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Fourth Edition 2009

INSURANCE RISK STUDYModeling The Global Market

reDEFININGCapital | Access | Advocacy | Innovation

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Insurance rIsk study

About the StudyRating agencies, regulators and investors today are demanding that insurers provide detailed assessments of their risk tolerance

and quantify the adequacy of their economic capital. To complete such assessments requires a credible baseline for underwriting

volatility. In order to help our clients address these needs Aon Benfield Analytics began a detailed study of underwriting volatility in

2003 leading to this published Insurance Risk Study, now in its fourth edition. The Study provides our clients with an objective and

data-driven set of underwriting volatility benchmarks by line of business and country as well as correlations by line and country.

These benchmarks are a valuable resource to CROs, actuaries, and other economic capital modeling professionals who seek reliable

parameters for their models.

Using the factors in the Study as a complement to existing industry or proprietary frequency and severity curves, insurers can

assess the volatility of their business using the same metrics as catastrophe models. For example, it is possible to estimate the

aggregate loss potential over an accident year, both with and without the impact of the pricing cycle, analogous to catastrophe

model aggregate PMLs. These estimates can be calculated using Aon Benfield’s ReMetrica software.

The 2009 Study quantifies the systemic risk or volatility of each line of business for 26 countries that together comprise more

than 85 percent of global premium. Systemic risk in the Study is the coefficient of variation of loss ratio for a large book of

business. Coefficient of variation (CV) is a commonly used normalized measure of risk defined as the standard deviation divided

by the mean. Systemic risk typically derives from non-diversifiable risk sources such as changing market rate adequacy, unknown

prospective frequency and severity trend, weather-related losses, legal reforms and court decisions, the level of economic activity

and other macroeconomic factors. It also includes the risk to smaller and specialty lines of business caused by a lack of credible

data. For many lines of business systemic risk is the major component of underwriting volatility.

Modern portfolio theory for assets teaches that increasing the number of stocks in a portfolio will diversify and reduce the

portfolio’s risk, but will not eliminate risk completely; the systemic market risk remains. This is illustrated in the left chart above. In

the same way, insurers can reduce underwriting volatility by increasing account volume, but they cannot reduce their volatility to

zero. A certain level of systemic insurance risk will always remain, due to factors such as the underwriting cycle, macroeconomic

factors, legal changes and weather, see right chart. The Insurance Risk Study calculates this systemic risk by line of business and

country. The Naïve Model on the right plot shows the relationship between risk and volume using a Poisson assumption for claim

count – a textbook actuarial approach. The Study clearly shows that this assumption does not fit with empirical data for any line of

business in any country. It will underestimate underwriting risk if used in an ERM model.

Portfolio Risk

SystemicMarket Risk

Number of Stocks

Port

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Ris

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Account Volume

Insu

ranc

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SystemicInsurance

Risk

Naïve Model

Empirical Data

asset portfolIo rIsk Insurance portfolIo rIsk

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Aon Benfield Insurance Risk StudyAon Benfield’s Insurance Risk Study is the industry’s leading publicly available set of risk parameters for modeling and benchmarking underwriting risk. The factors in the Study are an essential input for management to accurately quantify and manage underwriting portfolio risk, and to communicate the results credibly with rating agencies, regulators, investors and boards of directors.

The Study is a key component of Aon Benfield Analytics’ integrated and comprehensive risk modeling and assessment capabilities.

Our reinsurance optimization framework, consistently linking reinsurance to capital, relies on the Study for a >credible assessment of frequency and severity volatility.

Our ERM and economic capital modeling practice uses the Study to benchmark risk, quantify capital adequacy >and allocate capital to risk drivers. With chronic turmoil and rapid change in the financial markets, traditional capital may no longer be optimal or even available. Reliable capital modeling is essential to help clients maintain a secure and flexible capital structure.

Our state-of-the-art ReMetrica > ® risk evaluation and capital modeling software provides easy access to the Study parameters and risk insights.

Aon Benfield’s Investment Banking Group collaborates with the Analytics team in their engagements. We use the >Study to help companies considering mergers and acquisitions determine post-transaction underwriting risk and optimal capital structures.

Innovative solutions are needed in an evolving world. Aon Benfield recognizes that every client situation is unique and requires a bespoke solution. The massive database underlying the Study can be mined by our 400+ strong global Analytics team to extend the basic parameters reported here to answer clients’ specific and pressing business needs.

the fourth edItIon of the study expands the scope and coverage of prevIous edItIons:

The Insurance Risk Study is based on an extensive international database spanning 1.4 million individual data points for 5,600 companies and 300 different line/country combinations. Data sources include regulatory filings, statistical agents and rating agents.

Results from 26 countries comprising more than >85 percent of global premium

Results for eight core lines of business with more >granular splits varying by country

Correlation between lines within each country, and >between countries by line of business

Library of proprietary severity curves >

Correlation between macroeconomic variables and >insurance variables

Greater coverage of reserve risk, including correlation >of reserve development between lines

Detailed study of European motor large loss >frequency and severity

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Global Risk Parameters

The 2009 Insurance Risk Study quantifies the systemic risk for companies operating in 26 countries worldwide, up from 17 last year. Systemic risk is measured as the coefficient of variation (CV) of gross loss ratio. If gross loss ratios are not available the net loss ratio is used. All the risk factors are calculated on a consistent basis across all lines of business and countries and they indicate the baseline volatility inherent in each line. A table of the systemic risk factors for major lines appears on the following page.

common themes

Commercial lines are more volatile than personal lines. With few exceptions, personal motor is the least volatile line across >the countries surveyed. Fidelity & surety is the most volatile major line in nearly every country where it is reported.

Liability volatility in Europe ranges from 16 percent to 26 percent, consistently less than in the Americas where it >ranges from 36 percent to 84 percent. Japan has the lowest liability volatility at 10 percent; it also has materially fewer lawyers per capita than the U.S. or Western Europe.

Motor volatility in major European economies ranges from 7 percent to 12 percent compared to 16 percent in >the U.S. despite being an unlimited cover in Europe and having a relatively low limit in the U.S.

Risk of personal property (homeowners) is generally comparable with the estimated U.S. homeowners non-cat >risk CV of 27 percent, with the exceptions of Brazil and France which show a higher level of risk.

China’s 9 percent property CV does not reflect the 2008 winter storms and the Sichuan earthquake. We estimate >that the impact of these catastrophes was approximately 30 loss ratio points and the resulting property CV will be at least 20 percent when 2008 data is included.

