Insurance Risk StudyFifth Edition 2010

reDEFININGCapital | Access | Advocacy | Innovation

Portfolio Risk

SystemicMarket Risk Systemic

InsuranceRisk

Naïve Model

Insurance Portfolio RiskAsset Portfolio Risk

Contents

3 | Foreword

4 | Global Risk Parameters

6 | Evaluating Solvency II Factors

8 | U.S. Risk Parameters

10 | Best of Times, Worst of Times

12 | Correlation and the Pricing Cycle

16 | Modeling Dependence

17 | Size and Correlation

18 | Macroeconomic Correlation

19 | Managing Inflation Risk

21 | Global Market Review

25 | Afterword: The Greatest Risk

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. The Insurance Risk 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.

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 below. In the same way, insurers can reduce underwriting volatility by increasing portfolio 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 (right chart below). The Study calculates this systemic risk by line of business and country. The Naïve Model on the right chart 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.

Number of Stocks

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ForewordSince the first internal Aon Benfield Insurance Risk Study in 2003, the insurance world has been shaken by mega-catastrophes and threatened by financial market turmoil. Industry best practice in enterprise risk management has evolved almost beyond recognition, and techniques for risk quantification and capital modeling have advanced from nascent specialties into mainstream core competencies.

Yet despite change and progress, much remains constant. Risk quantification still relies fundamentally on accurate parameterization and realistic stochastic models. An incorrectly specified model is often worse than no model at all. Bad models can lead the user astray, as was shown by numerous examples during the financial crisis. Aon Benfield has consistently focused on the need to provide our clients the robust data and fact-based parameters published in this Study to complement state-of-the-art financial modeling tools such as our ReMetrica® software.

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

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

> Our global risk and capital strategy practice, providing ERM and economic capital services, uses the Study to benchmark risk, quantify capital adequacy and allocate capital to risk drivers

> Our ReMetrica risk evaluation and capital modeling software provides easy access to the Study parameters and risk insights

2010’s Fifth Edition has again expanded in scope and coverage from previous editions. It includes:

> Results from 46 countries, comprising more than 90 percent of global premium

> A global market review showing premium, historical loss ratio and volatility parameters for the top 50 countries

> A new approach to loss ratio volatility that measures year-over-year changes illustrating the magnitude of historical planning misses

> A focus on the potential impact of inflation on P&C companies

The massive database underlying the Study is supported by more than 450 professionals within the global Analytics team who are available to work with you to customize the basic parameters reported here to answer your specific, pressing business questions.

Aon Benfield’s Insurance Risk Study, now in its fifth edition, continues to be the industry’s leading publicly available set of risk parameters for modeling and benchmarking underwriting risk. We are pleased to offer the Study for the advancement of risk management within our industry. For convenient reference, you can find earlier editions of the Study at aonbenfield.com. I welcome your thoughts and suggestions, which you can share with an e-mail to stephen.mildenhall@aonbenfield.com.

Stephen Mildenhall CEO, Aon Benfield Analytics

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Insurance Risk Study

TaiwanMexico

PeruGreece

SingaporeHong Kong

NicaraguaIndonesiaHondurasColombiaRomania

BrazilArgentina

Dominican RepublicEcuador

U.S.Slovakia

NetherlandsPoland

South KoreaHungary

BoliviaVietnam

El SalvadorUruguay

VenezuelaFranceTurkey

IndiaJapan

MalaysiaChina

CanadaChile

DenmarkU.K.

PanamaSpain

AustriaGermany

SwitzerlandItaly

AustraliaSouth Africa

Israel

NicaraguaGreece

Hong KongPanama

IndonesiaSingapore

Dominican RepublicSlovakia

South AfricaDenmarkRomaniaEcuador

HondurasEl SalvadorVenezuela

CanadaVietnam

PeruU.S.

PolandColombiaMalaysiaUruguay

BrazilIndiaChile

NetherlandsChina

U.K.Bolivia

ItalySpain

ArgentinaFrance

MexicoGermany

SwitzerlandCzech Republic

AustriaAustralia

IsraelSouth Korea

TaiwanTurkeyJapan

Hungary 8%13%

16%17%18%18%18%19%21%22%22%

25%26%27%28%28%

31%31%32%

36%37%38%38%38%

40%42%42%43%44%45%46%

51%51%53%54%54%

58%58%

62%67%

71%73%

91%92%

98%

4%5%6%7%7%7%8%8%8%9%9%9%9%9%9%10%10%11%11%11%12%12%12%13%

15%15%15%16%16%

18%18%18%18%18%

21%23%24%25%25%25%

27%29%

35%43%

46%64%

Americas Asia Pacific Europe, Middle East & Africa

Motor Property

Global Risk Parameters

The 2010 Insurance Risk Study quantifies the systemic risk by line for 46 countries worldwide, up from 26 last year. 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 comes 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.

The systemic risk factors for major lines by region appear on the next page. Detailed charts comparing motor and property risk by country appear below. The factors measure the volatility of gross loss ratios. If gross loss ratios are not available the net loss ratio is used.

Coefficient of Variation of Gross Loss Ratio by Country

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Underwriting Volatility for Major Lines by Country, Coefficient of Variation of Loss Ratio for Each Line

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Americas

Argentina 9% 51% 61% 116% 164%Bolivia 10% 38% 18%Brazil 12% 53% 48% 60% 57% 45% 43% 58%Canada 18% 26% 18% 41% 37% 43% 72% 110% 116%Chile 12% 25% 51% 65%Colombia 15% 54% 55% 57% 71%Dominican Republic 25% 51% 120% 64%Ecuador 21% 46% 49% 178%El Salvador 18% 38% 21% 100%Honduras 18% 58% 5% 193%Mexico 9% 92% 65% 43%Nicaragua 64% 62% 91% 107%Panama 35% 21% 24% 103%Peru 16% 91% 60% 7% 21% 68% 77%Uruguay 13% 37% 124%U.S. 16% 14% 24% 45% 51% 34% 37% 52% 39% 28% 70%

Venezuela 18% 36% 23% 160%

Asia Pacific

Australia 8% 16% 23% 32% 54% 10% 30%China 11% 11% 27% 31% 19% 16% 113%Hong Kong 43% 44% 67% 82% 24% 60% 81%India 12% 12% 31% 14% 31%Indonesia 29% 29% 58% 124% 56% 72% 55% 92%Japan 5% 28% 10% 8% 17% 7%Malaysia 15% 28% 126% 30% 40% 88%Singapore 27% 71% 52% 57% 46%South Korea 7% 7% 42% 32% 55%Taiwan 7% 7% 98% 53% 33% 71% 44%Vietnam 18% 38% 41% 11% 38%

Europe, Middle East & Africa

Austria 8% 18% 12% 52% 21% 13% 21% 51%

Czech Republic 8%

Denmark 24% 22% 18% 33% 18% 16% 39% 23%

France 9% 32% 35% 26% 30% 25% 57%

Germany 9% 18% 20% 31% 29% 14% 22% 43%

Greece 46% 73% 81% 81%

Hungary 4% 40%

Israel 7% 8% 53%

Italy 10% 17% 25% 12% 46% 40% 72%

Netherlands 11% 43% 26% 49% 54% 41%

Poland 15% 42%

Romania 23% 54%

Slovakia 25% 44%

South Africa 25% 13% 61% 33% 46%

Spain 9% 19% 10% 23% 30% 13% 34% 48% 97%

Switzerland 9% 18% 21% 7% 50% 73%

Turkey 6% 10% 31% 44% 36% 15% 93% 52%

U.K. 11% 10% 18% 22% 21% 26% 30% 8% 67%

Reported CVs are of gross loss ratios, except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Uruguay, and Venezuela, which are of net loss ratios.

Accident & Health is defined differently in each country; it may include pure accident A&H coverage, credit A&H, and individual or group A&H. In the U.S., A&H comprises about 80 percent of the ”Other” line of business with the balance of the line being primarily credit insurance.

6

Insurance Risk Study

Solvency II is scheduled to take effect no later than January 1, 2013. The fifth quantitative impact study (QIS 5) is in progress with a deadline of October 2010 for individual insurers and mid-November 2010 for groups.

QIS 5 is likely the key test for most insurers across Europe. The Standard Formula factors were designed to be appropriate for the entire market — meaning a typical company of average size — so larger insurers with greater diversification will find the formula generates conservative capital requirements. Moreover, in QIS 5, the formula reflects added concerns that emerged from the 2008 financial crisis. Insurers now have heightened incentives to develop full or partial internal models as an alternative to the Solvency II Standard Formula.

At this stage, the most important aspect of preparing for Solvency II is correct parameterization, driven by access to data. Insurers may face serious challenges to their IT systems. They will need some reference point as they undertake the various Solvency II tests: evaluating the statistical quality of the data, calibrating and validating the models they are using.

The non-life Solvency Capital Requirement (SCR) is predominantly driven by premium risk, reserve risk and catastrophe risk. Since many companies with catastrophe exposure purchase excess of loss reinsurance, premium and reserve risk will be the key drivers of capital.

Solvency II vs. Insurance Risk StudyThere are four key differences between the Solvency II factors and those in the Insurance Risk Study.

