Big Data & Catastrophes: 3 Important Numbers You Need to Know
Catastrophes, as defined in insurance, are events that affect multiple locations in a geographic area at once. Think of: tornadoes, winter storms, floods, severe wind, earthquakes and, in this day and age, terrorism.
Carriers used to be limited in how much value they could insure in any one geographic area based on historical data, reinsurance directives and, quite honestly, opinion. That was before big data and $13 billion in damage from Hurricane Andrew in 1992.
Catastrophe models were built to address the questions of: “How big will the hurricane be? How much damage can we expect from the Earthquake?” These models are fed information from property details such as: location, number of stories, square footage and how the building is constructed. The system takes the details and simulates natural catastrophes over time periods. The model then determines what type of losses you can expect for these various time periods.
Below is a sample output of one of these models called Risk Management Solutions (RMS).
The following are three factors that are important in understanding these models and their use:
- 250-Year Return – This is the period of time most insurance underwriters use to determine damage. To be more conservative, an insured may utilize the longer periods such as 1,000 years.
- Ground-Up Aggregate Exceedance Probability (GU AEP) – This number is used to make sure the limits set for the modeled peril is adequate. It’s also typically what lenders will accept to confirm limits are acceptable.
- Annual Average Loss (AAL) – This is the amount of premium considered for the catastrophe load and doesn’t include premium to cover other non-cat losses, expense or profit. Carriers will generally apply a factor to this amount to determine how they will charge.
By knowing these three factors, a real estate company can judge if their catastrophe limit is where it should be. They also have data to provide to their lender to support the limit and identify the amount of premium they should be charged for.
Big data at its best.
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