The insurance penetration ratio (total written premium divided by gross domestic product, shown on page 13) provides a simple benchmark for the maturity of a country’s insurance market. In the chart to the right, this ratio is plotted against the CV for property insurance in each country surveyed. The results cluster according to economics. Developed countries show similar levels of insurance penetration and comparatively low volatility. Developing countries have less mature insurance markets, and in countries with dense populations such as Hong Kong and Singapore insurers can only diversify to a limited degree. Consequently these countries experience higher volatility. The BRIC countries fall between the two.

Differences remain due to the unique characteristics of each market and relative exposure to catastrophe activity. Despite having the highest insurance penetration, the U.S. still has among the highest volatilities of the major economies, due to the impact of Atlantic windstorms. This volatility drives high capital requirements for U.S. property, which are then reflected in higher insurance and reinsurance margins.

property, coeffIcIent of varIatIon of loss ratIo

Australia AusAustria AusiBrazil BrazCanada CanChina ChinaColombia ColDenmark DenFrance FrGermany Ger

Greece GrcHong Kong HKIndia IndiaIndonesia IndonItaly ItJapan JpMalaysia MalMexico MexPeru Peru

Singapore SingSouth Africa SAfSouth Korea KorSpain SpnSwitzerland SwitzTaiwan TaiU.K. U.K.U.S. U.S.

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0% 3.5% 4.0%0%

20%

40%

60%

80%

100%

Aus

Ausi

Braz

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Can

China

Col

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Fr

Ger

Grc

HK

India

Indon

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Mex

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Sing

S.Afr

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Switz

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U.S.

Premium as a Percentage of GDP

Developing/Small

BRIC

Developed/Europe

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underWrItIng volatIlIty for maJor lInes By country, coeffIcIent of varIatIon of loss ratIo for each lIne

asIa pacIfIc

australIa chIna hong kong IndIa IndonesIa Japan malaysIa sIngapore s. korea taIWan

Motor Personal Commercial

8% 14% 14%

43% 43%

9% 9%

33% 33%

4% 15% 32% 8% 8%

7% 7%

Property Personal Commercial

20% 19% 32%

9% 79% 31% 56% 28% 26% 80% 36% 83%

Liability 41% 29% 81% 79% 10% 133% 30% 48%

Accident & Health 19% 24% 16% 61% 6% 27% 29%

Marine, Aviation & Transit

14% 11% 64% 26% 72% 24% 29% 56% 45% 69%

Workers Compensation

29% 7% 56% 37%

Credit 80% 11% 49%

Fidelity & Surety 94%

europe & afrIca

austrIa denmark france germany greece Italy s. afrIca spaIn sWItzerland u.k.


28% 25% 7% 9% 40% 10% 26% 9% 8% 12% 11% 18%


31% 24% 93%

27% 19% 36%

32% 35% 27%

18% 17% 32%

63% 17% 12% 17% 10% 20%

16% 21% 18% 28%

Liability 16% 16% 22% 25% 22% 53% 25% 17% 26%

Accident & Health 5% 19% 18% 14% 79% 9% 33% 14% 7% 4%

Marine, Aviation & Transit

40% 36% 51% 20% 76% 44% 48% 33% 38% 76%

Workers Compensation

12%

Credit 20% 47% 32% 13% 61%

Fidelity & Surety 68% 93%

amerIcas

BrazIl canada colomBIa mexIco peru u.s.


12% 17% 15% 11% 14% 16% 15% 25%


45% 43% 46%

30% 20% 43%

54% 77% 90% 43% 49% 34%

Liability 41% 39% 49% 84% 60% 36%

Accident & Health 47% 41% 57% 3% 6% 47%

Marine, Aviation & Transit 42% 86% 21% 39%

Workers Compensation 159% 28%

Credit 54% 87% 23% 127%

Fidelity & Surety 102% 55% 136% 71%

Reported CVs are of gross loss ratios except for Australia, India, Malaysia and Singapore, which are of net loss ratios.

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underWrItIng cycle adJustedas reported

U.S. Risk Parameters

The U.S. portion of the Insurance Risk Study uses data from eight years of NAIC Annual Statements for 2,338 individual groups and companies. The database covers all 22 Schedule P lines of business and contains 1.2 million records of individual company observations from accident years 1992-2008.

The charts below show the loss ratio volatility for each Schedule P line, with and without the effect of the underwriting cycle. The effect of the underwriting cycle is approximately removed by normalizing loss ratios by accident year prior to computing volatility. This decomposes loss ratio volatility into its loss and premium components.

common themes

Personal automobile liability and auto physical damage consistently produce the lowest volatility results. >

With a 49 percent CV, homeowners insurance is the most volatile U.S. major line in the 17-year period of the >Study, due to the active hurricane seasons in 2004 and 2005. The 2008 storms did not change the overall calculated volatility of homeowners. If we exclude the major catastrophe losses of 1992, 2004, 2005, and 2008 homeowners has a CV of 27 percent – a risk level comparable to workers compensation. Catastrophe losses in these years increase the estimated volatility of homeowners by 80 percent.

The underwriting cycle amplifies loss ratio volatility through changes in pricing, terms and conditions. The >cycle increases risk by up to 50 percent or more, drives correlation between lines and reduces the benefits of underwriting diversification. The table on the following page measures this increase in risk.

Reserve development in long-tailed liability lines has produced upward revisions in estimated volatility each year >since 2001. For example, in the 2006 edition of this Study, medical malpractice claims-made had a calculated CV of 33 percent, while it now calculates as 38 percent.

coeffIcIent of varIatIon of gross loss ratIo | 1992-2008

Financial GuarantyProducts Liability - Claims-Made

Special PropertyReinsurance - FinancialReinsurance - Property

InternationalFidelity and Surety

Reinsurance - LiabilityHomeowners

Products Liability - OccurrenceOther

Other Liability - Claims-MadeSpecial Liability

Medical Malpractice - CMOther Liability - Occurrence

WarrantyCommercial Multi Peril

Medical Malpractice - OccurrenceWorkers Compensation

Commercial AutoAuto Physical Damage

Private Passenger Auto 15%17%

25%28%

31%34%35%36%38%39%41%

47%47%49%

66%71%74%

86%89%

106%108%

273%

14%16%18%19%

32%

27%37%

25%26%29%

27%45%

32%42%45%

55%56%57%

58%64%

40%120%

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Insurance rIsk compared to equIty rIsk

The 2008 financial crisis caused considerable volatility in stock prices, as measured by the CBOE Volatility Index (VIX). Nonetheless, many insurance lines have shown greater volatility than stocks in all but the most exceptional periods of the last 30 years. By the end of July 2009, the VIX had returned to the mid-20s, approximately the same level of risk as commercial auto.

the u.s. underWrItIng cycle

Volatility for most lines of business is increased by the insurance underwriting and pricing cycle. The underwriting cycle acts simultaneously across many lines of business, driving correlation between the results of different lines and amplifying the effect of underwriting risk to primary insurers and reinsurers. Our analysis shows that the cycle increases volatility substantially for all major commercial lines, as shown in the next table. For example, the underwriting volatility of other liability claims-made, which includes directors and officers liability claims, increases by 54 percent, workers compensation by 48 percent, other liability occurrence by 43 percent and commercial auto liability by 39 percent.