Key Differences

Standard Deviations vs. Coefficients of Variation

In Solvency II, the premium risk factors are calculated as standard deviations of historical loss ratios. Within the Standard Formula, these standard deviations are applied on the total volume of premium rather than to the premium net of loadings for costs, commissions and profit.

Whether this overestimates the risk, and thus the capital requirement, depends on the company and the line of business. Certainly the Regulator has made some conservative assumptions: expenses are assumed to have the same volatility as the losses, and no profit is assumed over the cycle. If insurers disagree with these assumptions they must apply for a partial internal model.

One-Year Emergence vs. View of Ultimate

The Standard Formula premium risk factors and the corresponding Insurance Risk Study factors appear below. For ease of comparison, we have restated our factors as standard deviations rather than coefficients of variation.

Non-Life Premium Risk, Gross of Reinsurance

The factors proposed by CEIOPS and those used in the QIS 5 exercise can be made comparable with the factors in this Study through appropriate adjustments. If we use Motor TPL as an example, the CV in this Study corresponds to a standard deviation of 12.0 percent. The Study standard deviation is calculated from an ultimate perspective. We can use the same dataset that was used in our analysis to recalculate it from a one-year perspective, producing a standard deviation of 8.7 percent. Finally, the Study parameter reflects the non-diversifiable premium risk for a large insurance company whereas the QIS 5 parameters used in the Standard Formula represent an average-sized insurance company. As expected the QIS 5 parameter, 10.0 percent, is higher than systemic-only parameter of 8.7 percent for motor TPL.

Solvency II Insurance Risk Study

Standard deviations of gross loss ratios

Coefficients of variation of gross loss ratios

Based on loss ratios at end of first year

Based on ultimate loss ratios

Excludes catastrophe risk Includes catastrophe risk

Average-sized company, parameter and process risk

Large company, parameter risk only

QIS 5 CEIOPS Risk Study

Line StDev StDev # Obs StDev # Obs

Motor — TPL 10.0% 11.5% 209 12.0% 4,631

Motor — Other 7.0% 8.5% 107 n/a n/a

Marine, Aviation & Transit

17.0% 23.0% 37 29.6% 2,623

Fire 10.0% 15.0% 138 17.4% 4,751

General Liability 15.0% 17.5% 101 19.9% 3,443

Credit 21.5% 28.0% 58 27.6% 570

Evaluating Solvency II Factors

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Solvency II Correlation CoefficientsNot surprisingly, correlation will be an important determinant of capital requirements.

Solvency II Correlation Coefficients

These coefficients are more conservative than we would derive from calculating linear correlation since they must consider nonlinear tail correlation. The factors applied were derived mainly from an analysis of German market data for the years 1998 through 2002. As an example, for the correlation between motor TPL and general liability, the average correlation was 28 percent using the data of 89 firms and 1,269 observations. The final coefficient selected was 50 percent, as seen above.

In this case, we find that the Solvency II correlations are significantly higher than many of the observed correlations for European insurers. The correlation matrix for Germany appears below, and corresponds to the larger matrix on page 13 of this Study.

Insurance Risk Study — Germany

S2Metrica: ReMetrica Modeling for Proposed Solvency IIDeveloping an internal model can be a significant investment of time and resources. To assist our clients, Aon Benfield has developed S2MetricaSM, a standalone tool built on ReMetrica® technology. S2Metrica builds a simplified internal model from QIS 5 inputs, supplemented with details about large losses, cat losses, reinsurance, and the asset portfolio. It also has a built-in economic scenario generator. Standard output reports include:

> Profit and loss accounts for different return periods

> Year-end balance sheets

> Comparisons between Standard Formula capital requirements and those generated by the S2Metrica internal model

Using S2Metrica, clients can quickly construct a competent baseline model, freeing them to focus on critical tasks such as parameterization and appropriate customization.

ReMetrica is Aon Benfield’s innovative financial modeling tool and the engine of S2Metrica. Insurers increasingly turn to financial modeling to help them achieve their corporate objectives. Each insurer has its own distinct objectives, risks, corporate structure, and reinsurance strategy.

Using the ReMetrica software platform, insurers can build adaptable and flexible models that capture their risks better than the Solvency II Standard Formula and fully recognize their risk mitigation strategies. In particular, ReMetrica allows insurers to:

> Create ”off-the-shelf” internal models that cover both assets and liabilities

> Use customizable templates to monitor internal metrics and Solvency II requirements such as Fair Value and SCR

> Model highly customized reinsurance structures

> Integrate partial models, such as non-life and health lines, in a full internal model covering all aspects of the balance sheet

The combination of the Insurance Risk Study with ReMetrica allows our clients to parameterize their models in an optimal way and to make informed decisions about risk transfer through reinsurance or the capital markets.

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Motor — TPL 50% 50% 25% 50% 25%

Motor — Other 50% 25% 25% 25% 25%

Marine, Aviation & Transit

50% 25% 25% 25% 25%

Fire 25% 25% 25% 25% 25%

General Liability 50% 25% 25% 25% 50%

Credit 25% 25% 25% 25% 50%

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Motor 20% 7% 6% 26%

Marine, Aviation & Transit

20% 22% 10% 45%

Property 7% 22% 0% 31%

General Liability 6% 10% 0% -3%

Credit 26% 45% 31% -3%

8

Insurance Risk Study

Financial Guaranty

Special Property

Products Liability – Claims-Made

Reinsurance – Financial

Reinsurance – Property

Fidelity & Surety

International

Reinsurance – Liability

Other

Homeowners

Products Liability – Occurrence

Other Liability – Claims-Made

Medical PL – Claims-Made

Special Liability

Other Liability – Occurrence

Commercial Multi Peril

Medical PL – Occurrence

Warranty

Workers Compensation

Commercial Auto

Auto Physical Damage

Private Passenger Auto 14%

17%

24%

28%

31%

32%

34%

37%

39%

40%

43%

47%

51%

52%

67%

68%

70%

85%

91%

102%

104%

163%

13%

15%

17%

19%

31%

32%

27%

25%

29%

28%

29%

32%

43%

49%

45%

54%

54%

54%

59%

47%

62%

106%

Coefficient of Variation of Gross Loss Ratio | 1992-2009

Impact of the Pricing Cycle

Line Impact of the Pricing Cycle

Reinsurance — Liability 50%

Other Liability — Occurrence 47%

Other Liability — Claims-Made 47%

Workers Compensation 46%

Medical PL — Claims-Made 43%

Commercial Auto 42%

Special Liability 33%

Commercial Multi Peril 24%

Homeowners 21%

Private Passenger Auto 9%

All Risk No Underwriting Cycle Risk

U.S. Risk ParametersThe U.S. portion of the Insurance Risk Study uses data from nine years of NAIC annual statements for 2,265 individual groups and companies. The database covers all 22 Schedule P lines of business and contains 1.4 million records of individual company observations from accident years 1992-2009.

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 removed by normalizing loss ratios by accident year prior to computing volatility. This adjustment decomposes loss ratio volatility into its loss and premium components.

The U.S. Underwriting CycleVolatility 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 demonstrates that the cycle increases volatility substantially for all major commercial lines, as shown in the table. For example, the underwriting volatility of other liability increases by 47 percent, workers compensation by 46 percent, medical professional liability by 43 percent, and commercial auto liability by 42 percent.

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Aon Benfield

U.S. Reserve Volatility by Line

Ultimate reserve CV calculated using the Mack method applied to industry paid triangles by line. One-year reserve CV uses the Merz Wuthrich method. Both methods adjusted to account for reserves more than 10 years old.

Line Reserve to Premium Ratio

% Reserves Over 10 Yrs Old

Ultimate Reserve CV

One Year Reserve CV

% CV Emerging in

One Year

One Year Combined

Ratio ImpactHomeowners 0.3 1.0% 5.1% 4.8% 94.0% 1.5%

Private Passenger Auto 0.9 4.0% 2.1% 1.7% 79.1% 1.5%

Commercial Auto 1.5 4.9% 2.5% 1.8% 69.4% 2.6%

Commercial Multi Peril 1.2 9.0% 4.6% 3.8% 81.4% 4.6%

Workers Compensation 3.4 26.0% 3.3% 2.2% 66.0% 7.5%

Medical PL - CM 2.5 2.7% 5.1% 4.0% 78.9% 10.0%

Other Liability - Occ 3.4 26.2% 5.2% 3.2% 62.1% 10.8%

Other Liability - CM 2.5 2.3% 6.3% 5.1% 79.7% 12.8%

Products Liability - Occ 7.0 47.4% 9.9% 5.0% 50.9% 35.1%

Industry Reserve Adequacy: How Long Can Favorable Development Continue?

U.S. P&C industry reserves continue to show redundancy at the end of 2009. Standard actuarial reserving methods applied to the industry Schedule P indicate that there is approximately $22 billion of excess reserves across all lines of business.

The market is unlikely to harden again as long as the industry has more than adequate reserves according

to these metrics. Calendar years 2007, 2008 and 2009 saw favorable reserve development, helping to prolong soft market conditions. We estimate that reserve redundancies will be depleted in two to three years if favorable development continues at the pace it has from 2007 to 2009.

