|Title:||United States top 10 costliest hurricanes ranked by estimated insured loss in dollars for the period 1989 to 2011|
Start of full article - but without data
TOP TEN COSTLIEST U.S. HURRICANES (X) ($ MILLIONS)
ESTIMATED INSURED LOSS (X)
Rank Date Hurricane Dollars Dollars When (X) In Occurred 2011
X Aug. Katrina $XX,XXX $XX,XXX XX-XX, 2005
X Aug. Andrew XX,XXX XX,XXX XX-XX, 1992
X Sep. Ike XX,XXX XX,XXX XX-XX, 2008
X Oct. Wilma XX,XXX XX,XXX XX, 2005
X Aug. Charley X,XXX X,XXX XX-XX, 2004
X Sep. Ivan X,XXX X,XXX XX-XX, 2004
X Sep. Hugo X,XXX X,XXX XX-XX, 1989
X Sep. Rita X,XXX X,XXX XX-XX, 2005
X Sep. Frances X,XXX X,XXX X-X, 2004
XX Aug. Irene X,XXX X,XXX XX-XX, 2011
(X) Includes hurricanes occurring through 2011.
(X) Property coverage only. Does not include flood
damage covered by the federally administered National
Flood Insurance Program.
(X) Adjusted for inflation through 2011 by ISO using
the GDP implicit price deflator.
Source: The Insurance Information Institute (I.I.I.)
Hurricane disasters can occur whether the season is active or relatively quiet. It only takes one hurricane (or tropical storm) to cause a disaster and virtually unimaginable destruction. Therefore, it is imperative for insurers and risk managers to adequately prepare for every hurricane season regardless of seasonal outlook.
The 2011 Atlantic hurricane season produced XX tropical cyclones, XX tropical storms, seven hurricanes, and four major hurricanes. It featured a record sequence of weak tropical storms, and Hurricane Irene, a powerful Category X storm, was the first hurricane of the season. The season tied 2010, 1995, and XXXX for the third highest number of tropical storms.
On the precipice of the official start of the 2012 Atlantic Hurricane Season, which runs from June X until November XX, we spoke with Karen Clark, president and CEO of Karen Clark & Company. Clark explains that while catastrophe models and software applications continue to evolve and improve over time, the bigger challenge for insurers is realizing the full value of such tools. She discusses new considerations in the assessment and management processes risk and how to fully utilize new methodologies. For the purposes of this article, CE denotes a "characteristic event."
Q In terms of what insurers need to be doing, what are the fundamental requirements for effective catastrophe risk management?
A After consulting with dozens of insurance and reinsurance companies over the past few years, we've found that insurers would very much like to have risk-management metrics with three fundamental qualities: consistency, transparency, and operational ability. In order to effectively manage catastrophe risk, insurers need to fully understand the risk and how it's being measured for their specific books of business. Insurers would also like a tool that enables them to monitor the effectiveness of their risk-management strategies over time.
Q What is/has been the alternative to the CE methodology?
A Catastrophe modeling has been the standard approach for measuring and monitoring catastrophe-loss potential. While the catastrophe models provide valuable information, they are not highly effective risk-management tools. The numbers generated by the models tend to swing widely from model to model and update to update, and the opaqueness of the models makes it very difficult for the modelers and the model users to decipher the true drivers of changes in the modeled loss estimates, particularly for company-specific books of business.
Q What differentiates the CE approach from other methods?
A The CE approach is transparent and is the right balance between fully probabilistic and deterministic approaches to catastrophe-loss estimation. Many companies, realizing the shortcomings in the probabilistic models, have turned back to scenario-based deterministic approaches that are more concrete but don't give a complete picture of catastrophe-loss potential. CEs are defined-probability events created for the return periods of most interest to insurers--such as one-in-XXX and one-in-XXX years--and are floated across a book of property exposures to provide a complete analysis of the loss potential from representative return-period events.
Q How does this complement the catastrophe models and/or address the limitations of the models?
A CEs are based on the same scientific data underlying the catastrophe models, but instead of generating a lot of random events, the science is used to develop return-period events representative of specific regions and perils. The models generate a large catalog of random events, calculate the losses from each event, and then sort the losses from most to least severe to estimate the XXX-and XXX-year probable maximum losses (PMLs).
Because of the modeling process, PMLs are not operational and are highly volatile numbers. Instead of one number, CEs provide a range of loss estimates for the XXX-and XXX-year events that are stable, operational-risk metrics that can be drilled down to counties, ZIP codes, and even individual policies for risk-management purposes. In this way, CEs provide valuable information that addresses the model limitations and complements the model-generated information.
Q How does it foster a more consistent and transparent view of hurricane risk specifically?
A The CE footprints are completely transparent to the user so they can be easily peer reviewed by internal and external experts. The damage functions that are used to calculate the losses are also visible to the user. Because they are based on the most credible and reliable scientific data, the CEs remain constant from year to year, thereby providing a consistent yardstick for measuring and monitoring risk over time.
Q What in your opinion is the greatest challenge P&C insurers face in predicting and preparing for hurricanes?
A By focusing on PMLs to manage risk, insurers frequently are surprised by actual events that cause losses over their PML estimates. While this may be because of "model miss," the more significant problem is that model-generated PMLs mask the large loss potential from XXX-year events making landfall in specific locations. PMLs give a false sense of security, while the CE analysis clearly shows where companies have exposure concentrations that will result in losses well above their XXX-year PML loss estimates even from XXX-year events.
Q Would you elaborate on how insurers are using the CE methodology to:
X Better understand cat risk and exposure concentrations.