Insurance Risk Study - Aon
Insurance Risk Study - Aon
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<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Fifth Edition 2010<br />
reDEFINING<br />
Capital | Access | Advocacy | Innovation
Contents<br />
3 | Foreword<br />
4 | Global <strong>Risk</strong> Parameters<br />
6 | Evaluating Solvency II Factors<br />
8 | U.S. <strong>Risk</strong> Parameters<br />
10 | Best of Times, Worst of Times<br />
12 | Correlation and the Pricing Cycle<br />
About the <strong>Study</strong><br />
Asset Portfolio <strong>Risk</strong><br />
Portfolio <strong>Risk</strong><br />
16 | Modeling Dependence<br />
17 | Size and Correlation<br />
18 | Macroeconomic Correlation<br />
19 | Managing Inflation <strong>Risk</strong><br />
21 | Global Market Review<br />
25 | Afterword: The Greatest <strong>Risk</strong><br />
Rating agencies, regulators and investors today are demanding that insurers provide detailed assessments of their risk tolerance<br />
and quantify the adequacy of their economic capital. To complete such assessments requires a credible baseline for underwriting<br />
volatility. The <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> provides our clients with an objective and data-driven set of underwriting volatility benchmarks<br />
by line of business and country as well as correlations by line and country. These benchmarks are a valuable resource to CROs,<br />
actuaries and other economic capital modeling professionals who seek reliable parameters for their models.<br />
Modern portfolio theory for assets teaches that increasing the number of stocks in a portfolio will diversify and reduce the<br />
portfolio’s risk, but will not eliminate risk completely; the systemic market risk remains. This is illustrated in the left chart below.<br />
In the same way, insurers can reduce underwriting volatility by increasing portfolio volume, but they cannot reduce their volatility<br />
to zero. A certain level of systemic insurance risk will always remain, due to factors such as the underwriting cycle, macroeconomic<br />
factors, legal changes and weather (right chart below). The <strong>Study</strong> calculates this systemic risk by line of business and country. The<br />
Naïve Model on the right chart shows the relationship between risk and volume using a Poisson assumption for claim count — a<br />
textbook actuarial approach. The <strong>Study</strong> clearly shows that this assumption does not fit with empirical data for any line of business<br />
in any country. It will underestimate underwriting risk if used in an ERM model.<br />
Portfolio <strong>Risk</strong><br />
<strong>Insurance</strong> Portfolio <strong>Risk</strong><br />
Systemic<br />
Market <strong>Risk</strong> Systemic<br />
<strong>Insurance</strong><br />
<strong>Risk</strong><br />
Number of Stocks<br />
<strong>Insurance</strong> <strong>Risk</strong><br />
Volume<br />
Naïve Model
Foreword<br />
Since the first internal <strong>Aon</strong> Benfield <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> in 2003, the insurance world has been shaken<br />
by mega-catastrophes and threatened by financial market turmoil. Industry best practice in enterprise<br />
risk management has evolved almost beyond recognition, and techniques for risk quantification and<br />
capital modeling have advanced from nascent specialties into mainstream core competencies.<br />
Yet despite change and progress, much remains constant. <strong>Risk</strong> quantification still relies fundamentally on accurate<br />
parameterization and realistic stochastic models. An incorrectly specified model is often worse than no model at all.<br />
Bad models can lead the user astray, as was shown by numerous examples during the financial crisis. <strong>Aon</strong> Benfield<br />
has consistently focused on the need to provide our clients the robust data and fact-based parameters published in<br />
this <strong>Study</strong> to complement state-of-the-art financial modeling tools such as our ReMetrica ® software.<br />
The <strong>Study</strong> is a cornerstone of <strong>Aon</strong> Benfield Analytics’ integrated and comprehensive risk modeling and risk<br />
assessment capabilities.<br />
> Our reinsurance optimization framework, linking reinsurance to capital, relies on the <strong>Study</strong> for a credible<br />
assessment of baseline frequency and severity volatility<br />
> Our global risk and capital strategy practice, providing ERM and economic capital services, uses the <strong>Study</strong> to<br />
benchmark risk, quantify capital adequacy and allocate capital to risk drivers<br />
> Our ReMetrica risk evaluation and capital modeling software provides easy access to the <strong>Study</strong> parameters and<br />