A summary of adequacy by major market segments appears below.

U.S. Reserve Estimated Adequacy ($B)

Line Estimated Reserves

Booked Reserves

Favorable/(Adverse) Development Remaining Redundancy

Years at Run Rate2007 2008 2009 Average

Personal Lines 123.0 129.0 5.9 5.4 5.8 5.7 6.0 1.1

Commercial Property 37.9 41.7 1.7 2.6 2.4 2.2 3.8 1.7

Commercial Liability 223.1 237.3 1.0 5.2 3.8 3.3 14.2 4.3

Workers Compensation 114.8 114.0 1.0 1.1 (0.5) 0.6 (0.8) n/a

Total Excl. Financial Guaranty 498.7 522.0 9.5 14.4 11.5 11.8 23.2 2.0

Financial Guaranty 34.1 32.8 (1.2) (12.6) 7.0 (2.3) (1.4) n/a

Total 532.9 554.7 8.3 1.7 18.6 9.5 21.9 2.3

Reserve Risk and LeverageInsurers face two sources of risk from reserves. The first source is volatility of reserve values over time to settlement. The second source comes from leverage. The longer the average duration of the claim payout, the larger the reserve balance becomes relative to the premium base. As reserve leverage increases, the sensitivity of calendar year combined ratio results compared to changes in reserve balances magnifies.

The first source of volatility has traditionally been measured with methods such as the Mack method, which calculates the volatility of the link ratio estimate of ultimate losses coming from a loss triangle. More recently, Merz and Wuthrich have published a methodology that calculates the same estimate, but over a one-year time horizon to be consistent with Solvency II. Using these methods, we can estimate the total reserve volatility and how much of that volatility is

expected to emerge in the next 12 months. We can also estimate the potential impact of reserve volatility on next year’s combined ratio.

Using the U.S industry aggregate workers compensation paid triangle as an example, the total reserve volatility is estimated at 3.3 percent based on an adjusted Mack method. The one-year reserve volatility is 2.2 percent based on an adjusted Merz Wuthrich method, meaning that 66 percent of the triangle’s volatility emerges in one development year. The Mack and Merz Wuthrich methods were both adjusted to account for reserves more than 10 years old. With reserves levered at 3.4 times premium, the impact on combined ratio of a one standard deviation change is 7.5 points. For a normal distribution a one standard deviation move, up or down, is a one in three year event.

10

Insurance Risk Study

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9% 14% 18% 23% 29% 36% 38% 39% 42% 44% 45% 48% 51% 56% 66% 67% 73% 78%

125%170% 499%

-12% -18% -25% -25%-41% -46% -52% -41% -32%

-72% -64% -67% -67%-93%

-123%

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-262%

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Biggest Increase (Deteriorating Results)

Biggest Decrease (Improving Results)

Mean Year-Over-Year Change in Loss Ratio

Best of Times, Worst of TimesIn economic capital modeling, the systemic volatility of each insurance line is a vital input to ensure that the parameters reflect an appropriate level of risk. As a result, we have always focused on quantifying this systemic volatility by measuring the CV. However, the CV offers guidance only on the variance of the resulting loss distribution; it does not offer a clear view of the distribution’s shape. Two insurance lines may have similar CVs but one may have a much thicker tail than the other.

To expand our view of insurance risk beyond the CV, we have studied the largest deviations in loss ratio between successive accident years. The 18 accident years in our dataset give us 17 years of changes, and with this data we selected the biggest increase and decrease in ultimate accident year loss ratio for each company. For example, on the following page commercial auto insurers showed on average a biggest increase (deteriorating results) of 18.3 loss ratio points and a biggest decrease (improving results) of 25.1 loss ratio points from one accident year to the next. The chart below shows mean changes for all U.S. lines.

Overall this analysis is consistent with the analysis of CVs. Personal and commercial auto show the smallest fluctuation in results, followed by the other commercial lines. The catastrophe-exposed lines — homeowners, special property, reinsurance property, and financial guaranty — comprise the top end of the range, with a mean worst increase of 60 loss ratio points or higher on a gross basis.

For most lines, the increases are significant. Among the commercial lines, the results show a mean increase of 44 percent for other liability claims-made, 39 percent for other liability occurrence, 36 percent for commercial multi-peril, 29 percent for medical professional liability claims-made, 23 percent for workers compensation, and 18 percent for commercial auto.

The following page shows detailed results for commercial auto, other liability occurrence and workers compensation. The impact of the underwriting cycle is clearly visible in all three lines, as insurers suffered their biggest increases from 1998 to 2000 and their biggest decreases in 2002 after the market hardened. Differences in volatility between lines are also visible. At 39.5 percent, the mean increase for other liability occurrence is double that of commercial auto; moreover, its distribution is more positively skewed with a 90th percentile of 78.4 loss ratio points compared with 32.5 points for commercial auto.

In planning, insurers may implicitly assume that loss ratios will be within two or three points of best estimates. But the evidence shows that results are not infrequently off by 20 points or more, on both the hard and soft sides of the cycle. This deviation is further exacerbated because initial booked loss ratios are generally near plan and vary little from year to year. The output from any financial modeling should reflect a realistic view of outcomes that can deviate from plan. The results of this extreme value analysis can serve as useful benchmarks for evaluating model results.

11

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Commercial Auto

Other Liability - Occurrence

Workers Compensation

Frequency by Accident Year

Frequency by Accident Year

Frequency by Accident Year

Year-Over-Year Change in Gross Loss Ratio

Probability Density

Probability Density

Probability Density

Biggest Increase

Mean 18.3%Median 15.1%90th %ile 32.5%

Biggest Increase

Mean 39.5%Median 24.8%90th %ile 78.4%

Biggest Increase

Mean 23.1%Median 20.1%90th %ile 38.9%

Biggest Decrease

Mean -25.1%Median -20.1%90th %ile -45.2%

Biggest Decrease

Mean -40.8%Median -27.4%90th %ile -71.5%

Biggest Decrease

Mean -25.0%Median -20.5%90th %ile -46.0%

12

Insurance Risk Study

China

Acc

iden

t

& H

ealt

h

Ag

ricu

ltur

e

Cre

dit

Eng

inee

rin

g

Gen

eral

Li

abili

ty

Mar

ine,

A

viat

ion

&

Tra

nsi

t

Mot

or

Prop

erty

Accident & Health 24% 31% 39% 53% 38% 59% 56%

Agriculture 24% 53% n/a 29% 9% 16% -4%

Credit 31% 53% n/a 18% 18% 34% 25%

Engineering 39% n/a n/a 68% 38% 64% 28%

General Liability 53% 29% 18% 68% 29% 62% 54%

Marine, Aviation & Transit 38% 9% 18% 38% 29% 41% 24%

Motor 59% 16% 34% 64% 62% 41% 59%

Property 56% -4% 25% 28% 54% 24% 59%

Australia

Gen

eral

Li

abili

ty

Mar

ine,

A

viat

ion

&

Tra

nsi

t

Mot

or

Prop

erty

Wor

kers

C

omp

General Liability 19% 21% -24% 21%

Marine, Aviation & Transit 19% 31% -3% 21%

Motor 21% 31% 25% 14%

Property -24% -3% 25% -6%

Workers Comp 21% 21% 14% -6%

Correlation and the Pricing CycleCorrelation of Underwriting ResultsCorrelation between different lines of business is central to a realistic assessment of aggregate portfolio risk, and, in fact, becomes increasingly significant as companies grow in size. Modeling is invariably performed using an analysis-synthesis paradigm: analysis is carried out at the product or business unit level and 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.

Correlation between LinesCorrelation 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 Australia, China, Germany, Japan, the U.K., and the U.S.

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 percent confidence level.

13

Aon Benfield

U.S.

Com

mer

cial

A

uto

Com

mer

cial

M

ulti

Per

il

Hom

eow

ner

s

Med

ical

M

alp

ract

ice

CM

Oth

er L

iab

ility

C

M

Oth

er L

iab

ility

O

cc

Pers

onal

Aut

o Li

abili

ty

Prod

ucts

Li

abili

ty

Occ

Wor

kers

C

omp

Commercial Auto 53% 8% 73% 44% 67% 28% 72% 63%

Commercial Multi Peril 53% 21% 56% 41% 48% 28% 40% 42%

Homeowners 8% 21% 1% -2% -1% 8% 14% -7%

Medical Malpractice CM 73% 56% 1% 72% 78% 58% 76% 71%

Other Liability CM 44% 41% -2% 72% 57% 42% 29% 62%

Other Liability Occ 67% 48% -1% 78% 57% 33% 66% 63%

Personal Auto Liability 28% 28% 8% 58% 42% 33% 42% 33%

Products Liability Occ 72% 40% 14% 76% 29% 66% 42% 63%

Workers Comp 63% 42% -7% 71% 62% 63% 33% 63%

U.K.

