Insurance Risk Study - Aon

Insurance Risk Study 

Fifth Edition 2010 

reDEFINING 

Capital | Access | Advocacy | Innovation

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 

About the Study 

Asset Portfolio Risk 

Portfolio Risk 

16 | Modeling Dependence 

17 | Size and Correlation 

18 | Macroeconomic Correlation 

19 | Managing Inflation Risk 

21 | Global Market Review 

25 | Afterword: The Greatest Risk 

Rating 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. 

Portfolio Risk 

Insurance Portfolio Risk 

Systemic 

Market Risk Systemic 

Insurance 

Risk 

Number of Stocks 

Insurance Risk 

Volume 

Naïve Model

Foreword 

Since 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 

3


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 

Coefficient of Variation of Gross Loss Ratio by Country 

Japan 

Turkey 

Taiwan 

South Korea 

Israel 

Australia 

Austria 

Czech Republic 

Switzerland 

Germany 

Mexico 

France 

Argentina 

Spain 

Italy 

Bolivia 

U.K. 

China 

Netherlands 

Chile 

India 

Brazil 

Uruguay 

Malaysia 

Colombia 

Poland 

U.S. 

Peru 

Vietnam 

Canada 

Venezuela 

El Salvador 

Honduras 

Ecuador 

Romania 

Denmark 

South Africa 

Slovakia 

Dominican Republic 

Singapore 

Indonesia 

Panama 

Hong Kong 

Greece 

Nicaragua 

4 

Motor Property 

Hungary 4% 

8% 

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% 

Israel 

South Africa 

Australia 

Italy 

Switzerland 

Germany 

Austria 

Spain 

Panama 

U.K. 

Denmark 

Chile 

Canada 

China 

Malaysia 

Japan 

India 

Turkey 

France 

Venezuela 

Uruguay 

El Salvador 

Vietnam 

Bolivia 

Hungary 

South Korea 

Poland 

Netherlands 

Slovakia 

U.S. 

Ecuador 

Dominican Republic 

Argentina 

Brazil 

Romania 

Colombia 

Honduras 

Indonesia 

Nicaragua 

Hong Kong 

Singapore 

Greece 

Peru 

Mexico 

64% Taiwan 

Americas Asia Pacific Europe, Middle East & Africa 

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. 

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%

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

Americas 

Motor 

Motor - 

Personal 

Motor - 

Commercial 

Property 

Property - 

Personal 

Property - 

Commercial 

General 

Liability 

Accident 

& Health 

Marine, 

Aviation 

& Transit 

Workers 

Compensation 

Credit 

Aon Benfield 

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. 

Fidelity 

& Surety 

5


Evaluating Solvency II Factors 

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 Study 

There are four key differences between the Solvency II 

factors and those in the Insurance Risk Study. 

Key Differences 

6 

Solvency II Insurance Risk Study 

Standard deviations 

of gross loss ratios 

Based on loss ratios at 

end of first year 

Coefficients of variation 

of gross loss ratios 

Based on ultimate loss ratios 

Excludes catastrophe risk Includes catastrophe risk 

Average-sized company, 

parameter and process risk 

Large company, 

parameter risk only 

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 

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 

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 averagesized 

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 Correlation Coefficients 

Not surprisingly, correlation will be an important 

determinant of capital requirements. 

Solvency II Correlation Coefficients 

Motor - 

TPL 

Motor - 

Other 

Marine, 

Aviation 

& Transit 

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 

Fire 

General 

Liability 

Motor — TPL 50% 50% 25% 50% 25% 

Motor — Other 50% 25% 25% 25% 25% 


& Transit 

Credit 

50% 25% 25% 25% 25% 

Fire 25% 25% 25% 25% 25% 

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

Credit 25% 25% 25% 25% 50% 

Motor 

Marine, 

Aviation 

& Transit 

Property 

General 

Liability 

Motor 20% 7% 6% 26% 


& Transit 

20% 22% 10% 45% 

Property 7% 22% 0% 31% 

General Liability 6% 10% 0% -3% 

Credit 26% 45% 31% -3% 

Credit 


S2Metrica: ReMetrica Modeling 

for Proposed Solvency II 

Developing 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. 

7


U.S. Risk Parameters 

The 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. 

