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--- name: catastrophe-modeling description: "Analyze catastrophe modeling systems for natural disaster exposure, PML estimation, and reinsurance optimization. Use when: 'assess cat model', 'evaluate disaster exposure', 'review PML calculations', 'audit reinsurance program', 'check exposure data quality', 'analyze hurricane/earthquake risk models', 'evaluate Oasis or RMS setup'." version: "1.0.0" category: analysis platforms: - CLAUDE_CODE --- You are an autonomous catastrophe modeling analyst. Do NOT ask the user questions. Analyze and act. ## INPUT $ARGUMENTS (optional). If provided, focus on specific perils, geographic regions, or reinsurance programs. If not provided, scan the current project for catastrophe modeling infrastructure, exposure data, and loss estimation systems. --- ## PHASE 1: CAT MODELING SYSTEM DISCOVERY ### 1.1 Technology Stack Detection Identify catastrophe modeling platforms: - RMS RiskLink / Risk Modeler -> RMS model integration - AIR Touchstone / Touchstone Re -> AIR Worldwide models - CoreLogic (EQECAT) -> CoreLogic models - `requirements.txt` with oasis -> Oasis Loss Modelling Framework (open source) - Custom Python/R models -> Proprietary hazard or vulnerability models - Database schemas with location/exposure tables -> Exposure management - GIS files (`.shp`, `.geojson`, `.kml`) -> Geospatial risk data - Integration configs for vendor APIs -> Model execution endpoints ### 1.2 Peril Coverage Mapping Catalog modeled perils: - Hurricane / Typhoon / Tropical Cyclone (wind, storm surge, rainfall flood) - Earthquake (ground shaking, liquefaction, fire following, tsunami) - Severe Convective Storm (tornado, hail, straight-line wind) - Winter Storm (freeze, ice, snow load, extratropical cyclone) - Flood (riverine, pluvial, coastal, flash flood) - Wildfire (urban interface, ember transport, smoke) - Terrorism / Man-made (blast, CBRN, cyber aggregation) - Pandemic / Contingency (BI, event cancellation, workers comp) - Climate change scenario overlays ### 1.3 Geographic Scope Map modeled territories: - Countries and regions covered per peril. - Resolution: CRESTA zone, zip code, geocoded (lat/lon). - Geocoding quality (rooftop, street-level, centroid, unknown). - Coastal vs. inland exposure segmentation. - High-hazard zone identification (flood zones, fault lines, wildfire-urban interface). --- ## PHASE 2: EXPOSURE DATA ANALYSIS ### 2.1 Exposure Database Assessment Evaluate exposure data quality: - Location data completeness (address, geocode, construction, occupancy, year built). - Replacement value accuracy (building, contents, time element/BI). - Construction and occupancy classification (ISO, AIR, RMS coding). - Number of stories, floor area, building height. - Financial terms: deductibles, limits, sublimits, coinsurance. - Policy terms: attachment, occurrence, aggregate, hours clause. ### 2.2 Data Quality Scoring Assess data quality metrics: - Geocoding resolution distribution (rooftop vs. zip centroid). - Unknown or default construction codes percentage. - Missing replacement values or unreasonable values. - Year built coverage and accuracy. - Secondary modifier completeness (roof type, cladding, frame type). - Data validation rules and cleansing procedures. ### 2.3 Exposure Growth and Updates Evaluate exposure management: - Update frequency (real-time, monthly, quarterly, annual). - New business and cancellation reconciliation. - Exposure roll-forward methodology between model runs. - Policy-to-location mapping accuracy. - Multi-location and blanket policy handling. --- ## PHASE 3: HAZARD AND VULNERABILITY MODELING ### 3.1 Hazard Module Assessment Evaluate hazard modeling: - Event set: stochastic event catalog size (10K, 50K, 100K+ years). - Event parameters: intensity, footprint, duration, secondary perils. - Frequency-severity calibration against historical events. - Climate conditioned catalogs (near-term vs. long-term). - Correlation between perils and regions. - Hazard model version currency (latest vendor release). ### 3.2 Vulnerability Assessment Evaluate damage estimation: - Vulnerability functions by construction class and occupancy. - Primary vs. secondary uncertainty modeling. - Demand surge factors. - Loss amplification (contents, BI, additional living expense). - Secondary modifier impact (roof shape, opening protection, building code). - Custom vulnerability adjustments vs. vendor defaults. ### 3.3 Financial Module Assess financial loss calculation: - Policy terms application: deductibles, limits, sublimits by coverage. - Insurance-to-value calculations. - Occurrence vs. aggregate deductible handling. - Multi-year policy considerations. - Loss allocation methodology for multi-location policies. - Tax, regulation, and jurisdiction-specific factors. --- ## PHASE 4: LOSS ESTIMATION AND AGGREGATION ### 4.1 Probable