Acc

iden

t

& H

ealt

h

Com

mer

cial

Li

nes

Lia

bili

ty

Com

mer

cial

M

otor

Com

mer

cial

Pr

oper

ty

Fin

anci

al L

oss

Hou

seh

old

&

Dom

esti

c

Priv

ate

Mot

or

Accident & Health 47% n/a 55% -49% 15% 55%

Commercial Lines Liability 47% 72% 40% 69% 49% 56%

Commercial Motor n/a 72% 51% -9% -14% 61%

Commercial Property 55% 40% 51% 33% 57% 39%

Financial Loss -49% 69% -9% 33% 16% -15%

Household & Domestic 15% 49% -14% 57% 16% 30%

Private Motor 55% 56% 61% 39% -15% 30%

Japan

Acc

iden

t

& H

ealt

h

Gen

eral

Li

abili

ty

Mar

ine,

A

viat

ion

&

Tra

nsi

t

Mot

or

Prop

erty

Wor

kers

C

omp

Accident & Health 27% 1% 50% 42% 53%

General Liability 27% 0% 3% 32% 28%

Marine, Aviation & Transit 1% 0% 16% 33% -4%

Motor 50% 3% 16% 61% 43%

Property 42% 32% 33% 61% 32%

Workers Comp 53% 28% -4% 43% 32%

Germany

Acc

iden

t

& H

ealt

h

Ass

ista

nce

Cre

dit

Gen

eral

Li

abili

ty

Leg

al

Prot

ecti

on

Mar

ine,

A

viat

ion

&

Tra

nsi

t

Mot

or

Prop

erty

Accident & Health 62% -37% 10% -13% -13% -12% -3%

Assistance 62% n/a -40% 39% 83% -6% -40%

Credit -37% n/a -3% -24% 45% 26% 31%

General Liability 10% -40% -3% -10% 10% 6% 0%

Legal Protection -13% 39% -24% -10% -48% 20% -21%

Marine, Aviation & Transit -13% 83% 45% 10% -48% 20% 22%

Motor -12% -6% 26% 6% 20% 20% 7%

Property -3% -40% 31% 0% -21% 22% 7%

14

Insurance Risk Study

Europe - Motor

Aus

tria

Belg

ium

Fran

ce

Ger

man

y

Ital

y

Net

her

lan

ds

Nor

way

Spai

n

Swit

zerl

and

Uni

ted

Kin

gd

om

Austria 52% 64% 65% 38% 87% 44% -12% 82% 12%

Belgium 52% 63% 79% 63% 45% 61% -41% 7% 34%

France 64% 63% 59% 41% 54% 45% -33% 49% 23%

Germany 65% 79% 59% 7% 54% 35% -29% 23% 10%

Italy 38% 63% 41% 7% -5% 71% -41% 17% 62%

Netherlands 87% 45% 54% 54% -5% 37% 1% 80% -19%

Norway 44% 61% 45% 35% 71% 37% -47% 41% 32%

Spain -12% -41% -33% -29% -41% 1% -47% 30% 7%

Switzerland 82% 7% 49% 23% 17% 80% 41% 30% -2%

United Kingdom 12% 34% 23% 10% 62% -19% 32% 7% -2%

Asia Pacific - Motor

Aus

tral

ia

Ch

ina

Hon

g Ko

ng

Ind

ia

Jap

an

Mal

aysi

a

Russ

ia

Sin

gap

ore

Sout

h Ko

rea

Taiw

an

Australia 63% 54% 44% -35% -49% 82% 8% -34% 21%

China 63% 24% -3% -7% 14% -30% -10% -20% 12%

Hong Kong 54% 24% 55% -46% -46% 5% 37% -67% -23%

India 44% -3% 55% -45% -32% 97% 13% -34% 33%

Japan -35% -7% -46% -45% 50% 85% -2% 68% -3%

Malaysia -49% 14% -46% -32% 50% 72% -8% 41% 33%

Russia 82% -30% 5% 97% 85% 72% 14% -35% -81%

Singapore 8% -10% 37% 13% -2% -8% 14% -35% -37%

South Korea -34% -20% -67% -34% 68% 41% -35% -35% 34%

Taiwan 21% 12% -23% 33% -3% 33% -81% -37% 34%

Americas - Motor

Arg

enti

na

Bra

zil

Can

ada

Ch

ile

Col

omb

ia

Mex

ico

Peru

Puer

to R

ico

Uni

ted

Stat

es

Ven

ezue

la

Argentina 49% -62% -71% -14% 23% 10% -13% -14% -28%

Brazil 49% -18% -5% -26% -3% -51% 34% 34% 32%

Canada -62% -18% 61% 22% -24% -27% 10% 85% 43%

Chile -71% -5% 61% 14% -13% -31% 14% 20% -4%

Colombia -14% -26% 22% 14% -28% 16% 37% 34% -5%

Mexico 23% -3% -24% -13% -28% 6% -47% -24% -58%

Peru 10% -51% -27% -31% 16% 6% -18% -21% -32%

Puerto Rico -13% 34% 10% 14% 37% -47% -18% 41% 7%

United States -14% 34% 85% 20% 34% -24% -21% 41% 41%

Venezuela -28% 32% 43% -4% -5% -58% -32% 7% 41%

Correlation between CountriesIn addition to correlation between lines of business, global insurers must also consider the correlation of business written in different countries. We estimated these correlation coefficients based on country-level loss ratios by line by year. The following tables show results by region for motor and liability lines.

15

Aon Benfield

Europe - Liability

Aus

tria

Belg

ium

Den

mar

k

Fran

ce

Ger

man

y

Ital

y

Nor

way

Spai

n

Swit

zerl

and

Uni

ted

Kin

gd

om

Austria -46% 55% 86% 88% 49% 19% -36% 29% 65%

Belgium -46% -29% -54% -35% -43% 21% -81% 13% -31%

Denmark 55% -29% 65% 60% 71% 16% 10% 29% 36%

France 86% -54% 65% 90% 61% 16% -26% 32% 59%

Germany 88% -35% 60% 90% 54% 5% -41% 27% 65%

Italy 49% -43% 71% 61% 54% 20% 15% 26% 5%

Norway 19% 21% 16% 16% 5% 20% -39% 3% 4%

Spain -36% -81% 10% -26% -41% 15% -39% -24% 12%

Switzerland 29% 13% 29% 32% 27% 26% 3% -24% 10%

United Kingdom 65% -31% 36% 59% 65% 5% 4% 12% 10%

Asia Pacific - Liability

Aus

tral

ia

Ch

ina

Hon

g Ko

ng

Jap

an

Mal

aysi

a

Russ

ia

Sin

gap

ore

Sout

h Ko

rea

Taiw

an

Australia 81% 46% -33% -11% -15% -30% 6% 32%

China 81% 36% -13% 22% 36% 91% -28% 17%

Hong Kong 46% 36% 1% -9% 2% -31% -15% -28%

Japan -33% -13% 1% 45% 7% 72% -3% 20%

Malaysia -11% 22% -9% 45% -57% 70% -35% 26%

Russia -15% 36% 2% 7% -57% -60% 65% 47%

Singapore -30% 91% -31% 72% 70% -60% -74% 10%

South Korea 6% -28% -15% -3% -35% 65% -74% 6%

Taiwan 32% 17% -28% 20% 26% 47% 10% 6%

Americas - Liability

Arg

enti

na

Bra

zil

Can

ada

Ch

ile

Col

omb

ia

Mex

ico

Peru

Puer

to R

ico

Uni

ted

Stat

es

Ven

ezue

la

Argentina 33% 18% -5% -67% 6% 3% 49% 30% 2%

Brazil 33% 33% 18% -59% 22% 15% 75% 72% -7%

Canada 18% 33% -17% -32% 25% 24% 26% 81% 5%

Chile -5% 18% -17% -28% 44% -12% 26% -1% 31%

Colombia -67% -59% -32% -28% -18% -26% -76% -67% -23%

Mexico 6% 22% 25% 44% -18% -3% 14% 23% -20%

Peru 3% 15% 24% -12% -26% -3% 0% 44% -18%

Puerto Rico 49% 75% 26% 26% -76% 14% 0% 69% 2%

United States 30% 72% 81% -1% -67% 23% 44% 69% 18%

Venezuela 2% -7% 5% 31% -23% -20% -18% 2% 18%

16

Insurance Risk Study

Dependence is a core component of economic capital modeling. Risk managers frequently discuss correlation, and this Study includes numerous correlation matrices. But correlation alone does not fully describe dependence. There are many ways to combine two variables to have the same linear correlation coefficient. For example, the familiar symmetric, elliptical contours of the normal copula can have the same linear correlation as a more pinched distribution, and pinching can occur either on the left, the right or both sides. The impact of dependence is most clearly seen in the distribution of the sum (or portfolio return) of the two variables, with extreme tail correlation producing an aggregate distribution with much fatter tails.

Variables in financial markets often exhibit such extreme tail correlation, as seen in the left chart below. In this plot, the outliers at the 10.0 percent and 1.0 percent significance levels assuming a multivariate normal distribution comprise 11.9 percent and 2.1 percent of the observations. This kind of behavior has led many analysts to reject the normal distribution as a model for dependence.

Academics and risk managers have introduced many different copulas as means of modeling dependence with flexible tail behavior. But the appropriateness of different copulas for insurance losses has been less well tested.

