Insurance Integration Guide
TODO: Complete Integration Guide for Insurance & Risk Mitigation Protocols
How to integrate EigenWatch into insurance and risk protocols.
Required Sections
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Why Insurance Protocols Use EigenWatch
- Operator risk scoring for premium pricing
- Slashing event detection for claims
- Underwriting data for policy decisions
- Real-time monitoring of insured positions
- Historical data for reserving models
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Premium Calculation
- Risk score to premium multiplier
- Examples: Low-risk operator pricing vs high-risk
- Dynamic premium adjustment
- Batch pricing for operator portfolios
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Example Premium Model
from eigenwatch import EigenWatchAPI
client = EigenWatchAPI(api_key="...")
def calculate_premium(operator_address, annual_coverage):
risk = client.get_risk_score(operator_address)
# Base premium 1% of coverage
base_premium = annual_coverage * 0.01
# Risk multiplier
multiplier = 1 + (risk['risk_score'] / 100)
return base_premium * multiplier -
Claims Workflow
- Detecting slashing events
- Verifying slashing occurred
- Claim settlement
- Payout automation
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Real-Time Monitoring
- Subscribe to slashing alerts
- Risk score threshold alerts
- Portfolio composition changes
- Incident response automation
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Data Integration for Underwriting
- Historical slashing data
- Operator reputation research
- Correlation analysis
- Reserve calculations
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Smart Contract Integration
- Oracle feeds for automated premiums
- Automated claims on slashing detection
- Dynamic coverage limits by risk
- Multi-sig dispute resolution
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Testing & Rollout
- Testnet integration
- Beta coverage periods
- Monitoring & tuning
- Production deployment
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Support & Escalation
- Data disputes
- Edge cases in slashing detection
- Manual claim review
Use Cases
- Insurance covering operator slashing losses
- Risk-based premium adjustment in real-time
- Automated claims on confirmed slashing
- Portfolio insurance with dynamic pricing
Related
Status: NOT STARTED — Requires insurance product design