risk insights<br />
2010’s Fifth Edition has again expanded in scope and coverage from previous editions. It includes:<br />
> Results from 46 countries, comprising more than 90 percent of global premium<br />
> A global market review showing premium, historical loss ratio and volatility parameters for the top 50 countries<br />
> A new approach to loss ratio volatility that measures year-over-year changes illustrating the magnitude of<br />
historical planning misses<br />
> A focus on the potential impact of inflation on P&C companies<br />
The massive database underlying the <strong>Study</strong> is supported by more than 450 professionals within the global Analytics<br />
team who are available to work with you to customize the basic parameters reported here to answer your specific,<br />
pressing business questions.<br />
<strong>Aon</strong> Benfield’s <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong>, now in its fifth edition, continues to be the industry’s leading<br />
publicly available set of risk parameters for modeling and benchmarking underwriting risk. We are<br />
pleased to offer the <strong>Study</strong> for the advancement of risk management within our industry. For convenient<br />
reference, you can find earlier editions of the <strong>Study</strong> at aonbenfield.com. I welcome your thoughts and<br />
suggestions, which you can share with an e-mail to stephen.mildenhall@aonbenfield.com.<br />
Stephen Mildenhall<br />
CEO, <strong>Aon</strong> Benfield Analytics<br />
3
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Global <strong>Risk</strong> Parameters<br />
The 2010 <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> quantifies the systemic<br />
risk by line for 46 countries worldwide, up from 26 last<br />
year. Systemic risk in the <strong>Study</strong> is the coefficient of<br />
variation of loss ratio for a large book of business.<br />
Coefficient of variation (CV) is a commonly used<br />
normalized measure of risk defined as the standard<br />
deviation divided by the mean. Systemic risk typically<br />
comes from non-diversifiable risk sources such as<br />
changing market rate adequacy, unknown prospective<br />
frequency and severity trend, weather-related losses,<br />
legal reforms and court decisions, the level of economic<br />
Coefficient of Variation of Gross Loss Ratio by Country<br />
Japan<br />
Turkey<br />
Taiwan<br />
South Korea<br />
Israel<br />
Australia<br />
Austria<br />
Czech Republic<br />
Switzerland<br />
Germany<br />
Mexico<br />
France<br />
Argentina<br />
Spain<br />
Italy<br />
Bolivia<br />
U.K.<br />
China<br />
Netherlands<br />
Chile<br />
India<br />
Brazil<br />
Uruguay<br />
Malaysia<br />
Colombia<br />
Poland<br />
U.S.<br />
Peru<br />
Vietnam<br />
Canada<br />
Venezuela<br />
El Salvador<br />
Honduras<br />
Ecuador<br />
Romania<br />
Denmark<br />
South Africa<br />
Slovakia<br />
Dominican Republic<br />
Singapore<br />
Indonesia<br />
Panama<br />
Hong Kong<br />
Greece<br />
Nicaragua<br />
4<br />
Motor Property<br />
Hungary 4%<br />
8%<br />
5%<br />
6%<br />
7%<br />
7%<br />
7%<br />
8%<br />
8%<br />
8%<br />
9%<br />
9%<br />
9%<br />
9%<br />
9%<br />
9%<br />
10%<br />
10%<br />
11%<br />
11%<br />
11%<br />
12%<br />
12%<br />
12%<br />
13%<br />
15%<br />
15%<br />
15%<br />
16%<br />
16%<br />
18%<br />
18%<br />
18%<br />
18%<br />
18%<br />
21%<br />
23%<br />
24%<br />
25%<br />
25%<br />
25%<br />
27%<br />
29%<br />
35%<br />
43%<br />
46%<br />
Israel<br />
South Africa<br />
Australia<br />
Italy<br />
Switzerland<br />
Germany<br />
Austria<br />
Spain<br />
Panama<br />
U.K.<br />
Denmark<br />
Chile<br />
Canada<br />
China<br />
Malaysia<br />
Japan<br />
India<br />
Turkey<br />
France<br />
Venezuela<br />
Uruguay<br />
El Salvador<br />
Vietnam<br />
Bolivia<br />
Hungary<br />
South Korea<br />
Poland<br />
Netherlands<br />
Slovakia<br />
U.S.<br />
Ecuador<br />
Dominican Republic<br />
Argentina<br />
Brazil<br />
Romania<br />
Colombia<br />
Honduras<br />
Indonesia<br />
Nicaragua<br />
Hong Kong<br />
Singapore<br />
Greece<br />
Peru<br />
Mexico<br />
64% Taiwan<br />
Americas Asia Pacific Europe, Middle East & Africa<br />
activity, and other macroeconomic factors. It also<br />
includes the risk to smaller and specialty lines of<br />
business caused by a lack of credible data. For many<br />
lines of business systemic risk is the major component of<br />
underwriting volatility.<br />
The systemic risk factors for major lines by region<br />
appear on the next page. Detailed charts comparing<br />
motor and property risk by country appear below. The<br />
factors measure the volatility of gross loss ratios. If gross<br />