Our study of U.S. data shows that apart from correlation driven by property catastrophe events there is little evidence of multi-line extreme correlation. The right chart below compares results for other liability occurrence and workers compensation. In this case, the outliers at the 10.0 percent and 1.0 percent significance levels assuming multivariate normal distribution represent 10.9 percent and 1.0 percent of the observations — well within expectations. Analysis of other U.S. lines shows similar results.

There is still the possibility that events with long return periods are not shown in our 18-year data sample — for example, the impact of asbestos on other liability occurrence and products liability. We may yet observe extreme tail correlation in insurance results. But its absence during the past 18 years suggests that it is not nearly as commonplace as in financial markets.

We conclude that while the traditional approach to modeling dependence using the normal copula has known limitations, it is not rejected by the data as a model for correlation between non-catastrophe insurance lines. Catastrophe simulation models address this issue for catastrophe lines by including correlation as a model output.

Normal Transformed Data

-3.0

-2.0

-1.0

0

1.0

2.0

3.0

-3.0 -2.0 -1.0 0 1.0 2.0 3.0

Normal Transformed Data

-3.0

-2.0

-1.0

0

1.0

2.0

3.0

-3.0 -2.0 -1.0 0 1.0 2.0 3.0

Daily Stock Returns of Two Financial Stocks Through the Crisis

Products Liability Occurrence vs. Workers Compensation

Modeling Dependence

17

Aon Benfield

Size and Correlation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 100 1,000 10,000

Size Threshold, $M

Cor

rela

tion

Abo

ve T

hres

hold

Correlation

Risk Study Coefficient

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

10 100 1,000 10,000

Size Threshold, $M

Cor

rela

tion

Abo

ve T

hres

hold

Correlation

Risk Study Coefficient

Workers Compensation vs. Other Liability — Occurrence Commercial Auto vs. Other Liability — Occurrence

The table below shows the measured correlation coefficients at different premium thresholds between U.S. Schedule P lines. In each case, both premium amounts exceed the threshold.

Line of Business Correlation by Premium Size Threshold

Line A Line B $25M $50M $100M $250M $500M $1,000M Homeowners Private Passenger Auto 10% 11% 8% 17% 33% 33%

Commercial Multi Peril Commercial Auto 33% 37% 53% 55% 73% 58%

Commercial Multi Peril Workers Compensation 27% 31% 42% 48% 48% 59%

Commercial Multi Peril Other Liability — Occ 22% 27% 48% 46% 53% 53%

Commercial Auto Workers Compensation 49% 60% 63% 71% 73% 85%

Commercial Auto Other Liability — Occ 51% 54% 67% 78% 82% 78%

Workers Compensation Other Liability — Occ 44% 51% 63% 67% 72% 80%

Other Liability — Occ Other Liability — CM 45% 50% 57% 55% 59% 65%

Medical PL — CM Other Liability — CM 65% 72% 72% 64% 68% n/a

Medical PL — CM Workers Compensation 47% 72% 71% 73% 77% n/a

The larger the company, the more important correlation becomes for the company. Regulators and rating agencies scrutinize correlation assumptions in their evaluations of capital adequacy. Aon Benfield Analytics can help companies understand the sensitivity of their model results to correlation assumptions and guide them during the rating agency review process.

Insurers of different sizes face different levels of correlation across their portfolios. For small insurers, the process risk in each line of business may keep the correlation observed between lines relatively low. In contrast, large insurers are exposed primarily to the systemic risk in each line, but correlation in systemic risk will drive observed correlations across the portfolio.

The U.S. correlation coefficients published earlier in the Study represent an average level of correlation for companies with premium volume above a threshold

of $100 million. We selected this threshold as representative of the size of a typical product division within a medium to large insurance company. The observed level of correlation varies within this threshold, as shown below for several pairs of lines. Companies with volume exceeding $100 million will observe an increasing level of correlation between lines. For example, between workers compensation and other liability occurrence, the correlation at $100 million is 63 percent, at $500 million it is 72 percent, and at $1 billion it is 80 percent.

18

Insurance Risk Study

Macroeconomic Correlations

Infla

tion

(C

PI-U

)

Infla

tion

(PP

I)

GD

P G

row

th

Une

mpl

oym

ent

Cha

nge

3-M

onth

T-

Bill

Rate

1-3

Year

T-

Bill

AA

A-A

A 3

-5

Year

Sp

read

BB

B 3

-5

Year

Sp

read

S&P

500

Retu

rns

VIX

Prop

erty

Re

turn

s

Inflation (CPI-U) 78% -3% -2% 32% 26% -11% -25% -12% -23% 13%

Inflation (PPI) 78% 4% -7% 30% 11% -4% -20% -7% -22% 14%

GDP Growth -3% 4% -70% -4% 25% -64% -69% 5% -44% 52%

Unemployment Change -2% -7% -70% -3% -27% 62% 77% -1% 57% -51%

3-Month T-Bill Rate 32% 30% -4% -3% 98% -34% -58% -6% -25% 13%

1-3 Year T-Bill 26% 11% 25% -27% 98% -39% -61% 19% -28% 10%

AAA-AA 3-5 Year Spread -11% -4% -64% 62% -34% -39% 85% -43% 62% -66%

BBB 3-5 Year Spread -25% -20% -69% 77% -58% -61% 85% -36% 67% -53%

S&P 500 Returns -12% -7% 5% -1% -6% 19% -43% -36% -51% 17%

Stock Volatility Index, VIX -23% -22% -44% 57% -25% -28% 62% 67% -51% -32%

Property Returns 13% 14% 52% -51% 13% 10% -66% -53% 17% -32%

Correlation among macroeconomic factors is a very important consideration in risk modeling. The interaction of inflation and GDP growth with loss ratios and investment returns has a profound effect on insurer financial health and stability.

The matrix below shows correlation coefficients for various macroeconomic variables that impact an insurer’s balance sheet.

The Consumer Price Index (CPI-U) and Producer Price Index (PPI) are highly correlated, but they do not show particularly strong correlation with other factors. This may be because inflation has been relatively tame for the last 25 years.

GDP growth shows strong negative correlation with changes in unemployment. When GDP drops — or unemployment increases — credit spreads tend to increase, property values fall and the VIX increases. We were surprised not to see stronger correlation between GDP and stock returns without a lag.

Treasury yields and corporate bond spreads are inversely correlated; financial market fears may push investors to flee corporates for the safety of treasuries, causing corporate yields to rise and treasury yields to fall.

Stock volatility measured by the VIX Index is sensitive to fear and directionally has the appropriate signs: positive correlation with spreads and unemployment, negative correlation with GDP and stock returns.

These coefficients represent only the beginning of an analysis of macroeconomic dependency. Lags may be appropriate among certain variables. For example, GDP and stock returns show the strongest correlation when stock returns lead GDP by two quarters, suggesting that stock prices adjust as soon as expectations for GDP change.

It is also important to consider values that shift over time. In successive eight-quarter periods, stock returns and property returns showed zero or negative correlations until the recent financial crisis when correlations turned strongly positive. This fact alone suggests that a simplistic view of correlation across the balance sheet will expose insurers to significant risks.

Model output is only as good as the assumptions used, and with the prevalence of DFA modeling and economic scenario generators there is potential for naïve assumptions to drive decision making. In the next section, we look more closely at the potential impact of inflation on insurer balance sheets.

Macroeconomic Correlation

19

Aon Benfield

Managing Inflation RiskRisk managers today recognize inflation as a potential threat in the years ahead, but struggle to quantify the risk and identify ways to mitigate it. The historical record reminds us that periods of high inflation have occurred repeatedly in virtually every economy. But during the last 25 years, inflation has been contained at low levels in the U.S. and other developed economies. As a result, the time series available to measure inflation’s impact on the current insurance industry will not serve us well in anticipating the next potential inflation shock.

Inflation 1914-2009

Impact on Insurers

Periods of high inflation, and high inflation volatility in particular, have generally preceded periods of rising accident year combined ratios.

Inflation and Combined Ratio

This lagged relationship between inflation and combined ratios is driven by three factors:

> Lags between the incidence of inflation rate changes and recognition in loss reserving systems and rate indications

> Lags between attempts to raise rates and actual rate changes due to regulatory and competitive limitations

> Immediate impact on balance sheets

Industry Balance Sheet Impact

In the table below we demonstrate the last of these three effects for U.S. insurers using the 2009 industry balance sheet and the sensitivity of bond holdings, equity holdings and nominal loss reserves to changes in inflation. We show that a 200 basis point increase in inflation could result in a $70.9 billion impact on surplus, a 13.7 percent decrease.

Impact of Inflation Increase on Industry Balance Sheet

The primary drivers of these changes are bonds, stocks and loss reserves.

Bonds — Changes in inflation affect bond yields differently for bonds of different maturities. Overall, a 200 basis point increase in inflation would be expected to decrease the value of the industry bond portfolio by 7.3 percent.

Stocks — Stock portfolios are often assumed to have a high sensitivity to changes in bond yields. However, since empirically 80 percent of changes in inflation expectations ultimately flow through the S&P 500 as higher nominal dividends, this significantly offsets the effect of discounting these dividends at higher yields. The overall affect is approximately a 6.0 percent decline in the value of a diversified equity portfolio for a 200 basis point change in inflation.

