analyzing-catastrophe-risk

name	analyzing-catastrophe-risk
description	Structures catastrophe risk assessment with model output interpretation and accumulation monitoring. Use when analyzing cat risk, interpreting cat model results, or managing cat exposure.
tags	["analysis","insurance","risk"]
metadata	{"author":"casemark","practice_areas":["Insurance","Actuarial Science","Reinsurance"],"document_types":["Analysis Report"],"skill_modes":["Analysis"]}

Analyzing Catastrophe Risk

Structures catastrophe risk assessment with model output interpretation and accumulation monitoring.

When To Use

• Evaluating portfolio exposure to natural catastrophe perils (hurricane, earthquake, flood, wildfire, severe convective storm)
• Interpreting output from vendor cat models (AIR, RMS, CoreLogic) for underwriting or reinsurance placement decisions
• Monitoring aggregate accumulations against defined tolerance limits or PML thresholds
• Preparing cat risk reports for reinsurance renewals, rating agency reviews, or board risk committees
• Assessing adequacy of cat reinsurance programs relative to modeled loss distributions

Inputs To Gather

• Exposure data: SOV (statement of values) or policy-level TIV schedules with geocoded locations, construction type, occupancy, year built, and number of stories
• Cat model output: EP (exceedance probability) curves, AEP/OEP tables, AAL (average annual loss), standard deviation, and event loss tables from one or more vendor models
• Accumulation data: Current aggregate exposures by peril, geography (CRESTA zone, county, state), and line of business
• Reinsurance structure: Treaty terms including attachment points, limits, co-participation, reinstatement provisions, and cascading layers
• Risk appetite parameters: Board-approved PML tolerances (e.g., 1-in-100 OEP net of reinsurance ≤ X% of surplus), concentration limits by zone
• Historical loss experience: Prior catastrophe claims data by event, including gross/ceded/net splits

Workflow

1. Validate exposure data quality

   • Check geocoding hit rates — flag portfolios with >5% county-level or worse resolution
   • Confirm TIV completeness: replacement cost vs. actual cash value, inclusion of business interruption and extra expense
   • Identify secondary modifiers: roof type, cladding, roof-to-wall connection [VERIFY against model-specific vulnerability requirements]
   • Reconcile SOV totals against in-force premium system

2. Run and interpret cat model output

   • Compare results across available vendor models (AIR Touchstone, RMS RiskLink/Intelligent Risk Platform, CoreLogic) — note model vintage and version
   • Extract key metrics at required return periods: AAL, 1-in-50, 1-in-100, 1-in-250 OEP and AEP, both gross and net of reinsurance
   • Decompose losses by peril, sub-peril (e.g., wind vs. storm surge for hurricane), and geography
   • Evaluate demand surge, loss amplification, and secondary uncertainty assumptions
   • Identify tail risk: review coefficient of variation and shape of EP curve beyond 1-in-250

3. Assess accumulation exposure

   • Map aggregate TIV by CRESTA zone, county, and custom-defined accumulation zones
   • Compare current accumulations against tolerance limits — highlight breaches or near-breaches
   • Evaluate clash potential across lines (property, auto physical damage, workers' comp from single event)
   • Test for concentration risk: percentage of total portfolio TIV within hurricane/earthquake wind speed or shaking intensity contours

4. Evaluate reinsurance program adequacy

   • Model net loss position after applying treaty structure layer by layer
   • Stress-test against historical benchmark events (e.g., Andrew, Katrina, Northridge, Joplin) and synthetic scenarios
   • Calculate expected recoveries, reinstatement costs, and residual net exposure above program exhaustion
   • Assess cost-efficiency: rate-on-line, payback period, ROL index relative to modeled expected loss [VERIFY current market benchmarks]

5. Compile risk assessment report

   • Summarize key findings with quantified metrics (not qualitative generalities)
   • Present modeled results in tabular and graphical format (EP curves, geographic heat maps, waterfall charts showing gross-to-net)
   • Highlight model divergence where vendor outputs differ materially (>15% at key return periods)
   • State all material assumptions: demand surge on/off, storm surge inclusion, fire-following earthquake, secondary uncertainty treatment

Output

• Executive summary: Portfolio AAL, key return period PMLs (gross/net), accumulation status vs. limits, and top 3 risk concerns
• Detailed EP curve analysis: Tabular AEP and OEP results at standard return periods with year-over-year comparison
• Accumulation dashboard: Geographic concentration by peril zone with breach/headroom indicators
• Reinsurance adequacy assessment: Program performance under modeled and historical scenarios, coverage gap analysis
• Model comparison matrix: Side-by-side vendor results with commentary on drivers of divergence
• Recommendations: Prioritized action items with estimated risk reduction impact

Quality Checks

• Confirm EP curve results are monotonically increasing (higher return period = higher loss) — non-monotonic results indicate data or modeling errors
• Verify AAL × multiplier reasonableness against market loss cost benchmarks [VERIFY against current industry loss ratios by peril/region]
• Cross-check net results against reinsurance treaty terms — ensure attachment, limit, and co-participation are correctly modeled
• Validate that all material perils are included (do not overlook flood in hurricane zones or fire-following in earthquake zones)
• Ensure exposure data vintage matches the effective period under analysis — stale SOVs produce misleading results
• Confirm that model settings (e.g., near-term vs. long-term hurricane view, warm SST assumptions) align with the company's stated risk philosophy
• Flag any use of flat rates or judgment-based overrides to modeled output — document rationale