Navigating the Storm: Why Traditional Approaches Fall Short Against Severe Convective Storms

The insurance industry is facing a critical challenge: Severe Convective Storms (SCS) are becoming more frequent, more costly, and harder to model than ever before. Characterized by hail, straight-line winds, and tornadoes, SCS events drive significant insured losses, with Aon estimating that 70% of global insured losses have been driven by severe convective storms in recent years. These high-frequency events exhibit complex atmospheric interactions and non-linear impacts that vary dramatically across space and time, presenting unique challenges to exposure managers and underwriters alike.

Traditional SCS models often fall short in capturing the full extent of this risk. Despite the growing exposure and rising loss trends, adoption of probabilistic models remains limited, with many in the industry relying on historical actuarial methods. But this backward-looking approach is increasingly risky, especially as development pushes into previously unexposed areas where little to no claims history exists. In short, the risk is growing and traditional methods aren’t keeping up.

At KatRisk, we believe the future demands more: higher fidelity models, transparent methodologies, and scientifically sound frameworks built for where risk is going, not where it’s been.

Why Current SCS Models Fall Short

1. Underestimation of Risk at Both AAL and PML Levels: Traditional models often underestimate both average annual loss (AAL) and probable maximum loss (PML). This can lead to mispriced risk, insufficient capital allocation, and surprise losses that undermine confidence and profitability. As climate variability increases and severe weather pushes into new regions, relying on historic baselines is no longer enough.
   
2. Lack of Inter-Peril Correlation: Many models treat SCS sub-perils, hail, wind, tornado, as isolated hazards. But in the real world, these perils often occur together in correlated events. Ignoring this correlation results in underrepresented tail losses and mischaracterized event-level losses. Without capturing how hazards cluster and interact, insurers may underestimate the true scale and financial impact of major storm systems.
   
3. Low-Resolution Peril Footprints: A common limitation of traditional SCS models is coarse resolution footprints. These low-resolution outputs tend to dilute the extremes: underestimating losses where destruction is most intense (like a direct tornado strike on a commercial facility), and overestimating losses at the edges of events where the impact is typically lower. This skews risk assessments and hampers underwriting precision.
   
4. Bias in Historical Data: Historical SCS data is often biased by population density, reporting inconsistencies, and limitations in radar coverage. This introduces geographic noise and distorts risk signals. For example, two neighboring counties in Texas might show radically different hail frequencies, not because the storms are different, but because one area has more people to report them. Basing future loss expectations on biased data is a recipe for surprise.
   
5. Validation Gaps: Many models lack sufficient validation at local or event-level scales. This can result in outputs that look reasonable in aggregate but fall apart when applied to real-world scenarios. Without rigorous validation, it’s difficult to trust the model’s performance, especially when venturing into new or high-growth geographies.
   

KatRisk’s Modeling Advantage: Stochastic Power at Unmatched Resolution

KatRisk built its SCS model from the ground up to address these problems. Notably faster than the competition, our SCS model is fully customizable and transparent, providing clients with more reliable risk assessments and enabling precise tailoring to specific needs.

• High-Resolution: Hail and straight-line wind are meticulously modeled at a 1-km resolution and tornado at 100-m, enabling the capture of strong gradients in intensity across challenging terrains, best in class for the industry. 
• 50,000+ Simulated Years: For each sub-peril, we simulate tens of thousands of years of meteorologically accurate scenarios (and 2.5 million years of simulations for Tornado). This enables convergence and robust tail risk estimates that traditional models simply can’t achieve.
  Global correlation between perils and regions: KatRisk’s inclusion of intra-peril correlation accounts for the overlapping presence of perils and avoids double counting, ultimately leading to better estimates of loss distribution tails.
• Customizable vulnerability factors: Our SCS model enables clients to fully customize vulnerability factors, allowing for precise calculations of average annual loss for specific locations and ensuring more accurate risk assessments.

Extensive Validation to Build Trust

KatRisk’s SCS model isn’t just scientifically rigorous, it’s extensively validated. We validate stochastic event footprints and financial outputs across state, county, and city levels. We’ve also tested our outputs against high-quality loss data, reinsurance reports, and claims data provided by multiple clients.

This validation ensures our clients can trust the model’s representation of both frequency and severity, and use it to make smarter underwriting and portfolio management decisions.

Real-World Impact

KatRisk’s SCS model allows insurers to:

• Identify and correct mispriced risks
  
• Improve pricing tables and rating plans
  
• Optimize policy structures
  
• Reduce surprise losses and improve resilience
  
• Expand into new regions with less historical exposure
  
• Secure better reinsurance pricing
  

We also help answer the key questions that drive smarter decisions:

• What is the likelihood of a tornado hitting a specific location?
  
• How rare was a past event like Oklahoma’s 152 tornadoes in 2024?
  
• What does a truly diversified SCS portfolio look like?
  
• How does current exposure vary by region and how should you respond?
  

At KatRisk, we go beyond just providing a model. We deliver the tools and insights our clients need to navigate volatility and grow with confidence. If your team is ready to rethink how you model and manage SCS risk, we’d love to show you what’s possible. Contact us today.