Loss Reserves — The impact of inflation, particularly when measured as changes in the broad CPI-U, varies by line of business. Many short-tailed lines are impacted directly, though modestly, as a result of their quick settlement. In contrast, long-tailed lines are impacted more significantly by components of the CPI-U, which results in a more muted relationship to general inflation. Overall, for the industry reserves, we estimate a 5.8 percent increase in undiscounted loss reserves for a 200 basis point increase in inflation.

-15%

-10%

-5%

0%

5%

10%

15%

20%

201019901970195019301910

0%

2%

4%

6%

8%

10%

12%

14%

90%

95%

100%

105%

110%

115%

120%

125%

20052000199519901985198019751970

Combined RatioInflation

Balance ($B)

Pre-tax Sensitivity

%

After-Tax Sensitivity

%

After- Tax Impact

($B)Assets

Bonds 866.3 -7.3% -4.7% -40.9

Stocks 227.0 -6.0% -3.9% -8.9

Other Assets 398.9 0.0% 0.0%

Total Assets 1,492.2 -5.1% -3.3% -49.7

Liabilities

Net Loss Reserves 564.0 5.8% 3.8% 21.2

Other Liabilities 411.4 0.0% 0.0%

Total Liabilitites 975.4 3.3% 2.2% 21.2

Surplus 516.8 -21.1% -13.7% -70.9

20

Insurance Risk Study

Managing Inflation RiskInsurers can seek to manage this risk in several ways.

Interest Rate Risk Management — An interest rate risk management process should distinguish between inflation duration and real interest rate duration, thus enhancing an asset-liability management framework. It is often assumed that asset and liability portfolios with equal present values and with the same duration will respond similarly on a discounted basis to changes in bond yields. However, this may not be the case if the changes are driven by changes in inflation rates. In the previous balance sheet example, a 200 basis point increase in inflation caused a $21.2 billion increase in the undiscounted loss reserves, and if interest rates rose as well, then there would also have been an increase in the amount of discounting leaving discounted reserves approximately unchanged.

On the asset side, a bond portfolio of comparable size and duration would have decreased by $17.9 billion as a result of the same change in interest rates. Despite being “matched”, the net effect would be a three percent decrease in surplus.

Asset Allocation — Several asset classes, including inflation-indexed bonds (TIPS), commodities and real estate, offer varying degrees of inflation hedging and can be considered as a natural part of insurers’ asset portfolios. However, careful consideration must be given to the impact on expected investment returns — in the case of TIPS especially — and to the additional volatility and portfolio management skills needed for these asset classes.

Equity Allocations — The conventional wisdom is that equities are a natural hedge against inflation, since companies can pass rising costs along to consumers. But over short horizons, equities have not always outperformed inflation. In the 1970s, inflation soared well above 10 percent even as real equity returns were negative. An investment in the S&P 500 made in 1973 would only have broken even in real dollar terms in 1986. When operating performance is measured over a five-year or ten-year period, these results suggest that insurers holding equities may face flat or even negative investment returns while their liabilities increase in value.

The experience of the 1970’s suggests that a broad index such as the S&P 500 may be less effective than a more carefully constructed equity portfolio. Sectors such as energy, medical services and defense offer a greater degree of inflation hedging than other sectors; value stocks also tend to perform better in inflationary environments than growth stocks. Equities do offer a degree of natural inflation hedging, but history suggests that risk managers should pay careful attention to sector and style allocations.

Reinsurance — Inflation is just one of many sources of volatility for liabilities. Rather than isolate and manage this risk separately, an alternative could be to incorporate aggregate stop loss or adverse development covers into reinsurance programs with coverage terms selected to respond appropriately to adverse inflation impacts on current or prior accident year claims.

0%

5%

10%

15%

20052000199519901985198019751970

-40%

-20%

0%

20%

40%

20052000199519901985198019751970

0%

1%

2%

3%

4%

20052000199519901985198019751970

Base Year = 1970

Inflation CPI-U, 1970-2009

S&P 500 Year 1-Year Change

S&P 500 Cumulative Real Index Value vs. CPI-U

21

Aon Benfield

Top 50 Markets by Gross Written Premium

Country P&C GWP ($B) GDP ($B) Premium/

GDP RatioGDP Per

Capita

U.S. 462.33 14,256.30 3.2% 45,954

Japan 76.12 5,067.53 1.5% 39,963

U.K. 71.93 2,174.53 3.3% 35,482

Germany 67.10 3,346.70 2.0% 40,673

France 62.66 2,649.39 2.4% 41,359

Italy 44.09 2,112.78 2.1% 36,370

China 42.10 4,909.28 0.9% 3,691

Spain 33.88 1,460.25 2.3% 36,012

S. Korea 32.55 929.12 3.5% 19,104

Canada 29.02 1,336.07 2.2% 39,576

Australia 21.40 924.84 2.3% 42,984

Brazil 17.02 1,571.98 1.1% 7,817

Netherlands 15.67 792.13 2.0% 47,198

Russia 12.48 1,230.73 1.0% 8,829

Switzerland 11.39 500.26 2.3% 65,621

Belgium 10.58 468.55 2.3% 44,952

Austria 9.13 384.91 2.4% 46,859

Norway 8.64 381.77 2.3% 81,638

Denmark 8.20 309.60 2.7% 56,131

Mexico 7.41 874.90 0.8% 7,779

Sweden 6.89 406.07 1.7% 44,751

Venezuela 6.34 314.15 2.0% 11,540

Poland 5.89 430.08 1.4% 11,181

Argentina 5.76 308.74 1.9% 7,468

Turkey 5.61 617.10 0.9% 7,931

India 5.01 1,296.09 0.4% 1,105

Ireland 4.80 227.19 2.1% 53,455

South Africa 4.66 285.98 1.6% 5,823

Portugal 4.49 227.68 2.0% 21,207

Czech Republic 4.24 190.27 2.2% 18,651

Finland 4.20 237.51 1.8% 45,197

U.A.E. 3.41 198.69 1.7% 39,933

Iran 3.38 286.06 1.2% 4,267

Greece 3.32 355.88 0.9% 33,105

Israel 3.28 194.79 1.7% 26,488

Malaysia 2.84 191.60 1.5% 7,324

Thailand 2.80 263.86 1.1% 3,973

Taiwan 2.77 355.47 0.8% 15,438

Luxembourg 2.73 52.45 5.2% 105,417

Colombia 2.54 230.84 1.1% 5,222

Ukraine 2.39 113.55 2.1% 2,500

Romania 2.29 161.11 1.4% 7,263

Indonesia 2.24 540.28 0.4% 2,224

New Zealand 2.05 125.16 1.6% 29,434

Hong Kong 1.98 215.36 0.9% 30,376

Chile 1.95 163.67 1.2% 9,773

Hungary 1.93 128.96 1.5% 13,053

Singapore 1.81 182.23 1.0% 38,764

Puerto Rico 1.74 67.90 2.6% 17,070

Saudi Arabia 1.68 369.18 0.5% 12,640

Grand Total 1,148.72 54,419.49 2.1% 11,416

U.S.

Middle East & Africa

Rest of EuropeRest of EUR Area

U.K.

Germany

France

Rest of APAC

South Korea

Japan

China

Rest of Americas

CanadaBrazil

U.S.

Middle East & Africa

Rest of Europe

Rest of EUR Area

U.K.

Germany

France

Rest of APAC

South KoreaJapan

China

Rest of AmericasCanadaBrazil

U.S.

Middle East & Africa

Rest of EuropeRest of EUR Area

U.K.

Germany

France

Rest of APAC

South Korea

Japan

ChinaRest of Americas

CanadaBrazil

Global Premium by Product Line

With rates continuing to soften and investment yields depressed, insurers are under intense pressure to find profitable areas to grow. The following pages present a summary of global insurance markets: the size of each market by premium, premium relative to GDP (insurance penetration ratio), loss ratios, and volatility of loss ratios. We have segmented premium into motor, property and liability lines for the top 50 markets.