One goal of ERM is to reduce this “self-inflicted” volatility penalty, allowing companies to write at greater leverage and to lower their cost of capital over the long term. Personal lines, which are more formula rated, show a much lower cycle effect, with private passenger auto volatility only increasing by seven percent because of the cycle.

When WIll the cycle turn?

The following chart shows the long-term behavior of the U.S. pricing cycle. Industry net written premium as a percentage of gross domestic product fluctuates widely, but, over the last 39 years, when the ratio falls below 3.0 pecent it has signaled the beginning of a new hard market. We forecast values of 3.2 percent for 2009 and 3.1 percent for 2010, which suggest that the current soft market will continue through 2010 in the absence of a major cat event.

Industry nWp as percent of gdp

lIne volatIlItycomparaBle perIod

of stock prIce volatIlIty

Private Passenger Auto 15% Early 2007

Commercial Auto 25% February 2008

Workers Compensation 28% Early 2008

Commercial Multi Peril 34% March 2003 (U.S. invades Iraq)

General Liability 36% April 30, 2009 (Chrysler bankruptcy)

D&O 41% Markets reopen after 9-11

Homeowners 49% 46% achieved during 1998 Russian financial crisis

Fidelity and Surety 71% October 10, 2008 (S&P 500 falls below 900)

Reinsurance - Property 86% 81% on November 20, 2008 (S&P hits 11 year low)

Special Property 106% 150% achieved on October 19, 1987

lIne Impact of prIcIng cycle

Other Liability - Claims-Made 54%

Workers Compensation 48%

Medical Malpractice - CM 47%

Reinsurance - Liability 45%

Other Liability - Occurrence 43%

Commercial Auto 39%

Special Liability 36%

Commercial Multi Peril 25%

Homeowners 17%

Private Passenger Auto 7%

2.5 %

3.0 %

3.5 %

4.0 %

4.5 %

Long-term average, 3.4%

% GDP

201020052000199519901985198019751970

1974, 3.0% 1984, 3.0% 2000, 3.0%2008, 3.1%

YearNWP

ChangeGDP

GrowthNWP

to GDP

2009 -0.5% -2.7% 3.2%

2010 0.0 2.0% 3.1%

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Loss Ratio Correlation correlatIon of underWrItIng results

Correlation between different lines of business is central to a realistic assessment of aggregate portfolio risk. Modeling is invariably performed using an analysis-synthesis paradigm: analysis is carried out at the product or business unit level and is then aggregated to the company level. In most applications, results are more significantly impacted by the correlation and dependency assumptions made during the synthesis step than by all the detailed assumptions made during the analysis step.

The Study determines correlations between lines within each country and also between countries. Although not shown here, we have also calculated confidence intervals for each correlation coefficient and the impact of the pricing cycle on correlation.

correlatIon BetWeen lInes

Correlation between lines is computed by examining the results from larger companies that write pairs of lines in the same country. The following tables show a sampling of the results available for the U.S., U.K., Germany, Japan, China, and Australia.

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Homeowners 100% 6% 20% 5% -9% -2% -11% -4% 11%

Personal Auto Liability 6% 100% 24% 27% 31% 31% 21% 34% 24%

Commercial Multi Peril 20% 24% 100% 51% 45% 49% 57% 43% 41%

Commercial Auto 5% 27% 51% 100% 64% 69% 71% 45% 74%

Workers Comp -9% 31% 45% 64% 100% 65% 76% 60% 65%

Other Liability Occ -2% 31% 49% 69% 65% 100% 80% 59% 66%

Medical Malpractice CM -11% 21% 57% 71% 76% 80% 100% 74% 80%

Other Liability CM -4% 34% 43% 45% 60% 59% 74% 100% 31%

Products Liability Occ 11% 24% 41% 74% 65% 66% 80% 31% 100%

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Accident & Health 100% 60% 15% n/a 78% 56% 48%

Private Motor 60% 100% 40% 61% 82% 20% 55%

Household & Domestic 15% 40% 100% 85% 37% 54% 51%

Financial Loss n/a 61% 85% 100% 39% n/a n/a

Commercial Motor 78% 82% 37% 39% 100% 32% 61%

Commercial Property 56% 20% 54% n/a 32% 100% 43%

Commercial Lines Liability 48% 55% 51% n/a 61% 43% 100%

Correlation is a measure of association between two random quantities. It varies between -1 and +1, with +1 indicating a perfect increasing linear relationship and -1 a perfect decreasing relationship. The closer the coefficient is to either +1 or -1 the stronger the linear association between the two variables. A value of 0 indicates no linear relationship whatsoever.

All correlations in the Study are estimated using the Pearson sample correlation coefficient.

In each table the correlations shown in bold are statistically different from zero at the 90% confidence level.