loss ratios are not available the net loss ratio is used.<br />
13%<br />
16%<br />
17%<br />
18%<br />
18%<br />
18%<br />
19%<br />
21%<br />
22%<br />
22%<br />
25%<br />
26%<br />
27%<br />
28%<br />
28%<br />
31%<br />
31%<br />
32%<br />
36%<br />
37%<br />
38%<br />
38%<br />
38%<br />
40%<br />
42%<br />
42%<br />
43%<br />
44%<br />
45%<br />
46%<br />
51%<br />
51%<br />
53%<br />
54%<br />
54%<br />
58%<br />
58%<br />
62%<br />
67%<br />
71%<br />
73%<br />
91%<br />
92%<br />
98%
Underwriting Volatility for Major Lines by Country, Coefficient of Variation of Loss Ratio for Each Line<br />
Americas<br />
Motor<br />
Motor -<br />
Personal<br />
Motor -<br />
Commercial<br />
Property<br />
Property -<br />
Personal<br />
Property -<br />
Commercial<br />
General<br />
Liability<br />
Accident<br />
& Health<br />
Marine,<br />
Aviation<br />
& Transit<br />
Workers<br />
Compensation<br />
Credit<br />
<strong>Aon</strong> Benfield<br />
Argentina 9% 51% 61% 116% 164%<br />
Bolivia 10% 38% 18%<br />
Brazil 12% 53% 48% 60% 57% 45% 43% 58%<br />
Canada 18% 26% 18% 41% 37% 43% 72% 110% 116%<br />
Chile 12% 25% 51% 65%<br />
Colombia 15% 54% 55% 57% 71%<br />
Dominican Republic 25% 51% 120% 64%<br />
Ecuador 21% 46% 49% 178%<br />
El Salvador 18% 38% 21% 100%<br />
Honduras 18% 58% 5% 193%<br />
Mexico 9% 92% 65% 43%<br />
Nicaragua 64% 62% 91% 107%<br />
Panama 35% 21% 24% 103%<br />
Peru 16% 91% 60% 7% 21% 68% 77%<br />
Uruguay 13% 37% 124%<br />
U.S. 16% 14% 24% 45% 51% 34% 37% 52% 39% 28% 70%<br />
Venezuela 18% 36% 23% 160%<br />
Asia Pacific<br />
Australia 8% 16% 23% 32% 54% 10% 30%<br />
China 11% 11% 27% 31% 19% 16% 113%<br />
Hong Kong 43% 44% 67% 82% 24% 60% 81%<br />
India 12% 12% 31% 14% 31%<br />
Indonesia 29% 29% 58% 124% 56% 72% 55% 92%<br />
Japan 5% 28% 10% 8% 17% 7%<br />
Malaysia 15% 28% 126% 30% 40% 88%<br />
Singapore 27% 71% 52% 57% 46%<br />
South Korea 7% 7% 42% 32% 55%<br />
Taiwan 7% 7% 98% 53% 33% 71% 44%<br />
Vietnam 18% 38% 41% 11% 38%<br />
Europe, Middle East & Africa<br />
Austria 8% 18% 12% 52% 21% 13% 21% 51%<br />
Czech Republic 8%<br />
Denmark 24% 22% 18% 33% 18% 16% 39% 23%<br />
France 9% 32% 35% 26% 30% 25% 57%<br />
Germany 9% 18% 20% 31% 29% 14% 22% 43%<br />
Greece 46% 73% 81% 81%<br />
Hungary 4% 40%<br />
Israel 7% 8% 53%<br />
Italy 10% 17% 25% 12% 46% 40% 72%<br />
Netherlands 11% 43% 26% 49% 54% 41%<br />
Poland 15% 42%<br />
Romania 23% 54%<br />
Slovakia 25% 44%<br />
South Africa 25% 13% 61% 33% 46%<br />
Spain 9% 19% 10% 23% 30% 13% 34% 48% 97%<br />
Switzerland 9% 18% 21% 7% 50% 73%<br />
Turkey 6% 10% 31% 44% 36% 15% 93% 52%<br />
U.K. 11% 10% 18% 22% 21% 26% 30% 8% 67%<br />
Reported CVs are of gross loss ratios, except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Uruguay, and Venezuela, which are of<br />
net loss ratios.<br />
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.,<br />
A&H comprises about 80 percent of the ”Other” line of business with the balance of the line being primarily credit insurance.<br />
Fidelity<br />
& Surety<br />
5
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Evaluating Solvency II Factors<br />
Solvency II is scheduled to take effect no later than<br />
January 1, 2013. The fifth quantitative impact study<br />
(QIS 5) is in progress with a deadline of October<br />
2010 for individual insurers and mid-November 2010<br />
for groups.<br />
QIS 5 is likely the key test for most insurers across<br />
Europe. The Standard Formula factors were designed to<br />
be appropriate for the entire market — meaning a<br />
typical company of average size — so larger insurers<br />
with greater diversification will find the formula<br />
generates conservative capital requirements. Moreover,<br />
in QIS 5, the formula reflects added concerns that<br />
emerged from the 2008 financial crisis. Insurers now<br />
have heightened incentives to develop full or partial<br />
internal models as an alternative to the Solvency II<br />
Standard Formula.<br />
At this stage, the most important aspect of preparing<br />
for Solvency II is correct parameterization, driven by<br />
access to data. Insurers may face serious challenges to<br />
their IT systems. They will need some reference point as<br />
they undertake the various Solvency II tests: evaluating<br />
the statistical quality of the data, calibrating and<br />
validating the models they are using.<br />
The non-life Solvency Capital Requirement (SCR) is<br />
predominantly driven by premium risk, reserve risk and<br />
catastrophe risk. Since many companies with<br />
catastrophe exposure purchase excess of loss<br />
reinsurance, premium and reserve risk will be the key<br />
drivers of capital.<br />