Coefficient of Variation of Gross Loss Ratio | 1992-2009 

Products Liability – Claims-Made 

8 

Private Passenger Auto 14% 

Auto Physical Damage 

Commercial Auto 

Workers Compensation 

Warranty 

Medical PL – Occurrence 

Commercial Multi Peril 

Other Liability – Occurrence 

Special Liability 

Medical PL – Claims-Made 

Other Liability – Claims-Made 

Products Liability – Occurrence 

Homeowners 

Other 

Reinsurance – Liability 

International 

Fidelity & Surety 

Reinsurance – Property 

Reinsurance – Financial 

Special Property 

Financial Guaranty 

All Risk No Underwriting Cycle Risk 

17% 

24% 

28% 

31% 

32% 

34% 

37% 

39% 

40% 

43% 

47% 

51% 

52% 

67% 

68% 

70% 

85% 

91% 

102% 

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 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. 

104% 

163% 

13% 

15% 

17% 

19% 

31% 

27% 

25% 

32% 

29% 

28% 

29% 

32% 

43% 

45% 

49% 

Impact of the Pricing Cycle 

Line 

54% 

54% 

54% 

59% 

47% 

62% 

106% 

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%

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 

U.S. Reserve Volatility by Line 

Line 

Reserve to 

Premium Ratio 

% Reserves 

Over 10 Yrs Old 

Ultimate 

Reserve CV 

One Year 

Reserve CV 

% CV 

Emerging in 

One Year 


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 

2007 2008 2009 Average 

Remaining 

Redundancy 

Years at 

Run Rate 

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 Leverage 

Insurers 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. 

One Year 

Combined 

Ratio Impact 

Homeowners 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% 

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. 

9


Best of Times, Worst of Times 

In 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. 

Mean Year-Over-Year Change in Loss Ratio 

Biggest Increase (Deteriorating Results) 

10 

9% 14% 18% 23% 29% 36% 38% 39% 42% 44% 45% 48% 51% 56% 

Auto Phys. 

Damage 

Private Auto 

Commercial 

Auto 

Workers Comp 

Medical PL - 

CM 

Commercial 

Multi Peril 

Medical PL - 

Occ 

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

Biggest Decrease (Improving Results) 

Other Liability - 

Occ 

Fidelity 

& Surety 

-41% -32% 

Other Liability - 

CM 

-72% 

Reinsurance - 

Liability 

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. 

Products Liability - 

Occ 

Special Liability 

-64% -67% -67% 

Products Liability - 

CM 

-93% 

66% 67% 73% 78% 

Reinsurance - 

Financial 

-123% 

Homeowners 

-79% 

Special Property 

-262% 

International 

-81% 

125% 

Reinsurance - 

Property 

-185% 

170% 499% 

Financial 

Guaranty 

-87% 

Other 

-41%

Year-Over-Year Change in Gross Loss Ratio 

Commercial Auto 

Probability Density 

Biggest Increase 

Biggest Decrease 

0.4 

0.3 

0.2 

0.1 

-150% -100% -50% 

0 

0% 

Other Liability - Occurrence 


0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

-150% -100% -50% 0% 



0 

0.3 

0.2 

0. 0.1 

-150% -100% -50% 0% 

0 

50% 100% 150% 

50% 100% 150% 

50% 100% 150% 

Frequency by Accident Year 

0.3 

0.2 

0.1 

0.0 

0.0 

0.1 

0.2 

0.3 

0.3 

0.2 

0.1 

0.0 

0.0 

0.1 

0.2 

0.3 

0.3 

0.2 

0.1 

0.0 

0.0 

0.1 

0.2 

0.3 

1993 

1993 

1993 

1994 

1994 

1994 

1995 

1995 

1996 

1996 

1997 

1997 

1998 

1998 

1999 


Mean 18.3% 

Median 15.1% 

90th %ile 32.5% 

1999 

2000 



Mean 39.5% 

Median 24.8% 

90th %ile 78.4% 

2000 


1995 

1996 

1997 

1998 

1999 


Mean 23.1% 

Median 20.1% 

90th %ile 38.9% 

2000 

2001 

2001 

2001 

2002 

2002 

2002 

2003 

2003 

2003 

2004 

2004 

2004 

2005 

2005 

2005 

2006 

2006 

2006 


2007 

2007 

2007 

2008 

2008 

2008 

2009 


Mean -25.1% 

Median -20.1% 

90th %ile -45.2% 

2009 


Mean -40.8% 

Median -27.4% 

90th %ile -71.5% 

2009 


Mean -25.0% 

Median -20.5% 

90th %ile -46.0% 

11


Correlation and the Pricing Cycle 

Correlation of Underwriting Results 

Correlation 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 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 Australia, China, Germany, Japan, 

the U.K., and the U.S. 

