Early Arrival of Hurricane Season Means It’s Time to Check Your CAT Model

Although hurricane season doesn’t officially start until June 1, it arrived early this year when Tropical Storm Ana formed in early May. The early formation of Ana and the devastating tornadoes across the Plains and Midwest are a sobering reminder of the need to take natural catastrophes seriously.

You likely depend on property and other insurance to mitigate potential losses from catastrophic events. In the 10 years since Hurricane Katrina, catastrophe (CAT) models have become key components of property insurance underwriting. They are an important factor in your ability to obtain the most effective coverage. But the models are only as good as the information you put into them. To run effectively high-quality data is a must.

Data Must Be Accurate and Complete

If the data you enter into your CAT model is incomplete or inaccurate, you may end up with higher loss estimates, which can translate into increased premiums and less capacity for an individual risk. CAT models are sensitive to the uncertainty poor or missing data can create, so bad information can result in an increase in the base loss projection and the variability associated with the modeled loss events.

Conversely, better data can result in significant premium savings. Because if you can improve the quality of the data in your CAT model, you may be able to produce better quantification and qualification of the risk being considered by underwriters.

A thorough review of CAT data quality can:

• Reduce loss estimates due to inaccuracies in the original data.
• Increase model accuracy.
• Decrease model uncertainty.
• Better inform underwriters.

Below Average Number of Hurricanes Expected This Season

Despite predictions for a below-average number of hurricanes in 2015, Tropical Storm Ana tells us the season has arrived. And it takes only one land-falling hurricane to cause significant property damage. Validated CAT model data can help you to properly and efficiently insure your company in the event of a storm.