Solvency II vs. <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
There are four key differences between the Solvency II<br />
factors and those in the <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong>.<br />
Key Differences<br />
6<br />
Solvency II <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Standard deviations<br />
of gross loss ratios<br />
Based on loss ratios at<br />
end of first year<br />
Coefficients of variation<br />
of gross loss ratios<br />
Based on ultimate loss ratios<br />
Excludes catastrophe risk Includes catastrophe risk<br />
Average-sized company,<br />
parameter and process risk<br />
Large company,<br />
parameter risk only<br />
Standard Deviations vs. Coefficients of Variation<br />
In Solvency II, the premium risk factors are calculated<br />
as standard deviations of historical loss ratios. Within<br />
the Standard Formula, these standard deviations are<br />
applied on the total volume of premium rather than<br />
to the premium net of loadings for costs,<br />
commissions and profit.<br />
Whether this overestimates the risk, and thus the capital<br />
requirement, depends on the company and the line of<br />
business. Certainly the Regulator has made some<br />
conservative assumptions: expenses are assumed to<br />
have the same volatility as the losses, and no profit is<br />
assumed over the cycle. If insurers disagree with these<br />
assumptions they must apply for a partial internal model.<br />
One-Year Emergence vs. View of Ultimate<br />
The Standard Formula premium risk factors and the<br />
corresponding <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> factors appear below.<br />
For ease of comparison, we have restated our factors as<br />
standard deviations rather than coefficients of variation.<br />
Non-Life Premium <strong>Risk</strong>, Gross of Reinsurance<br />
QIS 5 CEIOPS <strong>Risk</strong> <strong>Study</strong><br />
Line StDev StDev # Obs StDev # Obs<br />
Motor — TPL 10.0% 11.5% 209 12.0% 4,631<br />
Motor — Other 7.0% 8.5% 107 n/a n/a<br />
Marine, Aviation<br />
& Transit<br />
17.0% 23.0% 37 29.6% 2,623<br />
Fire 10.0% 15.0% 138 17.4% 4,751<br />
General Liability 15.0% 17.5% 101 19.9% 3,443<br />
Credit 21.5% 28.0% 58 27.6% 570<br />
The factors proposed by CEIOPS and those used in the<br />
QIS 5 exercise can be made comparable with the<br />
factors in this <strong>Study</strong> through appropriate adjustments.<br />
If we use Motor TPL as an example, the CV in this<br />
<strong>Study</strong> corresponds to a standard deviation of 12.0<br />
percent. The <strong>Study</strong> standard deviation is calculated<br />
from an ultimate perspective. We can use the same<br />
dataset that was used in our analysis to recalculate it<br />
from a one-year perspective, producing a standard<br />
deviation of 8.7 percent. Finally, the <strong>Study</strong> parameter<br />
reflects the non-diversifiable premium risk for a large<br />
insurance company whereas the QIS 5 parameters<br />
used in the Standard Formula represent an averagesized<br />
insurance company. As expected the QIS 5<br />
parameter, 10.0 percent, is higher than systemic-only<br />
parameter of 8.7 percent for motor TPL.
Solvency II Correlation Coefficients<br />
Not surprisingly, correlation will be an important<br />
determinant of capital requirements.<br />
Solvency II Correlation Coefficients<br />
Motor -<br />
TPL<br />
Motor -<br />
Other<br />
Marine,<br />
Aviation<br />
& Transit<br />
These coefficients are more conservative than we would<br />
derive from calculating linear correlation since they<br />
must consider nonlinear tail correlation. The factors<br />
applied were derived mainly from an analysis of German<br />
market data for the years 1998 through 2002. As an<br />
example, for the correlation between motor TPL and<br />
general liability, the average correlation was 28 percent<br />
using the data of 89 firms and 1,269 observations. The<br />
final coefficient selected was 50 percent, as seen above.<br />
In this case, we find that the Solvency II correlations are<br />
significantly higher than many of the observed<br />
correlations for European insurers. The correlation<br />
matrix for Germany appears below, and corresponds<br />
to the larger matrix on page 13 of this <strong>Study</strong>.<br />
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> — Germany<br />
Fire<br />
General<br />
Liability<br />
Motor — TPL 50% 50% 25% 50% 25%<br />
Motor — Other 50% 25% 25% 25% 25%<br />
Marine, Aviation<br />
& Transit<br />
Credit<br />
50% 25% 25% 25% 25%<br />
Fire 25% 25% 25% 25% 25%<br />
General Liability 50% 25% 25% 25% 50%<br />
Credit 25% 25% 25% 25% 50%<br />
Motor<br />
Marine,<br />