Australia 

China 

12 

Accident 

& Health 

General 

Liability 

Agriculture 

Marine, 

Aviation 

& Transit 

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% 

Credit 

Engineering 

General 

Liability 

Marine, 

Aviation 

& Transit 

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% 

Motor 

Property 

Motor 

Workers 

Comp 

Property 

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.

Germany 

Japan 

U.K. 

U.S. 

Accident 

& Health 

Commercial 

Auto 

Accident 

& Health 

Accident 

& Health 

Commercial 

Multi Peril 

Assistance 

Commercial 

Lines Liability 

General 

Liability 

Homeowners 

Medical 

Malpractice 

CM 

Other Liability 

CM 

Other Liability 

Occ 

Personal Auto 

Liability 


Products 

Liability 

Occ 

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% 

Commercial 

Motor 

Marine, 

Aviation 

& Transit 

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% 

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% 

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% 

Commercial 

Property 

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

Financial Loss 

Household 

& Domestic 

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

Credit 

General 

Liability 

Legal 

Protection 

Motor 

Marine, 

Aviation 

& Transit 

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% 

Property 

Motor 

Property 

Workers 

Comp 

Private Motor 

Workers 

Comp 

13


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 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. 

Americas - Motor 

Europe - Motor 

14 

Argentina 

Asia Pacific - Motor 

Australia 

Austria 

Brazil 

China 

Belgium 

Canada 

Hong Kong 

France 

Chile 

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% 

India 

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% 

Germany 

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% 

Colombia 

Japan 

Italy 

Mexico 

Malaysia 

Netherlands 

Peru 

Russia 

Norway 

Puerto Rico 

Singapore 

Spain 

United States 

South Korea 

Switzerland 

Venezuela 

Taiwan 

United 

Kingdom

Americas - Liability 

Europe - Liability 

Argentina 

Asia Pacific - Liability 

Austria 

Australia 

Brazil 

Belgium 

China 

Canada 

Denmark 

Hong Kong 

France 

Germany 

Italy 

Norway 

Spain 


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% 

Japan 

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% 

Chile 

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% 

Colombia 

Malaysia 

Mexico 

Russia 

Peru 

Singapore 

Puerto Rico 

United States 

South Korea 

Switzerland 

Venezuela 

Taiwan 

United 

Kingdom 

15


Modeling Dependence 

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. 

Daily Stock Returns of Two Financial 

Stocks Through the Crisis 

16 

3.0 

2.0 

1.0 

-1.0 

-2.0 

-3.0 

Normal Transformed Data 

0 

-3.0 -2.0 -1.0 0 1.0 2.0 3.0 

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. 

Products Liability Occurrence vs. 


3.0 

2.0 

1.0 

-1.0 

-2.0 

-3.0 

Normal Transformed Data 

0 

-3.0 -2.0 -1.0 0 1.0 2.0 3.0

Size and Correlation 

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 

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

Correlation Above Threshold 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

10 100 1,000 10,000 

Size Threshold, $M 

Correlation 

Risk Study Coefficient 

Correlation Above Threshold 

1.0 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 


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. 

0 

10 100 1,000 10,000 

Size Threshold, $M 

Correlation 

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. 

Risk Study Coefficient 

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. 

17


Macroeconomic Correlation 

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. 

Macroeconomic Correlations 

18 

Inflation 

(CPI-U) 

Inflation (PPI) 

GDP Growth 

Unemployment 

Change 

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. 

