Catastrophe Model Validator
Design rigorous catastrophe model validation matrices and detect anomalies systematically
What You Can Do
You can rapidly construct comprehensive validation test matrices for catastrophe models, then systematically interpret model outputs to identify statistical anomalies, edge cases, and performance gaps. Claude analyzes model behavior across stress scenarios, generates structured reports documenting limitations, and provides evidence-based recommendations for model refinement or retirement. Every validation deliverable is audit-ready and supports regulatory compliance workflows.
Features
Automatically generate multi-dimensional validation scenarios covering frequency, severity, correlation, and spatial aggregation dimensions. Claude designs test cases that expose edge behaviors without manual enumeration.
Identify statistical outliers, non-monotonic behavior, and unexpected correlations in model outputs. Flags results that violate domain assumptions or historical calibration bounds.
Create structured, evidence-based documentation of model constraints—data dependencies, temporal boundaries, geographic limits, peril gaps. Output is compliance-ready.
Design boundary condition scenarios (extreme frequency, tail correlation, missing perils, data gaps) and interpret how models respond under stress.
Compare model output distributions against baseline assumptions, historical performance, and peer benchmarks. Quantifies deviation significance.
Highlight coverage gaps—missing peril combinations, inadequate correlation treatment, unvalidated exposure classes—with quantified impact estimates.
Produce audit-trail formatted reports with executive summary, detailed findings, limitation inventory, and sign-off artifacts for compliance and governance.
Example Output
Validation Test Matrix (Example Output)
| Scenario | Peril Frequency | Severity Distribution | Geographic Correlation | Expected Output Range | ✓/✗ | Anomaly Flag |
|---|---|---|---|---|---|---|
| Base Case | Historical | Empirical | Calibrated | $45B–$52B | ✓ | None |
| Extreme Freq | 3σ above mean | Empirical | Calibrated | $78B–$95B | ✓ | None |
| Tail Corr | Historical | Empirical | 0.95 (max) | $62B–$71B | ✗ | Output skews low—correlation multiplier under-applied |
| Missing Peril | Excl. Wildfire | Empirical | Calibrated | $38B–$44B | ✓ | None |
| Data Gap | 1995–2005 excluded | Empirical | Calibrated | $41B–$48B | ✗ | 15% upward bias—calibration period loss bias |
Identified Limitations (Structured Excerpt)
- Peril Gap: Wildfire module unavailable for States CA, OR, WA. Impact: ~8% underestimate of total portfolio loss in Western exposure.
- Temporal Constraint: Calibration uses 1950–2022 data; tail behavior pre-1960 extrapolated via regression. Confidence: Medium.
- Correlation Limit: Frequency–severity correlation assumed independent across perils; observed clustering in hurricanes + surge not captured. Max downside: 12% underestimation in Gulf Coast concentrated portfolios.
Validation Sign-Off
✓ Model fit for deployment pending peril gap remediation. ✗ Geographic carve-out required for CA/OR/WA until wildfire module completed. ⚠ Recommend annual recalibration or data refresh if 2024+ loss experience deviates >5% from forecast.
What's Included
- Validation Test Matrix Templates: Pre-built scenario frameworks covering frequency shocks, severity scaling, correlation stress, and geographic sub-portfolio tests tailored to catastrophe model structure.
- Anomaly Detection Ruleset: Statistical rules for identifying non-monotonic outputs, unexpected correlation flips, regime breaks, and outliers beyond calibration confidence bounds.
- Limitation Documentation Framework: Structured checklist for peril coverage, temporal boundaries, geographic scope, data dependencies, validation confidence levels, and remediation paths.
- Validation Report Templates: Audit-trail formatted templates for executive summary, detailed findings tables, limitation inventory with impact quantification, and sign-off sections.
- Stress Test Scenario Library: Industry-standard edge cases: 1-in-1000 frequency events, zero correlation (independent perils), full correlation (system risk), data truncation, and missing exposure classes.
- Compliance & Governance Checklists: Pre-deployment validation steps, regulatory requirements (Solvency II, ORM, SEC disclosure), peer review prompts, and sign-off documentation.
Who It's For
- Actuaries & Model Validators—designing and executing rigorous model validation workflows
- Risk Management Professionals—documenting model fitness for decision-making and capital allocation
- Insurance Data Scientists—stress-testing models and identifying behavioral anomalies
- Model Risk Officers—preparing governance documentation and compliance artifacts
- Regulatory Compliance Teams—building audit trails and demonstrating model limitation transparency
Best For
- Pre-deployment model validation and fitness certification
- Regulatory model documentation and disclosure preparation
- Model governance assessments and control frameworks
- Stress testing and scenario analysis under extreme conditions
- Model limitation identification and remediation planning
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