Aviation<br />
& Transit<br />
Property<br />
General<br />
Liability<br />
Motor 20% 7% 6% 26%<br />
Marine, Aviation<br />
& Transit<br />
20% 22% 10% 45%<br />
Property 7% 22% 0% 31%<br />
General Liability 6% 10% 0% -3%<br />
Credit 26% 45% 31% -3%<br />
Credit<br />
<strong>Aon</strong> Benfield<br />
S2Metrica: ReMetrica Modeling<br />
for Proposed Solvency II<br />
Developing an internal model can be a significant<br />
investment of time and resources. To assist our clients,<br />
<strong>Aon</strong> Benfield has developed S2MetricaSM , a standalone<br />
tool built on ReMetrica ® technology. S2Metrica builds a<br />
simplified internal model from QIS 5 inputs,<br />
supplemented with details about large losses, cat losses,<br />
reinsurance, and the asset portfolio. It also has a<br />
built-in economic scenario generator. Standard output<br />
reports include:<br />
> Profit and loss accounts for different return periods<br />
> Year-end balance sheets<br />
> Comparisons between Standard Formula capital<br />
requirements and those generated by the S2Metrica<br />
internal model<br />
Using S2Metrica, clients can quickly construct a<br />
competent baseline model, freeing them to focus on<br />
critical tasks such as parameterization and appropriate<br />
customization.<br />
ReMetrica is <strong>Aon</strong> Benfield’s innovative financial<br />
modeling tool and the engine of S2Metrica. Insurers<br />
increasingly turn to financial modeling to help them<br />
achieve their corporate objectives. Each insurer has its<br />
own distinct objectives, risks, corporate structure, and<br />
reinsurance strategy.<br />
Using the ReMetrica software platform, insurers can<br />
build adaptable and flexible models that capture their<br />
risks better than the Solvency II Standard Formula and<br />
fully recognize their risk mitigation strategies. In<br />
particular, ReMetrica allows insurers to:<br />
> Create ”off-the-shelf” internal models that cover<br />
both assets and liabilities<br />
> Use customizable templates to monitor internal<br />
metrics and Solvency II requirements such as Fair<br />
Value and SCR<br />
> Model highly customized reinsurance structures<br />
> Integrate partial models, such as non-life and health<br />
lines, in a full internal model covering all aspects of<br />
the balance sheet<br />
The combination of the <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> with<br />
ReMetrica allows our clients to parameterize their models<br />
in an optimal way and to make informed decisions about<br />
risk transfer through reinsurance or the capital markets.<br />
7
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
U.S. <strong>Risk</strong> Parameters<br />
The U.S. portion of the <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong> uses data from nine years of NAIC annual statements for 2,265<br />
individual groups and companies. The database covers all 22 Schedule P lines of business and contains 1.4 million<br />
records of individual company observations from accident years 1992-2009.<br />
The charts below show the loss ratio volatility for each Schedule P line, with and without the effect of the<br />
underwriting cycle. The effect of the underwriting cycle is removed by normalizing loss ratios by accident year prior<br />
to computing volatility. This adjustment decomposes loss ratio volatility into its loss and premium components.<br />
Coefficient of Variation of Gross Loss Ratio | 1992-2009<br />
Products Liability – Claims-Made<br />
8<br />
Private Passenger Auto 14%<br />
Auto Physical Damage<br />
Commercial Auto<br />
Workers Compensation<br />
Warranty<br />
Medical PL – Occurrence<br />
Commercial Multi Peril<br />
Other Liability – Occurrence<br />
Special Liability<br />
Medical PL – Claims-Made<br />
Other Liability – Claims-Made<br />
Products Liability – Occurrence<br />
Homeowners<br />
Other<br />
Reinsurance – Liability<br />
International<br />
Fidelity & Surety<br />
Reinsurance – Property<br />
Reinsurance – Financial<br />
Special Property<br />
Financial Guaranty<br />
All <strong>Risk</strong> No Underwriting Cycle <strong>Risk</strong><br />
17%<br />
24%<br />
28%<br />
31%<br />
32%<br />
34%<br />
37%<br />
39%<br />
40%<br />
43%<br />
47%<br />
51%<br />
52%<br />
67%<br />
68%<br />
70%<br />
85%<br />
91%<br />
102%<br />
The U.S. Underwriting Cycle<br />
Volatility for most lines of business is increased by the insurance<br />
underwriting and pricing cycle. The underwriting cycle acts<br />
simultaneously across many lines of business, driving correlation<br />
between the results of different lines and amplifying the effect<br />
of underwriting risk to primary insurers and reinsurers. Our<br />