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aon BenfIeld

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Accident & Health 100% 56% 40% 55% 30% 69% 77%

Agriculture 56% 100% 52% 45% 27% 32% 18%

Credit 40% 52% 100% 17% 24% 38% 46%

General Liability 55% 45% 17% 100% 31% 62% 66%

Marine, Aviation & Transit 30% 27% 24% 31% 100% 31% 24%

Motor 69% 32% 38% 62% 31% 100% 70%

Property 77% 18% 46% 66% 24% 70% 100%

Japan

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Accident & Health 100% 28% 4% 51% 47% 52%

General Liability 28% 100% -8% 2% 34% 33%

Marine, Aviation & Transit 4% -8% 100% 18% 28% -11%

Motor 51% 2% 18% 100% 63% 40%

Property 47% 34% 28% 63% 100% 33%

Workers Comp 52% 33% -11% 40% 33% 100%

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Accident & Health 100% 66% -12% -37% 10% -14% -12% -3%

Assistance 66% 100% n/a n/a -40% 24% -26% -41%

Cargo -12% n/a 100% 46% 8% -49% 22% 23%

Credit -37% n/a 46% 100% -4% -21% 26% 33%

General Liability 10% -40% 8% -4% 100% -10% 7% -2%

Legal Protection -14% 24% -49% -21% -10% 100% 19% -21%

Motor -12% -26% 22% 26% 7% 19% 100% 5%

Property -3% -41% 23% 33% -2% -21% 5% 100%

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correlatIon BetWeen countrIes

In addition to correlation between lines of business, global insurers must also consider the correlation of business written in different countries. We estimate these correlation coefficients based on country-level loss ratios by line by year. For example, the following tables show loss ratio correlation among the G8 and BRIC countries for Motor, Property and Liability lines.

For all three lines, the U.S. shows very high correlation with Canada, as expected. The U.S. also shows significant correlations with Western Europe for Motor and Liability. Western Europe shows reasonably high correlations among its constituent countries. Brazil also shows strong correlations with Western Europe and North America. Although Japan is a mature insurance market, it does not appear strongly correlated with the other countries surveyed – this is consistent with domestic insurer dominance of the Japanese market.

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U.S. 100% 85% 17% 76% 78% 60% 4% -39% 65% -19% 75%

Canada 85% 100% -1% 21% -1% 59% 19% -13% 33% -45% 58%

U.K. 17% -1% 100% 23% 11% 63% -20% 15% 47% 8% 30%

France 76% 21% 23% 100% 75% 37% -9% -28% 22% 10% 55%

Germany 78% -1% 11% 75% 100% 15% -12% -33% -11% 26% 53%

Italy 60% 59% 63% 37% 15% 100% 22% -11% 53% -4% 45%

Japan 4% 19% -20% -9% -12% 22% 100% -55% -48% 71% -49%

China -39% -13% 15% -28% -33% -11% -55% 100% 65% -78% 6%

India 65% 33% 47% 22% -11% 53% -48% 65% 100% n/a 43%

Russia -19% -45% 8% 10% 26% -4% 71% -78% n/a 100% -53%

Brazil 75% 58% 30% 55% 53% 45% -49% 6% 43% -53% 100%

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General Liability 100% 17% 22% -23% 20%

Marine, Aviation & Transit 17% 100% 30% -8% 31%

Motor 22% 30% 100% 27% 16%

Property -23% -8% 27% 100% -6%

Workers Comp 20% 31% 16% -6% 100%

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Coefficients in bold are significantly different from zero at the 90 percent confidence level.

Limited data from Spain prevented its inclusion in these matrices.

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U.S. 100% 62% -9% 18% -23% -7% 2% -5% 3% -44% 26%

Canada 62% 100% 46% 23% 10% 57% 17% -25% 15% 36% 61%

U.K. -9% 46% 100% 30% 54% 41% 15% -70% 9% -2% 46%

France 18% 23% 30% 100% 29% 2% 2% -45% 2% 2% 26%

Germany -23% 10% 54% 29% 100% 26% -11% -22% 12% -8% 38%

Italy -7% 57% 41% 2% 26% 100% 34% -67% -11% 67% 55%

Japan 2% 17% 15% 2% -11% 34% 100% 11% -21% -9% -40%

China -5% -25% -70% -45% -22% -67% 11% 100% -5% -10% -58%

India 3% 15% 9% 2% 12% -11% -21% -5% 100% 4% 15%

Russia -44% 36% -2% 2% -8% 67% -9% -10% 4% 100% 19%

Brazil 26% 61% 46% 26% 38% 55% -40% -58% 15% 19% 100%

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U.S. 100% 79% 58% 35% 48% 60% 9% 4% -24% 40%

Canada 79% 100% 37% 35% 52% 53% 4% 41% 29% 44%

U.K. 58% 37% 100% 44% 59% -6% 18% 13% 47% 59%

France 35% 35% 44% 100% 88% 61% -39% -11% 6% 55%

Germany 48% 52% 59% 88% 100% 51% -33% 8% 30% 61%

Italy 60% 53% -6% 61% 51% 100% -45% 9% 1% 37%

Japan 9% 4% 18% -39% -33% -45% 100% -21% 8% -25%

China 4% 41% 13% -11% 8% 9% -21% 100% 62% -3%

Russia -24% 29% 47% 6% 30% 1% 8% 62% 100% -13%

Brazil 40% 44% 59% 55% 61% 37% -25% -3% -13% 100%

12


Macroeconomic CorrelationCorrelation among macroeconomic factors is a very important consideration in risk modeling. For example, GDP growth and inflation affect underwriting and investment returns and thus impact insurers’ financial health and stability.

The boxes group similar kinds of variables. Bond and stock returns move together and are less correlated with changes in property values. The VIX and credit spreads both measure the perceived level of risk in financial markets. They are strongly positively correlated, and both are strongly negatively correlated with stock returns. For insurers, the number of insolvencies moves with combined ratios, and both are negatively correlated with changes in surplus.

The impact of the finacial crisis can be seen by comparing the two matrices. Some variables that previously showed little correlation now show correlation that is significant at the 90 percent confidence level. The measured correlation between inflation and the VIX moved from +4 percent to -22 percent. The opposite effect also appears. In the period through 2006, insurance insolvencies showed 52 percent correlation with credit spreads. In 2007 and 2008 insurer insolvencies were the lowest on record even as spreads exploded, and the measured correlation disappeared. In this case it is also important to consider lagged correlations. The insurance-specific coefficients change very little when we exclude the recent observations.

