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Catastrophe Models Perception, Science and Reality June 28, 2007 He-Jung Kim, Senior Vice President Thomas Clift, Principal.

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Presentation on theme: "Catastrophe Models Perception, Science and Reality June 28, 2007 He-Jung Kim, Senior Vice President Thomas Clift, Principal."— Presentation transcript:

1 Catastrophe Models Perception, Science and Reality June 28, 2007 He-Jung Kim, Senior Vice President Thomas Clift, Principal

2 1 Discussion Topics  Perception, Science and Reality –Perception of catastrophe models at the senior insurance levels –Reasons behind current thinking –Reaction by modeling firms –Northeast observations  Stochastic databases / SSI classification  Industry loss estimates  Historical perspective –(He-Jung – need to add your topics)

3 2 Model Perception (Limited View) RIMS 2007 – New Orleans  Mr. Evan Greenberg, President and CEO, ACE Ltd. told the audience –“There is a science and there is a framework” for catastrophe modeling activities but cautioned that the science should not give insurance buyers too much comfort. –“It’s (catastrophe models) a crude science and it’s an evolving science and those models probably are as good as the next cat season.”  Mr. Shivan S. Subramaniam, Chairman and CEIO, FM Global –“The thing to remember is that models don’t predict disaster. What they do predict is what aggregations should be, given a certain set of circumstances.” Industry Focus, May 2007

4 3 2006 Catastrophe Model Changes (Science)  New and enhanced model features –Near term verses long term view –Updates to event catalog and vulnerability functions –Loss amplification (enhanced demand surge) –Storm surge confusion  General Impact

5 4 Model Credibility (Reality)  Depends on who you ask! Two to four times variance from actual Actua l Losse s

6 5 Catastrophe Models – Integral Part of the Insurance Industry (Reality)  Thee biggest influencers on the insurance industry over the last 10 to 15 years – Consolidations, Internet (?) and Catastrophe Models –Business strategies (line of business, policy form, distribution base, geography) –Rate development (catastrophe load) –Expenses (reinsurance costs) –Regulatory (increased capital requirements, start - up companies) –Overall operating income / loss –Helps define financial stability

7 6 Model Impact on Reinsurance Costs - Industry Combined Ratio (Perception and Reality) Hurricane Andrew September 11 th 2004 / 05 Hurricanes * All lines figure is full-year III estimate. RAA figure for 2005: 9 mos. Source: A.M. Best, ISO, Reinsurance Association of America, Insurance Information Institute

8 7 Model* Perspective of Northeast – Interesting Observations (Science)  About 5% of the total stochastic database is a northeast landfall event  With reference to the northeast landfalls, about –15 to 40% are classified as SSI 1 –25 to 37% are SSI 2 –23 to 37 noted as SSI 3 –9 to 10% are SSI 4, and –Less than 1% are classified as SSI 5 (but there are 5s) *Based on leading hurricane models

9 8 Northeast Hurricanes – Interesting Observations  Fourteen (14) between 1895 and 2006  Average return period is 7.9 years  Time span between  2007 season will mark sixteen (16) years since prior  Only once in the last 111 years have we gone more than fifteen (15) years without a northeast hurricane Plus current

10 9 Northeast Hurricane - $65B Industry Loss Potential  11 major airports  3 major ports

11 10  $100b gross property loss with demand surge and storm surge  Other lines affected  Fine Art  Aviation  Liability  Business Interruption Miami-Dade Hurricane - $100B Industry Loss Potential

12 11  2,000 deaths and 20,000 injuries California Earthquake Event – 75B Industry Loss Potential

13 12 Summary  The perception of catastrophe models needs to improve at the senior management levels  Catastrophe models should be viewed as “one” of many inputs  Catastrophe models are deeply rooted in the insurance industry and influences many actions  We can not ignore the marketplace if also significantly influenced by operating results  Model limitations must be understood and accounted for as there are many unknowns


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