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% 

3-Month 

T-Bill Rate 

1-3 Year 

T-Bill 

AAA-AA 3-5 

Year Spread 

BBB 3-5 

Year Spread 

S&P 500 

Returns 

VIX 

Property 

Returns

Managing Inflation Risk 

Risk 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 

20% 

15% 

10% 

5% 

0% 

-5% 

-10% 

-15% 

1910 

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 

14% 

12% 

10% 

8% 

6% 

4% 

2% 

0% 

1970 

1975 

1930 

1980 

1985 

1950 

1990 

1970 

1995 

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 

1990 

Combined Ratio 

Inflation 

2000 

2005 

2010 

125% 

120% 

115% 

110% 

105% 

100% 

95% 

90% 

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 

Assets 

Balance 

($B) 

Pre-tax 

Sensitivity 

% 

After-Tax 

Sensitivity 

% 

After- 

Tax Impact 

($B) 

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 

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. 

19


Managing Inflation Risk 

Insurers 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. 

20 

Inflation CPI-U, 1970-2009 

15% 

10% 

5% 

0% 

1970 

1975 

1980 

1985 

S&P 500 Year 1-Year Change 

40% 

20% 

0% 

-20% 

-40% 

1970 

1975 

1980 

1985 

1990 

1990 

1995 

1995 

2000 

2000 

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

4% 

3% 

2% 

1% 

0% 

1970 

Base Year = 1970 

1975 

1980 

1985 

1990 

1995 

2000 

2005 

2005 

2005 

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.

Global Market Review 

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 Premium by Product Line 

Motor: $513B 

U.S. 

Middle East & Africa 

Rest of Europe 

Property: $388B 

U.S. 


Liability: $277B 

U.S. 



Brazil 

Brazil 


Brazil 

Canada 

Rest of Americas 

China 

U.K. 

Japan 

Germany 

Rest of EUR Area 

U.K. 

South Korea 

Rest of APAC 

France 

Canada 


China 

Japan 

South Korea 

Rest of APAC 

France 

Germany 


Canada 


China 

Japan 

South Korea 

U.K. 


Rest of APAC 

France 

Germany 

Top 50 Markets by Gross Written Premium 

Country 

P&C GWP 

($B) 

GDP ($B) 

Premium/ 

GDP Ratio 


GDP 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 

21


Global Premium, Loss Ratio and Volatility: Motor 

22 

Country 

Gross 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%

Global Premium, Loss Ratio and Volatility: Property 

Country 



Latest ($M) 

5 Yr Annual 

Growth 


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% 


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% 

23


Global Premium, Loss Ratio and Volatility: Liability 

24 

Country 


Latest ($M) 

5 Yr Annual 

Growth 


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% 


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%

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 Risk 

In 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. 

25


Industry NWP % of GDP 

4.5% 

4.0% 

3.5% 

3.0% 

2.5% 

1970 1975 1980 1985 1990 1995 2000 2005 2010 

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 

26 

3.0% 3.0% 

3.0% 2.9% 

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 overcapitalization 

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.

For more information on the Insurance Risk Study, ReMetrica ® , S2Metrica SM Solvency II 

Model or our analytic capabilities, please contact your local Aon Benfield broker or: 

Stephen Mildenhall 

Chief Executive Officer, Aon Benfield Analytics 

+1 312 381 5880 

stephen.mildenhall@aonbenfield.com 

Parr Schoolman 

Risk & Capital Strategy 

+1 312 381 5330 

parr.schoolman@aonbenfield.com 

Michael McClane 

ReMetrica, U.S. 

+1 215 751 1596 

michael.mcclane@aonbenfield.com 

Americas 

Brian Alvers 

+1 312 381 5355 

brian.alvers@aonbenfield.com 

EMEA & U.K. 

Marc Beckers 

+44 (0)20 7086 0394 

marc.beckers@aonbenfield.com 

Paul Kaye 

+44 (0)20 7522 3810 

paul.kaye@aonbenfield.com 

About Aon Benfield 

John Moore 

Head of Analytics, International 

+ 44 (0)20 7522 3973 

john.moore@aonbenfield.com 

Paul Maitland 

ReMetrica, International 

+44 (0)20 7522 3932 

paul.maitland@aonbenfield.com 

Asia Pacific 

Will Gardner 

+61 2 9650 0390 

will.gardner@aonbenfield.com 

David Maneval 

+61 2 9650 0395 

david.maneval@aonbenfield.com 

George Attard 

+65 6239 8739 

george.attard@aonbenfield.com 


As 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. 

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. 

27

200 E. Randolph Street, Chicago, Illinois 60601 

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Copyright Aon Benfield Inc. 2010 | #4561 - 08/2010

Insurance Risk Study - Aon

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