InflatIongdp

groWthBond

returnsstock

returnsproperty returns

vIxcredIt

spreadsnumBer of

InsolvencIescomBIned

ratIochange In

surplus

all avaIlaBle years

Inflation 100% -2% -25% -13% 14% -22% -23% -29% -20% -3%

GDP Growth -2% 100% -27% 4% 50% -38% -67% -6% -7% 15%

Bond Returns -25% -27% 100% 37% -6% 5% 6% 26% 45% 11%

Stock Returns -13% 4% 37% 100% 17% -55% -48% 13% 4% 70%

Property Returns 14% 50% -6% 17% 100% -33% -65% 23% 8% 30%

VIX -22% -38% 5% -55% -33% 100% 68% -18% 6% -63%

Credit Spreads -23% -67% 6% -48% -65% 68% 100% -18% 4% -75%

Number of Insolvencies -29% -6% 26% 13% 23% -18% -18% 100% 64% -8%

Combined Ratio -20% -7% 45% 4% 8% 6% 4% 64% 100% -33%

Change in Surplus -3% 15% 11% 70% 30% -63% -75% -8% -33% 100%

2006 & prIor data

Inflation 100% -8% -23% -22% 0% 4% -3% -36% -23% -12%

GDP Growth -8% 100% -30% 0% 22% -13% -36% -11% -10% 11%

Bond Returns -23% -30% 100% 40% -19% 17% 20% 25% 47% 9%

Stock Returns -22% 0% 40% 100% -19% -38% -44% 3% 0% 66%

Property Returns 0% 22% -19% -19% 100% 24% 27% -24% -23% -15%

VIX 4% -13% 17% -38% 24% 100% 48% 19% 30% -43%

Credit Spreads -3% -36% 20% -44% 27% 48% 100% 52% 43% -78%

Number of Insolvencies -36% -11% 25% 3% -24% 19% 52% 100% 63% -18%

Combined Ratio -23% -10% 47% 0% -23% 30% 43% 63% 100% -39%

Change in Surplus -12% 11% 9% 66% -15% -43% -78% -18% -39% 100%

13

aon BenfIeld

Insurance and the EconomyThe 2008-09 global economic crisis has had a profound effect on nearly all insurers but its impact varies significantly by line and by type of insurer. The comments below are primarily based on experience in the U.S.

credit related lines, such as mortgage guarantee, financial guarantee, credit accident & health and trade credit, have all been severely impacted. For example, financial guarantee produced a combined ratio of 415 percent in 2008 versus an average of 44 percent between 1996 and 2006.

public directors and officers liability results have historically been very sensitive to stock market performance. Aon Benfield estimates D&O gross loss ratios were in excess of 150 percent for 1999-2002, driven by a combination of inadequate pricing and unexpected frequency and severity. Since then loss ratios have averaged around 60 percent. In 2008, braced for higher frequency, insurers increased their initial ultimate loss ratio selections more than 6 points for other liability claims-made.

surety is adversely impacted by economic downturns, as declining contract volume leads to more insolvencies. However, it is usually a lagged impact. Surety’s combined ratio in 2008, 66 percent, is actually well below the long-run average of 96 percent. In the stressed period 2001-04 surety posted combined ratios over 125 percent each year compared to an average of 86 percent in 1996-2000.

automobile can see better results in a recession as drivers log fewer miles.

Workers compensation has a complicated relationship with economic activity. Some argue that layoffs and shorter work weeks reduce workplace accidents because less experienced workers are let go. Others see a potential increase in losses as companies cut corners in maintenance and safety and place less emphasis on back-to-work initiatives. Statistical analyses of the results tend to be inconclusive and are certainly a second-order effect relative to primary pricing levels.

property often sees spikes in arson and other fraudulent claims activity in recessions although measuring this impact is difficult.

IndiaSouth Africa

PolandTurkey

NorwayMexico

DenmarkSwedenAustria

BelgiumSwitzerland

RussiaBrazil

NetherlandsAustralia

ChinaCanada

SpainSouth Korea

ItalyFranceJapan

GermanyU.K.U.S. 482,409

76,50165,75865,224

59,95143,880

35,00034,424

33,25526,260

20,54917,246

14,15714,128

10,1439,842

8,6817,9437,7167,5627,455

5,9945,9405,7445,223

IndiaPeru

MexicoChina

TaiwanHong Kong

GreeceBrazil

RussiaColombiaGermany

South AfricaItaly

BelgiumNetherlands

AustraliaFrance

CanadaAustria

SwitzerlandSpain

DenmarkU.K.

South KoreaU.S. 3.49%

3.34%2.73%

2.49%2.40%2.38%

2.34%2.33%2.31%

2.26%2.22%

2.15%2.09%

2.02%1.98%

1.30%1.10%

1.06%0.93%

0.82%0.81%0.78%

0.74%0.53%

0.46%

IndiaChinaPeru

MexicoBrazil

RussiaHong Kong

GreeceSingapore

PortugalJapan

South KoreaItaly

SpainGermanyBelgium

FranceAustralia

CanadaAustria

NetherlandsU.K.

SwitzerlandDenmark

U.S. 1,5771,418

1,3551,259

1,0501,038

1,012991

972941

799777

745730

512453

327261

23499

7471

20204

2007 Insurance penetratIon statIstIcs

gross WrItten premIum (usd mIllIon)

Insurance penetratIon (gWp as % of gdp)

gross WrItten premIum per capIta (usd)

14


Asset Risk Modelingaon BenfIeld economIc scenarIo generator

Economic capital models often use a set of economic scenarios to help evaluate the impact of fundamental variables such as inflation and interest rates on the valuation of assets and liabilities. Scenarios can also be used to determine asset allocation strategies. As its name implies, an economic scenario generator (ESG) is used to generate a realistic set of economic scenarios.

Aon Benfield is collaborating with the Cass Business School, London, to develop a true multi-currency, credit-enabled ESG. Cass is providing technical expertise to determine appropriate theoretical models and calibration methodologies. The Aon Benfield ESG provides the following variables for all major economies:

Inflation >

Risk-free interest rates >

Credit default rates and credit spreads >

Equity return >

Foreign exchange rates between economies >

Our ESG captures correlation between economies and correlation of economic variables within each economy. It also produces market consistent calibrations within each economy.

realIstIc scenarIos and Back-testIng

A key principle in the development of the ESG is to ensure its scenarios back-test well against the recent financial crisis. We require that reasonable probabilities be assigned to the actual losses experienced across all modeled asset classes. This testing is discussed more on the facing page.

stochastIc volatIlIty

Stochastic volatility models have the advantage of producing scenarios that reflect economic conditions at the calibration date. As a result, in times of market turbulence the modeled downside risk will be higher than in times of economic stability. This reflects the economic reality that during times of high volatility, market prices will tend to move more quickly and by greater amounts. Aon Benfield recommends models that incorporate stochastic volatility and this approach is used in our ESG for equity returns and interest rates.

open standards

The Aon Benfield ESG is not a black box. The underlying models, calibration methodology and statistical validation tests are available to our clients along with a ReMetrica model that can be used to reproduce the economic scenario outputs. Such transparency is an important consideration for clients who wish to use an ESG in a regulatory environment such as Solvency II which requires a detailed understanding of modeling techniques and model sensitivity to input assumptions.

