Are catastrophe models misunderstood?

Expert addresses common misconceptions by brokers

Are catastrophe models misunderstood?

Catastrophe & Flood

By Gia Snape

Extreme weather events have driven billions of dollars in losses for the insurance industry. Amid one of the busiest hurricane seasons in the Atlantic, there are concerns that another major catastrophic event could plunge the property market into chaos.

One of the common scapegoats for the property hard market is catastrophe models. While they provide insurers with an analytical means of assessing risk, overreliance on cat models can cause underwriters to overlook unique, property-specific risk factors that these assessments may not capture.

However, one expert is seeking to clarify the misconception about cat models. “The model is the starting point for [carrier pricing],” explained Bruce Norris (pictured), EVP – National Property Practice at Jencap Group. Norris noted that while models provide a foundation for risk pricing, real-world conditions and constraints significantly impact final pricing decisions.

“The carrier is aggregating the capacity they have in an area,” he said. “Let’s say you got $100 million to sell in a certain county or zip code, and they’re at $95 million because the values are going up constantly, they are going to increase their price.”

Why are catastrophe models misunderstood?

Catastrophe models provide insurers with a sophisticated means of assessing risk. By simulating various disaster scenarios, these models enable insurers to estimate potential losses more accurately.

This enhanced risk assessment allows insurers to set premiums that more accurately reflect the risk profile of the properties they cover, ensuring financial stability and protecting against insolvency.

But despite their sophistication, catastrophe models are not infallible. They rely on historical data and assumptions, which may not accurately predict future events.

Uncertainties in model inputs, such as climate change and evolving land use patterns, can lead to significant discrepancies between predicted and actual losses. This uncertainty can result in either overestimating or underestimating risk, impacting premium pricing.

But how influential are cat models in carriers’ pricing decisions? Norris said that while models provide a baseline, insurers adjust them based on their understanding of the market and the risks.

“Carriers can override certain characteristics in the model, too,” said Norris.

Factors influencing cat models and premium pricing

A critical factor in this pricing equation is the balance of supply and demand. When carriers approach their capacity limits, they adjust prices upwards, regardless of what the models suggest.

“The model doesn’t have a bearing on the price at that point,” said Norris. “They just need to have a minimum premium to offer.”

Other factors, such as cost of capital and reinsurance costs, also impact carriers’ financial health and capacity and, therefore, affect pricing decisions.

Data quality is crucial when it comes to cat models. Norris said that the industry is moving towards better data quality to assess increasingly complex and volatile catastrophe risks. He also highlighted the importance of secondary characteristics in data, which can impact model outcomes and how risks are perceived and priced.

Why should brokers care about cat models?

By understanding the model’s assumptions and results, brokers can better prepare for discussions with carriers.

“If you can leverage a cat model to understand the risk, kind of ‘pre-underwriting’ before you go to the underwriting community, you’re in a better position to negotiate,” said Norris.

Brokers also increasingly use models for their assessments, helping clients manage and understand their risks. Norris shared some advice: “The primary way to leverage catastrophe modeling is to ensure your data is correct. If you send incorrect data to a carrier, it undermines trust and leads the carrier to adopt a more conservative pricing approach.

“Always verify your data using available online tools. Additionally, including secondary characteristics in your data can significantly improve the model’s accuracy. When discussing discrepancies with the carrier, you can highlight the secondary characteristics used to explain differences.

“Don’t send data blindly; use the model to anticipate the carrier’s perspective.”

What are your thoughts on catastrophe models? Do you have something to say about this topic? Please leave a comment below.

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