financing usage
- Payment method preferences
Engineered Features
Price Competitiveness Index
How competitive is this carrier's price vs. market average for this risk profile
(market_avg_price - carrier_price) / market_avg_price
Carrier Win Rate
Historical win rate for similar risk profiles with this carrier
binds_similar_profiles / quotes_similar_profiles
Agent Performance Score
Agent-specific conversion rate with this carrier
agent_binds_carrier / agent_quotes_carrier
Risk Category Embedding
Vector representation of risk characteristics for similarity matching
embedding(risk_features)
Customer LTV Proxy
Estimated lifetime value based on profile and policy type
premium * expected_tenure * (1 - churn_prob)
Payment Reliability Score
Likelihood of consistent, on-time payments
weighted_avg(payment_history, credit_proxy)
Retention Affinity
Predicted renewal likelihood with this carrier
retention_model(profile, carrier)
Cross-sell Potential
Opportunity for additional policy sales
gap_analysis(current_coverage, optimal_coverage)
Data Flow Pipeline
1
Raw Data
Multiple sources
2
Ingestion
ETL & validation
3
Feature Store
Computed features
4
Model Input
Feature vectors
5
Prediction
Ranked output