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Catastrophe Modelers Credit Hurricane Andrew with Jump-Starting Business

AIR Worldwide's insurer clients didn't believe the company when it reported its first loss estimates from Hurricane Andrew. Now, catastrophe risk models are integrated across the enterprise — some might argue, too integrated.

Karen Clark started the company now known as AIR Worldwide in 1987 after falling in love with the concept of measuring exposure risk. Today, she's still in the business, running a consultancy that bears her name, counseling insurance companies on how to best interpret their CAT model data. Hurricane Andrew, she says, was a tipping point in the respect insurers gave to catastrophe risk models.

"Catastrophe modeling was a very young industry when Andrew hit," Clark says. "Before Andrew we had about 30 clients. But Andrew really changed the emphasis on exposure data."

The U.S. was in a bit of a lull when it came to hurricanes at that point in time, Clark adds. There hadn't been a major landfall in about two decades. Further, Hurricane Hugo's final tally of $4 billion in insured losses after landing near Charleston, S.C. gave insurers a somewhat false sense of their coastal risks. They viewed Charleston as enough of a "major metropolis" to base future risks on.

"Somehow people got in their mind that $7 billion was the maximum" amount of losses insurers could expect from a hurricane, she says. For Andrew, "We were getting loss numbers as high as $13 billion. It took about nine months afterward for that to sink in."

Total insured losses ended up being about $15.5 billion.

"There were 11 insurance companies that became insolvent back in 1992, says Dr. Jayanta Guin, who is the current SVP of research and modeling at Boston-based AIR Worldwide. "After that, the industry really began seeing the value of computer-based models."

If the same event happened today, Guin says, the industry could expect $57 billion in total insured losses, taking into account the amount of property that's been built up in the affected area. Karen Clark & Co. has looked at more historical hurricanes and noted that, for example, the 1926 Great Miami Hurricane would lead to a whopping estimated $125 billion industry loss today.

Modeling technologies, however, have led to better capital management and underwriting practices in the P&C insurance industry and it's unlikely that there would be widespread failures of insurance companies if these types of events were to occur, Guin says. He points to the fact that insurers came through battered but unbroken from such events as Hurricane Katrina and the Tohoku Earthquake as evidence of that positive influence.

"Companies have the tools now with the cat models to anticipate the likelihood of such losses, and can therefore better manage their capital," Guin says. "Last year, for the first time the industry had more than $100 billion in loss worldwide, and managed it quite well."

Catastrophe modeling revolutionized how insurers use technology to write business in the wake of the devastating disaster, Clark says. For some underwriters, this might have been the first computer-based tool they used, she explains. And, modeling greatly influenced the rise of data and its related infrastructure in the insurance enterprise, as insurers began collecting information on things like coastal property values and passing that on to their reinsurers -- who now demanded a higher level of detail.

In fact, Clark says that now models are in danger of becoming overused. As companies look to run increasingly tighter ships and manage their risk as effectively as possible, there's been more and more focus on point estimates that have "a tremendous amount of uncertainty," she explains. That means when there's a change to a model in a new version, business plans can be disrupted.

"One thing that companies have started doing, which doesn't work, is going all the way down to individual policy underwriting, especially focusing on average annual losses for commercial books," she adds. "At that level, the uncertainty is multiple hundreds of percent. When there's a model update, you might see that risks you had classified as good risks suddenly aren't."

"But the problem is not the models," she concludes. "The problem is basing so many decisions on a few numbers that come out of the models."

Nathan Golia is senior editor of Insurance & Technology. He joined the publication in 2010 as associate editor and covers all aspects of the nexus between insurance and information technology, including mobility, distribution, core systems, customer interaction, and risk ... View Full Bio

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