Global Market Review

Motor: $513B

Property: $388B

Liability: $277B

22

Insurance Risk Study

Global Premium, Loss Ratio and Volatility: Motor

CountryGross Written Premium Average Loss Ratio Graph

Latest ($M) 5 Yr Annual Growth 1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR

Argentina 2,692 16.4% 66% 68% 67% 4% 6%

Australia 7,726 4.3% 98% 98% 92% 9% 9%

Austria 3,923 3.0% 71% 66% 65% 8% 11%

Belgium 4,425 4.3% 91% 79% 76% 10% 12%

Brazil 9,996 19.3% 65% 59% 59% 5% 9%

Canada 13,607 -3.3% 76% 74% 73% 6% 8%

Chile 523 14.0% 69% 66% 66% 4% 6%

China 49,055 49.7% 60% 55% 56% 4% 6%

Colombia 1,177 16.6% 56% 55% 54% 4% 7%

Czech Republic 2,120 9.2% 51% 51% 52% 4% 8%

Denmark 2,363 4.0% 65% 58% 62% 11% 16%

Finland 1,778 6.5% 78% 78% 78% 6% 7%

France 52,439 22.1% 82% 82% 81% 3% 4%

Germany 27,924 0.0% 96% 95% 92% 5% 5%

Hong Kong 342 -1.2% 60% 62% 58% 9% 15%

Hungary 1,045 1.2% 60% 59% 59% 2% 4%

India 2,755 10.2% 76% 70% 76% 17% 19%

Indonesia 1,367 29.1% 49% 50% 47% 5% 10%

Iran 4,957 45.9% 76% 83% 82% 6% 7%

Ireland 4,417 15.2% 87% 74% 69% 15% 18%

Israel 2,075 4.7% 107% 100% 98% 8% 8%

Italy 28,370 1.5% 79% 76% 75% 6% 8%

Japan 83,411 15.2% 69% 67% 66% 2% 3%

Luxembourg 1,012 27.2% 68% 67% 68% 5% 7%

Malaysia 1,490 7.9% 82% 77% 71% 8% 12%

Mexico 3,462 3.0% 73% 72% 72% 3% 4%

Netherlands 12,729 21.4% 72% 67% 68% 5% 7%

New Zealand 753 1.8% 67% 70% 69% 2% 4%

Norway 2,606 5.2% 70% 70% 68% 4% 6%

Poland 3,757 8.0% 78% 69% 67% 4% 6%

Portugal 2,365 -0.4% 71% 68% 69% 3% 4%

Puerto Rico 510 -9.2% 59% 59% 60% 4% 7%

Romania 1,835 29.0% 47% 59% 60% 6% 11%

Russia 2,718 -2.5% 58% 58% 54% 9% 18%

S. Korea 18,976 24.4% 70% 71% 72% 4% 6%

Saudi Arabia 1,356 46.1% 59% 54% 55% 6% 11%

Singapore 731 12.7% 75% 84% 77% 10% 13%

South Africa 4,857 29.1% 69% 70% 69% 4% 5%

Spain 16,205 3.4% 73% 70% 76% 9% 12%

Sweden 2,831 -0.5% 74% 78% 84% 11% 12%

Switzerland 10,283 23.3% 59% 59% 62% 5% 7%

Taiwan 1,521 -1.6% 59% 57% 58% 3% 4%

Thailand 1,908 9.0% 57% 57% 56% 3% 5%

Turkey 3,223 10.8% 76% 73% 70% 7% 11%

U.A.E. 2,381 45.3% 70% 68% 67% 8% 10%

U.K. 47,582 17.0% 81% 79% 79% 5% 6%

U.S. 186,586 -0.4% 63% 62% 60% 5% 8%

Ukraine 758 -20.5% 53% 44% 42% 8% 19%

Venezuela 8,015 64.0% 53% 52% 51% 9% 16%

Grand Total 648,939 22.0% 71% 69% 68% 5% 8%

0% 50% 100%

23

Aon Benfield

Global Premium, Loss Ratio and Volatility: Property

CountryGross Written Premium Average Loss Ratio Graph

Latest ($M) 5 Yr Annual Growth 1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR

Argentina 1,113 11.9% 50% 47% 43% 8% 18%

Australia 6,349 7.9% 75% 75% 66% 13% 20%

Austria 3,213 5.3% 83% 75% 71% 11% 15%

Belgium 3,172 7.6% 67% 57% 54% 7% 13%

Brazil 4,955 19.5% 46% 47% 46% 16% 30%

Canada 10,437 4.5% 66% 63% 62% 8% 13%

Chile 993 11.8% 21% 41% 50% 19% 40%

China 14,703 48.2% 59% 54% 53% 7% 14%

Colombia 855 8.4% 30% 35% 34% 8% 23%

Czech Republic 2,166 28.3% 51% 52% 49% 40% 62%

Denmark 3,338 2.7% 75% 73% 72% 23% 29%

Finland 1,264 5.0% 66% 70% 68% 8% 10%

France 4,724 -25.5% 79% 70% 69% 17% 22%

Germany 23,062 3.0% 70% 73% 70% 8% 11%

Hong Kong 635 1.0% 44% 46% 42% 7% 19%

Hungary 701 5.1% 18% 31% 32% 7% 21%

India 1,103 3.2% 52% 43% 38% 14% 34%

Indonesia 2,595 22.9% 39% 40% 47% 18% 33%

Iran 1,285 35.0% 31% 26% 29% 6% 24%

Ireland 2,958 16.8% 88% 65% 56% 16% 26%

Israel 786 4.1% 62% 62% 65% 14% 22%

Italy 6,984 1.6% 66% 61% 59% 5% 9%

Japan 31,062 18.4% 37% 38% 45% 12% 27%

Luxembourg 1,874 48.4% 74% 76% 61% 16% 27%

Malaysia 1,021 5.9% 34% 23% 20% 16% 64%

Mexico 2,911 7.9% 33% 52% 71% 33% 54%

Netherlands 10,942 26.4% 60% 58% 55% 5% 9%

New Zealand 1,031 2.8% 51% 57% 53% 7% 15%

Norway 3,961 6.2% 45% 41% 38% 7% 17%

Poland 1,251 8.8% 49% 46% 41% 6% 15%

Portugal 1,063 3.0% 49% 48% 43% 8% 17%

Puerto Rico 711 -2.1% 19% 21% 21% 9% 39%

Romania 362 18.7% 14% 20% 19% 4% 22%

Russia 9,420 21.9% 54% 40% 29% 15% 71%

S. Korea 4,453 23.1% 54% 47% 43% 16% 31%

Saudi Arabia 1,305 29.4% 27% 30% 26% 21% 69%

Singapore 567 6.6% 42% 34% 31% 8% 23%

South Africa 4,195 28.8% 59% 56% 56% 7% 12%

Spain 10,037 10.3% 58% 57% 67% 14% 21%

Sweden 3,768 2.3% 52% 53% 57% 5% 8%

Switzerland 7,421 20.6% 48% 52% 54% 5% 9%

Taiwan 977 -4.3% 47% 39% 40% 20% 48%

Thailand 628 5.9% 39% 42% 43% 8% 22%

Turkey 2,196 13.7% 34% 34% 35% 11% 25%

U.A.E. 1,934 41.7% 54% 53% 52% 16% 31%

U.K. 55,442 20.6% 57% 61% 59% 9% 14%

U.S. 160,396 3.9% 57% 56% 62% 13% 20%

Ukraine 907 -21.9% 5% 18% 10% 9% 106%

Venezuela 783 16.0% 19% 23% 22% 57% 114%

Grand Total 418,011 12.3% 56% 56% 58% 8% 13%

0% 50% 100%

24

Insurance Risk Study

Global Premium, Loss Ratio and Volatility: Liability

CountryGross Written Premium Average Loss Ratio Graph

Latest ($M) 5 Yr Annual Growth 1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR

Argentina 1,959 27.7% 62% 60% 61% 3% 5%

Australia 6,476 2.4% 72% 58% 52% 14% 31%

Austria 2,060 10.9% 66% 60% 57% 5% 8%

Belgium 4,111 9.7% 154% 105% 97% 22% 24%

Brazil 1,625 18.1% 33% 35% 38% 9% 23%

Canada 5,360 5.6% 53% 49% 50% 7% 12%

Chile 433 17.3% 77% 66% 57% 15% 26%

China 3,498 13.3% 56% 52% 47% 10% 21%

Colombia 509 19.1% 28% 27% 28% 4% 15%

Czech Republic 2,117 40.6% 36% 38% 43% 6% 13%

Denmark 1,250 7.1% 57% 68% 76% 12% 15%

Finland 1,160 2.9% 80% 82% 85% 8% 9%

France 11,253 5.5% 57% 56% 56% 8% 13%

Germany 16,116 3.8% 74% 69% 68% 6% 9%

Hong Kong 1,001 3.8% 47% 51% 50% 16% 27%

Hungary 187 3.1% 43% 40% 37% 6% 18%

India 1,154 3.2% 34% 33% 35% 10% 25%

Indonesia 515 30.2% 27% 27% 25% 9% 39%

Iran 527 30.6% 54% 51% 55% 11% 23%

Ireland 2,536 14.6% 62% 47% 55% 22% 31%

Israel 418 -5.0% 98% 94% 89% 8% 9%

Italy 8,739 10.6% 75% 73% 74% 6% 8%

Japan 27,029 11.4% 33% 29% 28% 6% 26%

Luxembourg 856 26.2% 96% 68% 54% 25% 50%

Malaysia 324 7.7% 40% 28% 27% 19% 54%

Mexico 1,040 5.6% 36% 30% 30% 8% 22%

Netherlands 7,775 28.6% 55% 53% 56% 5% 8%

New Zealand 245 9.9% 58% 48% 42% 9% 24%

Norway 983 -11.5% 69% 47% 42% 12% 31%

Poland 880 20.3% 30% 26% 27% 5% 18%

Portugal 1,059 -3.1% 77% 73% 69% 15% 25%

Puerto Rico 601 8.2% 43% 41% 43% 5% 10%

Romania 93 5.7% 41% 47% 50% 13% 30%

Russia 1,012 2.7% 28% 24% 21% 10% 80%

S. Korea 41,722 34.7% 80% 80% 81% 27% 28%

Saudi Arabia 283 39.6% 17% 25% 24% 13% 50%

Singapore 510 -0.2% 38% 37% 44% 9% 18%

South Africa 1,435 22.8% 50% 56% 58% 7% 11%

Spain 7,640 5.6% 63% 55% 65% 12% 19%

Sweden 287 7.3% 181% 236% 227% 66% 32%

Switzerland 5,066 22.3% 47% 45% 44% 8% 16%

Taiwan 271 -6.3% 57% 42% 46% 15% 29%

Thailand 264 3.3% 35% 30% 40% 16% 40%

Turkey 187 2.6% 19% 21% 20% 4% 23%

U.A.E. 2,507 46.5% 32% 30% 40% 18% 33%

U.K. 40,830 17.5% 58% 54% 55% 4% 8%

U.S. 120,533 -0.4% 67% 66% 61% 9% 14%

Ukraine 723 12.6% 44% 44% 28% 14% 65%

Venezuela 1,553 57.1% 8% 9% 11% 16% 70%

Grand Total 338,712 7.2% 64% 60% 59% 5% 9%

0% 50% 100%

25

Aon Benfield

Afterword: The Greatest Risk

In last year’s conclusion we highlighted the significance of reserve risk to an insurance company’s balance sheet. In 2010, U.S. industry reserve levels appear strong. Companies continue to release reserves from prior accident years and we expect releases to continue for the next two years or so. Reserve risk is, however, a retrospective manifestation of prospective pricing risk, and pricing risk truly is the greatest risk facing insurance companies globally. Surprisingly, despite its severity, pricing risk is often poorly modeled, inadequately monitored, and insufficiently scrutinized, especially when compared to catastrophe risk. The factors provided in this Study are specifically designed to help quantify pricing risk. Working with Aon Benfield brokers, our Analytics team can help structure risk transfer solutions to manage pricing risk to appropriate levels.

Pricing Risk: The Greatest RiskIn its annual study of insurance company impairments, A.M. Best identifies two symptoms of pricing risk, deficient loss reserves and excessive growth, as the primary cause in 52 percent of the 1,028 U.S. impairments it has tracked since 1969. By comparison, they identify only 7.6 percent of impairments as primarily caused by catastrophe losses. These statistics do not reflect the relative riskiness of pricing vs. catastrophe loss; instead they reflect the quality of models and the risk management practices underlying the two perils.

Catastrophe modeling technology, while not perfect, represents a very successful application of science to the question of risk quantification. It has revolutionized underwriting, pricing and risk management practice. Its general acceptance within the industry has also revolutionized the provision of risk capacity: the flow of capital to bear catastrophe risk, whether through insurance-linked securities or more traditional solutions, is predicated in large degree on the agreed and objective view of risk provided by scientifically-based catastrophe models. The lack of analogous models and agreement for non-catastrophe property and casualty lines has many important ramifications.

Risk Management, Regulatory and Rating Agency Treatment of Pricing Risk There is no more risky unit of premium than an under-priced unit. Paradoxically, the less volatile a line of business, the more true this statement becomes — to the extent that for many predictable lines of business pricing risk is the number one risk component.

Internal, regulatory, and rating agency risk and capital models often equate risk with tail risk and attempt to model risk from a loss-only perspective. For catastrophe perils this paradigm has been very successful. Outside property catastrophe lines the approach is far less successful. Frequency and severity models of loss incorporating pricing risk, while common for internal models, rarely underlie regulatory or rating agency models, which tend to use simple factor-based approaches, with default factors by line and geography applied to premiums.

An obvious problem with a factor-based approach to underwriting risk is that it does not use an objective measure of exposure: an inadequate (lower) premium generates a smaller risk charge in a model which simply applies a factor to premium! One important pricing adequacy statistic tracked in this Study is the ratio of net written premium to gross domestic product for the U.S., both in nominal dollars. A time series for the ratio back to 1970 is shown in the next chart. It clearly shows three market cycles in the last forty years: with turns after 1974, 1984 and 2001. It also shows that, relative to GDP as an exposure measure, premium levels today are at historically low levels, being below 3.0 percent for the first time since 1970. In 2010, we reach a tipping point: will the industry repeat the excesses of the 1998-2000 soft market, or will it stabilize risk pricing, ushering in a period of barely adequate returns? Hoping for a severe catastrophe loss to solve the industry’s over-capacity is not a strategy.

26

Insurance Risk Study

Two expected outcomes of the financial crisis were a heightened respect for risk and a drive towards objective and consistent risk measures. Aon Benfield anticipated a move by regulators to supplement the trend towards internal capital models with stronger, exposure factor-based capital requirements. But in Europe, Solvency II has continued almost unchanged since the crisis, with no apparent pause caused by the failure of the ”certify-yourself” risk management processes used in banking. We have seen heightened respect for risk, but it has been driven from within companies rather than being imposed externally.

Pricing Uncertainty and Reinsurance Reinsurance is not a solution to inadequate pricing. It can, however, be used to provide an effective hedge against pricing uncertainty. New products and emerging geographies generate important opportunities for needed revenue and income growth, but both also generate heightened levels of pricing uncertainty. Reinsurance can be used to effectively

manage this pricing uncertainty and to facilitate underlying growth. Reinsurance provides objective, third-party validation of underlying rate levels and policy forms — backed up by an effective financial guarantee. It allows a more aggressive approach to growth and expansion, enabling the innovation and experimentation companies need to prosper in today’s hyper-competitive global market.

Recently, Aon Benfield has been working with clients globally to understand what primary policy coverage enhancements and product differentiation could be used to stop, and potentially reverse, the erosion of primary rate levels. Many of these enhancements can be safely and effectively reinsured by an eager reinsurance market suffering the same over-capitalization and top line erosion as primary companies. We believe we are unique in advocating growth through reinsurance-backed innovation, and recommend that you contact your local Aon Benfield broker to learn more about the exciting prospects of this strategy.

Industry NWP % of GDP

1970 1975 1980 1985 1990 1995 2000 2005 20102.5%

3.0%

3.5%

4.0%

4.5%

3.0% 3.0% 2.9%3.0%

27

Aon Benfield

Sources: A.M. Best, ANIA (Italy), Association of Vietnam Insurers, Axco Insurance Information Services, BaFin (Germany), Banco Central del Uruguay, Bank Negara Malaysia , Bloomberg, Bureau of Economic Analysis (U.S.), Bureau of Labor Statistics (U.S.), CADOAR (Dominican Republic), Cámara de Aseguradores de Venezuela, Comisión Nacional de Bancos y Seguros de Honduras, Comisión Nacional de Seguros y Fianzas (Mexico), Danish FSA, Dirección General de Seguros (Spain), DNB (Denmark), E&Y Annual Statements (Israel), Finma (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 (Australia), Korea Financial Supervisory Service, Monetary Authority of Singapore, MSA Research Inc. (Canada), Quest Data Report (South Africa), Romanian Insurance Association, SNL (U.S.), “The Statistics of Japanese Non-Life Insurance Business” (ed. Insurance Research Institute), Superintendencia de Banca y Seguros (Peru), Superintendencia de Bancos y Otras Instituciones Financieras de Nicaragua, Superintendencia de Bancos y Seguros (Ecuador), Superintendencia de Pensiones de El Salvador, Superintendencia de Pensiones, Valores y Seguros (Bolivia), Superintendencia de Seguros de la Nación (Argentina), Superintendencia de Seguros Privados (Brazil), Superintendencia de Seguros y Reaseguros de Panama, Superintendencia de Valores y Seguros de Chile, Superintendencia Financiera de Colombia, Taiwan Insurance Institution, Turkish Insurance and Reinsurance Companies Association, Yahoo! Finance, Yearbooks of China’s Insurance, and annual financial statements.

Stephen MildenhallChief Executive Officer, Aon Benfield Analytics+1 312 381 5880stephen.mildenhall@aonbenfield.com

Parr SchoolmanRisk & Capital Strategy+1 312 381 5330parr.schoolman@aonbenfield.com

Michael McClaneReMetrica, U.S.+1 215 751 1596michael.mcclane@aonbenfield.com

John MooreHead of Analytics, International+ 44 (0)20 7522 3973john.moore@aonbenfield.com

Paul MaitlandReMetrica, International +44 (0)20 7522 3932paul.maitland@aonbenfield.com

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.

For more information on the Insurance Risk Study, ReMetrica®, S2MetricaSM Solvency II Model or our analytic capabilities, please contact your local Aon Benfield broker or:

Americas

Brian Alvers+1 312 381 5355brian.alvers@aonbenfield.com

EMEA & U.K.

Marc Beckers+44 (0)20 7086 0394marc.beckers@aonbenfield.com

Paul Kaye+44 (0)20 7522 3810paul.kaye@aonbenfield.com

Asia Pacific

Will Gardner+61 2 9650 0390will.gardner@aonbenfield.com

David Maneval+61 2 9650 0395david.maneval@aonbenfield.com

George Attard+65 6239 8739george.attard@aonbenfield.com

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