The Aon Benfield ESG development is nearing completion. It is expected to be released in Spring 2010.

15

aon BenfIeld

Equity market losses drove substantial book value reductions and earnings hits between September 2008 and March 2009 for many insurers. Given the magnitude of the swings involved, it is important that an economic capital model correctly captures the true risk of equities.

Standard equity models did not perform well during the market decline. The return periods assigned to observed declines range between 1 in 1,000 years and 1 in 239,000 years for major markets. The probabilities shown below are based on a standard “geometric Brownian motion” (i.e. lognormal return) model parameterized with estimated volatility observed in August 2008.

The standard lognormal model assumes the current level of stock price volatility will not change in the future. As a result, the perceived risk of equities in a low volatility environment is very low. Conversely, when volatility increases these models greatly increase the risk, and hence capital requirements, of holding equities. In reality volatility is not constant. Measured by the VIX, it moved from 20 percent in August 2008 to a peak of 81 percent in November and back to 25 percent in August 2009.

Working with a standard lognormal, the modeler has the choice either to select a long-term average volatility or to calibrate volatility to current levels. The former will understate the risk in turbulent times but the latter will greatly overstate long-term risk in a multi-year model.

Aon Benfield recommends using a stochastic volatility model such as the Heston model.

The Heston has important advantages over the standard lognormal model:

By allowing volatility to evolve it assigns a higher, >more realistic probability to the types of extreme swings experienced in 2008-09. For a decline of 35 percent in the S&P 500, the Heston model estimates a return period of less than 80 years compared to more than 1,400 years for a lognormal model when both are parameterized as of August 2008. Similar results hold for European, Japanese and British markets.

It can be calibrated to current volatility, but allows for >the possibility that volatility will change in the future. Despite being calibrated to current volatility, the Heston model is less sensitive to the starting value over the long run because volatility is modeled using a mean-reverting process.

We have also tested more complicated models, such as a lognormal with jumps (Merton model) and a version of the Heston model with jumps. However, our research indicates that the Heston model provides a good balance: it is tractable, easy to parameterize, and back-tests well.

market correlatIon

The level of correlation between global equity markets adds a significant level of risk to international insurers’ equity portfolios. Correlations between four major equity markets are shown below. During the period from August 2008 to July 2009, correlation increased by approximately 10 percent between the U.S. and other major markets, and by 20 percent between Japan and Europe.

Equity Model Performance in 2008

market u.s. euro u.k. Japan

Index S&P 500 EURO STOXX

FTSE All-share

TOPIX 1000

Index at August 29, 2008 1283 318 2869 1200

Index at March 9, 2009 677 169 1792 674

Capital Return -47% -47% -38% -44%

Lognormal Return Period 239,000 6,500 13,500 1,000

market u.s. euro u.k. Japan

U.S. 100% 53% 49% 11%

Euro 53% 100% 88% 28%

U.K. 49% 88% 100% 32%

Japan 11% 28% 32% 100%

16


ReMetrica: Economic Capital ModelingReMetrica® is Aon Benfield’s DFA and economic capital modeling environment. During 2009 we have enhanced ReMetrica with features from our Prime/ReTM DFA tool. ReMetrica is currently used by many of the world’s leading insurance, reinsurance and actuarial consulting firms. Users can quickly build realistic and transparent risk models for any specific business need by leveraging our extensive library of built-in components. Parameters from this Study are also available to users within ReMetrica.

complete, comprehensIve, sophIstIcated

Ability to model every aspect of risk including >property, casualty and life underwriting (pricing, reserving, catastrophes), market, credit, and operational risk

Flexible modeling of insurance and reinsurance >business including ground-up, excess of loss, large deductible, layered and shared property

Global portfolio aggregation >

Capital markets transactions >

Asset and liability management >

Business planning and forecasting >

Corporate structure analysis >

Mergers and acquisitions >

Structured timeline of events makes temporal >dependency natural – events in earlier periods can influence events in later periods

Management decisions and business logic >feedback loops

fast and easy to use

Drag and drop software with a graphical user >interface and over 100 built-in components

Full integration with Microsoft Excel to facilitate data >input and output

Incorporates results from all commercial catastrophe >models (RMS, AIR, IF, EQECat)

Easily aggregates data from other models >(investments, reserving, life)

Provides instantaneous feedback on model >parameterization, without the need for simulations

Supports stochastic and scenario-based modeling >

Extensive library of customized templates to >jumpstart modeling

BuIlt- In functIonalIty

Ability to model all types of assets and import results >from Aon Benfield and third-party economic scenario generators

Multiple capital allocation methodologies >

A.M. Best and S&P capital adequacy models >

Reinsurance risk transfer analysis >

Models easy to check with transparent auditing trail >and built-in diagnostics

Correlation and dependency through common >mixing variables, copulas, and common drivers

WorldWIde applIcatIon

Supports multiple currencies >

Income statement, balance sheet, and cash flow >analysis under multiple accounting standards

17

aon BenfIeld

As Europe prepares for the 2012 adoption of Solvency II, now is the time to consider its impact on required capital levels. The Solvency II capital requirement may be calculated using either the prescribed “Standard Formula” or an internal model. Although the Standard Formula is a reasonable starting point, it fails to consider important and unique aspects of an insurer’s strategy, such as:

The relationship between premium size and volatility >(computed in this Study)

Catastrophe exposure >

True impact of reinsurance >

Furthermore, some assumptions of the Standard Formula under Quantitative Impact Study (QIS) 4 produce higher capital requirements than are actually required. For example:

Unfair treatment of non-proportional reinsurance >

Conservative assumptions for reserve volatility on a >1-year time horizon

Assumed 100 > percent loss ratio for premium risk charge

Consequently, clients can benefit greatly by developing an internal model to calculate required capital.

IntroducIng s2metrIcasm

Developing an internal model can be a significant investment of time and resources. To assist our clients, Aon Benfield has developed S2Metrica, a standalone tool built on ReMetrica technology that automatically builds an internal model from QIS4 inputs. Using S2Metrica, clients can quickly construct a competent baseline model, freeing them to focus on critical tasks such as parameterization and appropriate customization.