analysis demonstrates that the cycle increases volatility<br />
substantially for all major commercial lines, as shown in the<br />
table. For example, the underwriting volatility of other liability<br />
increases by 47 percent, workers compensation by 46 percent,<br />
medical professional liability by 43 percent, and commercial<br />
auto liability by 42 percent.<br />
104%<br />
163%<br />
13%<br />
15%<br />
17%<br />
19%<br />
31%<br />
27%<br />
25%<br />
32%<br />
29%<br />
28%<br />
29%<br />
32%<br />
43%<br />
45%<br />
49%<br />
Impact of the Pricing Cycle<br />
Line<br />
54%<br />
54%<br />
54%<br />
59%<br />
47%<br />
62%<br />
106%<br />
Impact of the<br />
Pricing Cycle<br />
Reinsurance — Liability 50%<br />
Other Liability — Occurrence 47%<br />
Other Liability — Claims-Made 47%<br />
Workers Compensation 46%<br />
Medical PL — Claims-Made 43%<br />
Commercial Auto 42%<br />
Special Liability 33%<br />
Commercial Multi Peril 24%<br />
Homeowners 21%<br />
Private Passenger Auto 9%
Industry Reserve Adequacy: How Long Can Favorable Development Continue?<br />
U.S. P&C industry reserves continue to show<br />
redundancy at the end of 2009. Standard actuarial<br />
reserving methods applied to the industry Schedule P<br />
indicate that there is approximately $22 billion of<br />
excess reserves across all lines of business.<br />
The market is unlikely to harden again as long as the<br />
industry has more than adequate reserves according<br />
U.S. Reserve Volatility by Line<br />
Line<br />
Reserve to<br />
Premium Ratio<br />
% Reserves<br />
Over 10 Yrs Old<br />
Ultimate<br />
Reserve CV<br />
One Year<br />
Reserve CV<br />
% CV<br />
Emerging in<br />
One Year<br />
<strong>Aon</strong> Benfield<br />
to these metrics. Calendar years 2007, 2008 and 2009<br />
saw favorable reserve development, helping to prolong<br />
soft market conditions. We estimate that reserve<br />
redundancies will be depleted in two to three years if<br />
favorable development continues at the pace it has<br />
from 2007 to 2009.<br />
A summary of adequacy by major market segments<br />
appears below.<br />
U.S. Reserve Estimated Adequacy ($B)<br />
Line<br />
Estimated<br />
Reserves<br />
Booked<br />
Reserves<br />
Favorable/(Adverse) Development<br />
2007 2008 2009 Average<br />
Remaining<br />
Redundancy<br />
Years at<br />
Run Rate<br />
Personal Lines 123.0 129.0 5.9 5.4 5.8 5.7 6.0 1.1<br />
Commercial Property 37.9 41.7 1.7 2.6 2.4 2.2 3.8 1.7<br />
Commercial Liability 223.1 237.3 1.0 5.2 3.8 3.3 14.2 4.3<br />
Workers Compensation 114.8 114.0 1.0 1.1 (0.5) 0.6 (0.8) n/a<br />
Total Excl. Financial Guaranty 498.7 522.0 9.5 14.4 11.5 11.8 23.2 2.0<br />
Financial Guaranty 34.1 32.8 (1.2) (12.6) 7.0 (2.3) (1.4) n/a<br />
Total 532.9 554.7 8.3 1.7 18.6 9.5 21.9 2.3<br />
Reserve <strong>Risk</strong> and Leverage<br />
Insurers face two sources of risk from reserves. The first<br />
source is volatility of reserve values over time to<br />
settlement. The second source comes from leverage.<br />
The longer the average duration of the claim payout,<br />
the larger the reserve balance becomes relative to the<br />
premium base. As reserve leverage increases, the<br />
sensitivity of calendar year combined ratio results<br />
compared to changes in reserve balances magnifies.<br />
The first source of volatility has traditionally been<br />
measured with methods such as the Mack method,<br />
which calculates the volatility of the link ratio estimate<br />
of ultimate losses coming from a loss triangle. More<br />
recently, Merz and Wuthrich have published a<br />
methodology that calculates the same estimate, but<br />
over a one-year time horizon to be consistent with<br />
Solvency II. Using these methods, we can estimate the<br />
total reserve volatility and how much of that volatility is<br />
expected to emerge in the next 12 months. We can<br />
also estimate the potential impact of reserve volatility<br />
on next year’s combined ratio.<br />
Using the U.S industry aggregate workers compensation<br />
paid triangle as an example, the total reserve volatility is<br />
estimated at 3.3 percent based on an adjusted Mack<br />
method. The one-year reserve volatility is 2.2 percent<br />
based on an adjusted Merz Wuthrich method, meaning<br />
that 66 percent of the triangle’s volatility emerges in<br />
one development year. The Mack and Merz Wuthrich<br />
methods were both adjusted to account for reserves<br />
more than 10 years old. With reserves levered at 3.4<br />
times premium, the impact on combined ratio of a one<br />
standard deviation change is 7.5 points. For a normal<br />
distribution a one standard deviation move, up or down,<br />
