The CAT Modeling Code: A DOXA Perspective on CAT modeling and insurance risk management.

As storms grow more unpredictable, the insurance industry is reassessing how we analyze and underwrite property risks.

When the remnants of Hurricane Helene struck the Carolinas in September 2024, its devastation extended over 500 miles inland—well beyond the areas initially predicted to be at risk. Shortly after, Hurricane Milton made landfall with unprecedented force, causing both wind damage and widespread flooding that models hadn’t anticipated. California wildfires raged in unprecedented ways, causing massive devastation. These events underscore a growing reality: our current approach to risk modeling isn’t exactly working.

With climate change introducing new variables and unpredictable storm behaviors, traditional CAT models face limitations. Now is the time for a fresh perspective—one that blends data, human expertise, and collaboration to improve risk assessment and deliver stronger outcomes for clients.

DOXA’s team of experts explore the following topics:

1. What CAT Models Do Well—and Where They Fall Short
2. Words Matter: Defining Risks, Defining Coverage
3. The Power of Parametric Insurance
4. Strengthening Confidence in CAT Models

What CAT Models Do Well—and Where They Fall Short

CAT models are advanced tools designed to predict potential losses from catastrophic events like hurricanes, wildfires, and earthquakes. They rely on historical data, geospatial information, and simulations to help insurers assess risk and set premiums. However, these models are relatively new and continuously evolving. Their effectiveness depends on accurate inputs, and while they provide a strong foundation for decision-making, they can’t account for every real-world complexity.

What CAT Models Do Well:

• Pinpointing Loss Drivers: Identifies locations and exposures most likely to generate high losses.
• Aggregating Portfolio Risk: Helps insurers analyze cumulative exposure across portfolios.
• Scenario Simulations: Allows for preparation against extreme but plausible scenarios.
• Standardizing Risk Evaluation: Provides a consistent framework across different perils and geographies.

Words Matter: Why Definitions Make All the Difference in Coverage

In insurance, the way we define and categorize events directly affects whether a policy covers them—or doesn’t. As we continue to use CAT modeling as a tool, agreeing on language is extremely important, especially when it comes to complex, multi-layered risks.

Examples:

• Storm Surge vs. Flood vs. Named Windstorm: Coverage may vary depending on classification, impacting claim approvals.
• Mudslide vs. Flood vs. “Acts of Nature”: Different categorizations can lead to coverage denials.
• Tsunami: Earthquake or Flood?: Classification may depend on policy language, potentially leaving gaps in coverage.

The Power of Parametric Insurance

Ambiguity is the last thing anyone wants during a disaster. Parametric insurance eliminates the guesswork by offering clear, pre-defined payouts based on objective triggers—no loss adjustment process required.

How It Works:

• Defined Triggers: Coverage is activated if a specific event meets agreed-upon parameters.
• Customizable Coverage Area: Clients define the geographic boundary for protection.
• Pre-Set Payouts: Policyholders and capital providers determine the payout structure.
• Immediate Claims & Payments: Payouts occur once the trigger event happens, eliminating delays.

Strengthening Confidence in CAT Models

By integrating accurate data, advanced modeling, and expert insights, DOXA offers a comprehensive approach to risk management tailored to meet the challenges of an unpredictable world.

Step 1: Accurate Data Collection & Valuation

• Inputs: Construction materials, occupancy, property valuation.
• Outputs: Historical storm patterns, climate projections, geographic risk factors.

Step 2: CAT Model Data Analysis

• Probable Maximum Loss (PML): Largest estimated loss from a single insured event.
• Return Period: Frequency at which a specific PML event is expected.
• Maximum Foreseeable Loss (MFL): Worst-case financial loss estimate.
• Hazard Mapping: Visualizes potential risk zones.
• Event Triggers: Identifies overlapping hazards.
• Average Annual Loss (AAL): Dictates premium pricing.

Step 3: Expert Interpretation

• Carriers use CAT model outputs to assess portfolio risk, balance capacity, and manage aggregate limits.
• If a risk nears its aggregate limit, carriers may raise prices or decline coverage.

Bottom Line

CAT modeling remains an essential tool in the insurance industry, but it must evolve alongside the increasing unpredictability of natural disasters. DOXA is committed to refining and strengthening these models, incorporating both traditional methodologies and innovative solutions like parametric insurance to provide more reliable risk assessments and coverage solutions.

To learn more about how DOXA can help you navigate CAT modeling complexities, connect with our team today.