An S2Metrica internal capital model captures the following distinctive features of an insurer’s business:

Use of commercial model catastrophe scenarios >

Highly customized reinsurance structures on >proportional or non-proportional basis

Asset portfolio structure >

Separate modeling of attritional, large and >catastrophe losses

Other detailed modeling assumptions >

open and extensIBle

S2Metrica is designed as a self-contained tool, but users of Aon Benfield’s ReMetrica software can view and run the models directly within the ReMetrica environment. Models can then be further edited and customized to meet a company’s unique needs.

example s2metrIca output: Internal model vs. standard model

ReMetrica: Ready for Solvency II

rIsk type standard formula

Internal model % dIfference

Premium 536 867 62%

Reserve 1,330 1,005 -24%

Catastrophe 315 356 13%

Market 5,764 6,536 13%

Credit 3 5 67%

Bscr 6,269 5,558

Operational 1,881 2,087

scr 8,150 7,645

Diversification Benefit 1,679 3,211

18


Motor Third Party Reinsurance in EuropeThere are almost 300 million vehicles on the road in Europe, making its motor insurance market the largest in the world. Motor insurance is also the largest sector of the non-life insurance market in Europe, with total premium income of around €130 billion.

The 2009 Aon Benfield European Motor Report is an in-depth study of third party motor reinsurance for 16 European countries. It provides an actuarial assessment of large loss frequency and severity. The report can be used to:

Price excess of loss layers of reinsurance based on industry experience >

Benchmark and assess current reinsurance rates >

Calibrate motor frequency and severity distributions >

Determine probable maximum losses based on catastrophic motor scenarios >

Quantify Green Card exposures in Central and Eastern Europe (where exposures from foreign motorists can >exceed those of domestic motorists)

assessment of large losses frequency and severIty

For each country we develop historical trends to form assumptions regarding large claim frequency and severity. This is shown below for the United Kingdom, where we estimate that the frequency of claims excess of £1.5 million is 6.8 per million vehicle years and that the severity of claims is best described by a generalized Pareto distribution.

0

1

2

3

4

5

6

7

8

9

10

Renewal year estimated

Latest revalued

Projected ultimate

2008200720062005200420032002200120001999

Years

Freq

uenc

y (p

er 1

M V

ehic

le Y

ears

)

u.k. claIms frequency excess of £1.5m

19

aon BenfIeld

motor thIrd party rIsk across europe

The potential for large claims varies substantially by country. The exhibits below show the probabilities of claims excess of various thresholds as well as the loss cost per vehicle year for layers of reinsurance in each of the highest risk countries.

loss cost per vehIcle (In €)

€3m xs €2m

€5m xs €5m

unlIm xs €10m

Portugal 0.48 0.20 0.18

Finland 0.88 0.09 0.02

Czech Republic 1.45 0.08 0.00

Denmark 1.49 0.61 0.05

Italy 2.34 0.18 0.02

Germany 2.64 0.25 0.02

Belgium 6.82 0.56 0.04

U.K. 10.45 5.02 2.67

Ireland 4.83 0.77

France 8.36 1.30

excess frequency (per 1m vehIcle years)

€2m €5m €10m

Spain 0.35 0.02 0.00

Portugal 0.40 0.08 0.03

Finland 1.27 0.06 0.01

Czech Republic 1.81 0.07 0.00

Germany 2.56 0.21 0.01

Italy 2.70 0.11 0.01

U.K. 6.22 1.82 0.42

Belgium 7.32 0.39 0.03

Ireland 2.26 0.29

France 3.26 0.61

1

10

100

0.1% 1.0% 10.0% 100.0%

Reva

lued

Cla

im S

ize (£

M)

Exceedance Probability

Revalued Data

Generalized Pareto

u.k. claIms severIty

20


Severity Curve LibraryAon Benfield’s global network of actuaries and financial professionals has compiled a substantial library of size of loss distributions by country and line of business. These curves are available to clients for use in their pricing, reinsurance evaluation and economic capital modeling, and they can be easily accessed within ReMetrica.

A sample of the distributions available includes:

* ISO and NCCI curves can be shared with clients subject to copyright restrictions.

Aon Benfield’s CRAFT software (part of the ReMetrica toolkit) fits loss severity distributions to loss experience data. Users enter a mixture of losses and constraints so distributions reflect all relevant data. CRAFT displays CDF plots of a range of fitted distributions to allow for a visual examination of the fits.

CRAFT Features:

Supports the most commonly used distributions such as the normal, lognormal, Pareto, Weibull, extreme value, >exponential, and gamma

Allows fitting against subsets of the data, excluding any outliers >

Uses Kaplan-Meier estimator to work with censored and truncated data >

Three measures of goodness of fit: maximum likelihood estimate, sum of squares and Kolmogorov-Smirnov >statistics

Facilitates the parameterization of economic capital models >

Integrates with ReMetrica, @Risk and Microsoft Excel spreadsheets >

U.S. - Hospital PI by State

U.S. - Physician PI by State

U.S. - Public D&O

U.S. - EPL

U.S. - ISO CGL*

U.S. - ISO Commercial Motor*

U.S. - NCCI Workers Comp*

Australia - GL

Australia - Motor

Australia - PI / D&O

Australia - Workers Comp

Belgium - Motor

Brunei - Liability

Czech Republic - Motor

Czech Republic - Property

Denmark - Motor

Finland - Motor

France - Motor

Germany - Motor

Hong Kong - Liability

Hungary - Motor

Hungary - Property

Ireland - Motor

Italy - Motor

Malaysia - Motor

Norway - Motor

Poland - Motor

Poland - Property

Portugal - Motor

Singapore - Liability

Singapore - Motor

Singapore - Workers Comp

Slovakia - Motor

Slovakia - Property

Spain - Motor

Sweden - Motor

U.K. - Employers Liability

U.K. - Motor

U.K. - Regional PI

U.K./International - CGL

Non-U.S. - PI

Non-U.S. - Financial PI

21

aon BenfIeld

Coda: Reserve RiskReserves are the major source of risk to the balance sheet for many insurers. (This discussion applies to companies required to book management’s best estimate reserves on a nominal basis. Other considerations may apply when discounting reserves and adding risk loads.) Reserve risk is insidious; it is harder to model and mitigate than catastrophe risk. Reserve deficiencies are notoriously highly correlated, both between lines and between years.

The magnitude of reserve risk is illustrated by the fact that through the end of 2008 the U.S. insurance industry has taken over $50 billion of net reserve development on accident years 2002 and prior. By comparison, the 9/11 terrorist attacks produced a gross industry loss of $40 billion and Hurricane Katrina in 2005 a gross loss of approximately $45 billion.