is a one in three year event.<br />
One Year<br />
Combined<br />
Ratio Impact<br />
Homeowners 0.3 1.0% 5.1% 4.8% 94.0% 1.5%<br />
Private Passenger Auto 0.9 4.0% 2.1% 1.7% 79.1% 1.5%<br />
Commercial Auto 1.5 4.9% 2.5% 1.8% 69.4% 2.6%<br />
Commercial Multi Peril 1.2 9.0% 4.6% 3.8% 81.4% 4.6%<br />
Workers Compensation 3.4 26.0% 3.3% 2.2% 66.0% 7.5%<br />
Medical PL - CM 2.5 2.7% 5.1% 4.0% 78.9% 10.0%<br />
Other Liability - Occ 3.4 26.2% 5.2% 3.2% 62.1% 10.8%<br />
Other Liability - CM 2.5 2.3% 6.3% 5.1% 79.7% 12.8%<br />
Products Liability - Occ 7.0 47.4% 9.9% 5.0% 50.9% 35.1%<br />
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<br />
adjusted to account for reserves more than 10 years old.<br />
9
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Best of Times, Worst of Times<br />
In economic capital modeling, the systemic volatility<br />
of each insurance line is a vital input to ensure that<br />
the parameters reflect an appropriate level of risk. As<br />
a result, we have always focused on quantifying this<br />
systemic volatility by measuring the CV. However,<br />
the CV offers guidance only on the variance of the<br />
resulting loss distribution; it does not offer a clear view<br />
of the distribution’s shape. Two insurance lines may<br />
have similar CVs but one may have a much thicker tail<br />
than the other.<br />
To expand our view of insurance risk beyond the CV,<br />
we have studied the largest deviations in loss ratio<br />
between successive accident years. The 18 accident<br />
years in our dataset give us 17 years of changes, and<br />
with this data we selected the biggest increase and<br />
decrease in ultimate accident year loss ratio for each<br />
company. For example, on the following page<br />
commercial auto insurers showed on average a biggest<br />
increase (deteriorating results) of 18.3 loss ratio points<br />
and a biggest decrease (improving results) of 25.1 loss<br />
ratio points from one accident year to the next. The<br />
chart below shows mean changes for all U.S. lines.<br />
Overall this analysis is consistent with the analysis of<br />
CVs. Personal and commercial auto show the smallest<br />
fluctuation in results, followed by the other commercial<br />
lines. The catastrophe-exposed lines — homeowners,<br />
special property, reinsurance property, and financial<br />
guaranty — comprise the top end of the range, with a<br />
mean worst increase of 60 loss ratio points or higher on<br />
a gross basis.<br />
Mean Year-Over-Year Change in Loss Ratio<br />
Biggest Increase (Deteriorating Results)<br />
10<br />
9% 14% 18% 23% 29% 36% 38% 39% 42% 44% 45% 48% 51% 56%<br />
Auto Phys.<br />
Damage<br />
Private Auto<br />
Commercial<br />
Auto<br />
Workers Comp<br />
Medical PL -<br />
CM<br />
Commercial<br />
Multi Peril<br />
Medical PL -<br />
Occ<br />
-12% -18% -25% -25% -41% -46% -52%<br />
Biggest Decrease (Improving Results)<br />
Other Liability -<br />
Occ<br />
Fidelity<br />
& Surety<br />
-41% -32%<br />
Other Liability -<br />
CM<br />
-72%<br />
Reinsurance -<br />
Liability<br />
For most lines, the increases are significant. Among the<br />
commercial lines, the results show a mean increase of<br />
44 percent for other liability claims-made, 39 percent<br />
for other liability occurrence, 36 percent for commercial<br />
multi-peril, 29 percent for medical professional liability<br />
claims-made, 23 percent for workers compensation,<br />
and 18 percent for commercial auto.<br />
The following page shows detailed results for<br />
commercial auto, other liability occurrence and workers<br />
compensation. The impact of the underwriting cycle is<br />
clearly visible in all three lines, as insurers suffered their<br />
biggest increases from 1998 to 2000 and their biggest<br />
decreases in 2002 after the market hardened.<br />
Differences in volatility between lines are also visible. At<br />
39.5 percent, the mean increase for other liability<br />
occurrence is double that of commercial auto;<br />
moreover, its distribution is more positively skewed<br />
with a 90th percentile of 78.4 loss ratio points<br />
compared with 32.5 points for commercial auto.<br />
In planning, insurers may implicitly assume that loss<br />
ratios will be within two or three points of best<br />
estimates. But the evidence shows that results are not<br />
infrequently off by 20 points or more, on both the hard<br />
and soft sides of the cycle. This deviation is further<br />
exacerbated because initial booked loss ratios are<br />
generally near plan and vary little from year to year.<br />
The output from any financial modeling should reflect a<br />
realistic view of outcomes that can deviate from plan.<br />
