Feedback Loop
Continuous learning and model improvement
OverviewData & FeaturesModels & AlgorithmsDecisioning PipelineReal-time ArchitectureExplainabilityFeedback LoopMetrics & Impact
Closed-Loop Learning System
Quote
- Carrier selected
- Premium quoted
- Coverage options
Bind
- Win/loss outcome
- Final premium
- Time to bind
Renewal
- Retained/churned
- Price change
- Coverage changes
Feedback
- Agent override reasons
- Customer satisfaction
- Claims data
Retrain
- Updated model
- New feature weights
- Improved accuracy
Continuous improvement cycle
Model Drift Detection
Monitor prediction accuracy over time to detect when the model needs retraining.
Accuracy trendStable
Current: 87% | Threshold: 80%
Retraining Schedule
ScheduledWeekly
Trigger-basedOn drift
Last retrain3 days ago
Drift Alerts
Conversion model: Stable
Retention model: Stable
Price model: Monitor
Experimentation Framework
A/B Testing
Compare model versions with controlled traffic splits.
Control (v2.3)50%
Treatment (v2.4)50%
Multi-Armed Bandits
Dynamically allocate traffic to better-performing variants.
Exploration10%
Exploitation90%
Holdout Groups
Measure long-term impact with persistent control groups.
Holdout size5%
Duration90 days
Compounding Intelligence
Every quote, bind, and renewal feeds back into the system. This creates a data flywheel that continuously improves prediction accuracy—a competitive advantage that compounds over time and becomes increasingly difficult for competitors to replicate.