There are no standard reserve risk computer models paralleling catastrophe risk models, although there are a number of promising approaches. In part this is because reserve risk is very idiosyncratic: it is correlated across companies but varies substantially by company.

Development between lines over time is highly correlated. The matrix below shows this correlation for U.S. aggregate industry data. It shows very strong positive correlation across the board – even more marked than the correlations shown for accident year loss ratios on page eight.

The dependence relationship between a company’s first estimate of a new accident year and prior year development is very important for capital modeling. The linear correlation between these variables is unstable and not predictive. For example, the graph on the next page shows results for the total U.S. statutory market. It plots ultimate accident

ho

meo

Wn

er

s

pe

rso

na

l a

ut

o

lIa

BIl

Ity

co

mm

er

cIa

l m

ult

I p

er

Il

co

mm

er

cIa

l a

ut

o

ot

he

r

lIa

BIl

Ity

oc

c

Wo

rk

er

s c

om

p

me

dIc

al

ma

lp

ra

ct

Ice

cm

ot

he

r

lIa

BIl

Ity

cm

to

ta

l

Homeowners 100% 49% 31% 62% 15% 47% 47% 8% 60%

Personal Auto Liability 49% 100% 29% 58% 31% 83% 55% 54% 79%

Commercial Multi Peril 31% 29% 100% 69% 67% 54% 87% 44% 68%

Commercial Auto 62% 58% 69% 100% 27% 60% 81% 18% 67%

Other Liability Occ 15% 31% 67% 27% 100% 69% 67% 87% 74%

Workers Comp 47% 83% 54% 60% 69% 100% 80% 82% 91%

Medical Malpractice CM 47% 55% 87% 81% 67% 80% 100% 55% 83%

Other Liability CM 8% 54% 44% 18% 87% 82% 55% 100% 72%

Total 60% 79% 68% 67% 74% 91% 83% 72% 100%

22


55% 60% 65% 70% 75% 80%

-6%

-4%

-2%

0%

2%

4%

6%

8%

19971998

1999

2000

2001

2002

2003

2004

2005

20062007

2008

Cale

ndar

Yea

r Los

s Re

serv

e De

velo

pmen

tAccident Year Ultimate Loss Ratio at 12 Months

year loss ratio at 12 months on the x-axis against calendar year development to premium for all prior accident years on the y-axis. The graph shows 2001 was booked initially at 77 percent and that during 2001 there were 3.6 calendar year loss ratio points ($10 billion) of development on 2000 and prior accident years. The linear correlation between the variables is only -7 percent, not statistically different from zero.

Nonetheless, there is a functional dependency in the data. This reflects the actuarial and behavioral realities of the reserving, underwriting and planning processes. Soft markets are characterized by high initial loss ratios which in hindsight are too aggressive. Conversely, hard markets are characterized by low initial loss ratios which turn out to be too conservative. As a result, loss development can occur whether the initial accident year ultimate loss ratio is high or low. The graph indicates that the market has come full circle and may be close to a turning point (consistent with the premium to GDP analysis on page seven).

This graph is an archetype; the same pattern appears for major casualty lines and many individual company results. It shows why linear correlation does not work: the relationship is neither linear nor necessarily continuous and creates a difficult modeling problem. Aon Benfield Analytics continues to research ways to model reserve development appropriately at the individual company and line of business level.

About Aon BenfieldAs the industry leader in treaty, facultative and capital markets, Aon Benfield is redefining the role of the reinsurance intermediary

and capital advisor. Through our unmatched talent and industry-leading proprietary tools and products, we help our clients to

redefine themselves and their success. Aon Benfield offers unbiased capital advice and customized access to more reinsurance and

capital markets than anyone else. As a trusted advocate, we provide local reach to the world’s markets, an unparalleled investment

in innovative analytics, including catastrophe management, actuarial, and rating agency advisory, and the right professionals

to advise clients in making the optimal capital choice for their business. With an international network of more than 4,000

professionals in 50 countries, our worldwide client base is able to access the broadest portfolio of integrated capital solutions and

services. Learn more at aonbenfield.com.

reserve dependency

23

aon BenfIeld

sources: a.m. Best, anIa (Italy), axco Insurance InformatIon servIces, BafIn dataBase (germany), Bank negara malaysIa, BloomBerg, Bureau of economIc analysIs (u.s.), Bureau of laBor statIstIcs (u.s.), danIsh fsa and If p&c, dIreccIón general de seguros (spaIn), the economIst, federacIon de aseguradores colomBIanos, f Inma (sWItzerland), fma (austrIa), fsa returns (u.k.), “handBook on IndIan Insurance statIstIcs” (ed. Idra), hkocI (hong kong), http://WWW.Bapepam.go.Id/perasuransIan/Index.htm (IndonesIa), Ica macro statIstIcs (australIa), Insurance superIntendency (peru), korea fInancIal supervIsory servIce, monetary authorIty of sIngapore, msa research Inc. (canada), quest data report (south afrIca), snl (u.s.), “the statIstIcs of Japanese non-lIfe Insurance BusIness” (ed. Insurance research InstItute), susep (BrazIl), taIWan Insurance InstItute, yahoo! f Inance, yearBooks of chIna’s Insurance, and annual f InancIal statements.

For more information on the Insurance Risk Study, ReMetrica®, S2Metrica Solvency II Model or our analytic capabilities, please contact your local Aon Benfield broker or:

stephen mIldenhall

Head of Analytics, Americas t: +1 312 381 5880 e: [email protected]

John mooreHead of Analytics, Internationalt: + 44 (0) 20 7522 3973e: [email protected]

paul maItlandReMetrica, Internationalt: +44 (0) 20 7522 3932e: [email protected]

mIchael mcclaneReMetrica, U.S.t: +1 215 751 1596e: [email protected]

This document is intended for general information purposes only and should not be construed as advice or opinions on any specific facts or circumstances. The analysis and comments in this summary are based upon Aon Benfield’s preliminary analysis of publicly available information. The content of this document is made available on an “as is” basis, without warranty of any kind. Aon Benfield disclaims any legal liability to any person or organization for loss or damage caused by or resulting from any reliance placed on that content. Aon Benfield reserves all rights to the content of this document. Members of the Aon Benfield Analytics Team will be pleased to consult on any specific situations and to provide further information regarding the matters discussed here.

200 E. Randolph Street, Chicago, Illinois 60601 t: +1 312 381 5300 | f: +1 312 381 0160 | www.aonbenfield.com

Copyright Aon Benfield Inc. 2009 | #2735 - 08/2009

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