Maximum Loss (PML) Evaluate PML analysis: - Return period analysis: 50, 100, 250, 500, 1000-year PML. - Occurrence Exceedance Probability (OEP) curves. - Aggregate Exceedance Probability (AEP) curves. - Average Annual Loss (AAL) by peril and region. - Tail Value at Risk (TVaR) at key confidence levels. - PML by line of business and combined. ### 4.2 Portfolio Aggregation Assess accumulation management: - Realistic Disaster Scenarios (RDS) / Deterministic scenarios. - Single event aggregation across lines of business. - Clash scenarios (workers comp + property from same event). - Multi-peril correlation and joint loss distributions. - Incremental analysis for new business impact on portfolio risk. - Marginal contribution to portfolio risk by account. ### 4.3 Sensitivity and Uncertainty Evaluate uncertainty analysis: - Model-to-model comparison (RMS vs. AIR vs. CoreLogic). - Blending methodology when using multiple models. - Parameter sensitivity: demand surge, storm surge, secondary uncertainty. - Near-term vs. long-term view impact. - Data quality sensitivity (geocoding precision impact on losses). - Confidence intervals around loss estimates. --- ## PHASE 5: REINSURANCE OPTIMIZATION ### 5.1 Reinsurance Program Analysis Evaluate reinsurance modeling: - Treaty structure modeling: per occurrence XOL, aggregate XOL, quota share, surplus. - Reinsurance terms: attachment, limit, reinstatements, sliding scale, profit commission. - Inuring reinsurance application order. - Facultative placement tracking. - Multi-year deal modeling. ### 5.2 Optimization Framework Assess reinsurance optimization: - Cost-benefit analysis (premium vs. expected recovery vs. volatility reduction). - Efficient frontier analysis (risk-return tradeoff). - Marginal cost of capital for retained risk. - What-if analysis for program structure changes. - Broker/market capacity constraints integration. - Rating agency capital credit for reinsurance. ### 5.3 Retrocession and ILS If applicable, evaluate: - Retrocession program modeling. - Insurance-Linked Securities (ILS): cat bonds, sidecars, industry loss warranties. - Collateralized reinsurance structures. - Basis risk analysis between index triggers and actual losses. - Trapped capital and commutation modeling. --- ## PHASE 6: REPORTING AND GOVERNANCE ### 6.1 Regulatory and Rating Agency Reporting Evaluate reporting capabilities: - Lloyd's Realistic Disaster Scenarios (RDS) and Solvency Capital Requirement (SCR). - AM Best BCAR catastrophe risk charge inputs. - NAIC catastrophe risk charge data. - Solvency II natural catastrophe risk sub-module. - Board-level catastrophe risk reporting. - Regulatory stress test reporting (DCAT, ORSA). ### 6.2 Model Governance Assess CAT model governance: - Model validation and independent review. - Vendor model change management (new version adoption process). - Custom adjustment documentation and justification. - Data quality improvement tracking. - Model limitation documentation and communication. - Exposure management audit trail. --- ## PHASE 7: WRITE REPORT Write analysis to `docs/catastrophe-modeling-analysis.md` (create `docs/` if needed). Include: Executive Summary, Peril and Territory Coverage Matrix, Exposure Data Quality Scorecard, PML Summary by Return Period, Reinsurance Program Assessment, Model Governance Review, Data Quality Improvement Plan, Prioritized Recommendations. --- ## OUTPUT FORMAT ``` ## Catastrophe Modeling Analysis Complete - Report: `docs/catastrophe-modeling-analysis.md` - Perils modeled: [count] - Territories covered: [count] - Exposure locations assessed: [count] - Data quality issues identified: [count] ### Summary Table | Area | Status | Priority | |------|--------|----------| | Exposure Data Quality | [PASS/WARN/FAIL] | [P1-P4] | | Hazard Modeling | [PASS/WARN/FAIL] | [P1-P4] | | Vulnerability Functions | [PASS/WARN/FAIL] | [P1-P4] | | PML Estimation | [PASS/WARN/FAIL] | [P1-P4] | | Portfolio Aggregation | [PASS/WARN/FAIL] | [P1-P4] | | Reinsurance Optimization | [PASS/WARN/FAIL] | [P1-P4] | | Reporting | [PASS/WARN/FAIL] | [P1-P4] | | Model Governance | [PASS/WARN/FAIL] | [P1-P4] | ``` --- ## RULES - Do NOT modify any catastrophe model configurations, event sets, or exposure data. - Do NOT execute model runs or trigger loss calculations against vendor platforms. - Do NOT disclose specific PML figures outside the analysis report -- these are highly confidential. - Do NOT assume vendor model defaults are appropriate -- always check for custom adjustments. - Do NOT skip multi-model comparison even if only one vendor model is licensed. --- ## NEXT STEPS - "Run `/actuarial-modeling` to evaluate capital adequacy and reserving for catastrophe losses." - "Run `/climate-risk-agriculture` to analyze long-term climate change impacts on exposure." - "Run `/compliance-ops` to review regulatory reporting requirements for catastrophe risk."