The results of this extreme value analysis can serve as<br />
useful benchmarks for evaluating model results.<br />
Products Liability -<br />
Occ<br />
Special Liability<br />
-64% -67% -67%<br />
Products Liability -<br />
CM<br />
-93%<br />
66% 67% 73% 78%<br />
Reinsurance -<br />
Financial<br />
-123%<br />
Homeowners<br />
-79%<br />
Special Property<br />
-262%<br />
International<br />
-81%<br />
125%<br />
Reinsurance -<br />
Property<br />
-185%<br />
170% 499%<br />
Financial<br />
Guaranty<br />
-87%<br />
Other<br />
-41%
Year-Over-Year Change in Gross Loss Ratio<br />
Commercial Auto<br />
Probability Density<br />
Biggest Increase<br />
Biggest Decrease<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
-150% -100% -50%<br />
0<br />
0%<br />
Other Liability - Occurrence<br />
Probability Density<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
-150% -100% -50% 0%<br />
Workers Compensation<br />
Probability Density<br />
0<br />
0.3<br />
0.2<br />
0. 0.1<br />
-150% -100% -50% 0%<br />
0<br />
50% 100% 150%<br />
50% 100% 150%<br />
50% 100% 150%<br />
Frequency by Accident Year<br />
0.3<br />
0.2<br />
0.1<br />
0.0<br />
0.0<br />
0.1<br />
0.2<br />
0.3<br />
0.3<br />
0.2<br />
0.1<br />
0.0<br />
0.0<br />
0.1<br />
0.2<br />
0.3<br />
0.3<br />
0.2<br />
0.1<br />
0.0<br />
0.0<br />
0.1<br />
0.2<br />
0.3<br />
1993<br />
1993<br />
1993<br />
1994<br />
1994<br />
1994<br />
1995<br />
1995<br />
1996<br />
1996<br />
1997<br />
1997<br />
1998<br />
1998<br />
1999<br />
Biggest Increase<br />
Mean 18.3%<br />
Median 15.1%<br />
90th %ile 32.5%<br />
1999<br />
2000<br />
Frequency by Accident Year<br />
Biggest Increase<br />
Mean 39.5%<br />
Median 24.8%<br />
90th %ile 78.4%<br />
2000<br />
Frequency by Accident Year<br />
1995<br />
1996<br />
1997<br />
1998<br />
1999<br />
Biggest Increase<br />
Mean 23.1%<br />
Median 20.1%<br />
90th %ile 38.9%<br />
2000<br />
2001<br />
2001<br />
2001<br />
2002<br />
2002<br />
2002<br />
2003<br />
2003<br />
2003<br />
2004<br />
2004<br />
2004<br />
2005<br />
2005<br />
2005<br />
2006<br />
2006<br />
2006<br />
<strong>Aon</strong> Benfield<br />
2007<br />
2007<br />
2007<br />
2008<br />
2008<br />
2008<br />
2009<br />
Biggest Decrease<br />
Mean -25.1%<br />
Median -20.1%<br />
90th %ile -45.2%<br />
2009<br />
Biggest Decrease<br />
Mean -40.8%<br />
Median -27.4%<br />
90th %ile -71.5%<br />
2009<br />
Biggest Decrease<br />
Mean -25.0%<br />
Median -20.5%<br />
90th %ile -46.0%<br />
11
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Correlation and the Pricing Cycle<br />
Correlation of Underwriting Results<br />
Correlation between different lines of business is central to a realistic assessment of aggregate portfolio risk,<br />
and, in fact, becomes increasingly significant as companies grow in size. Modeling is invariably performed using<br />
an analysis-synthesis paradigm: analysis is carried out at the product or business unit level and then aggregated<br />
to the company level. In most applications, results are more significantly impacted by the correlation and<br />
dependency assumptions made during the synthesis step than by all the detailed assumptions made during<br />
the analysis step.<br />
The <strong>Study</strong> determines correlations between lines within each country and also between countries. Although<br />
not shown here, we have also calculated confidence intervals for each correlation coefficient.<br />
Correlation between Lines<br />
Correlation between lines is computed by examining the results from larger companies that write pairs of lines in<br />
the same country. The following tables show a sampling of the results available for Australia, China, Germany, Japan,<br />
the U.K., and the U.S.<br />
Australia<br />
China<br />
12<br />
Accident<br />
& Health<br />
General<br />
Liability<br />
Agriculture<br />
Marine,<br />
Aviation<br />
& Transit<br />
General Liability 19% 21% -24% 21%<br />
Marine, Aviation & Transit 19% 31% -3% 21%<br />
Motor 21% 31% 25% 14%<br />
Property -24% -3% 25% -6%<br />
Workers Comp 21% 21% 14% -6%<br />
Credit<br />
Engineering<br />
General<br />
Liability<br />
Marine,<br />
Aviation<br />
& Transit<br />
Accident & Health 24% 31% 39% 53% 38% 59% 56%<br />
Agriculture 24% 53% n/a 29% 9% 16% -4%<br />
Credit 31% 53% n/a 18% 18% 34% 25%<br />
Engineering 39% n/a n/a 68% 38% 64% 28%<br />
General Liability 53% 29% 18% 68% 29% 62% 54%<br />
Marine, Aviation & Transit 38% 9% 18% 38% 29% 41% 24%<br />
Motor 59% 16% 34% 64% 62% 41% 59%<br />
Property 56% -4% 25% 28% 54% 24% 59%<br />
Motor<br />
Property<br />
Motor<br />
Workers<br />
Comp<br />
Property<br />
Correlation is a measure of association<br />
between two random quantities. It<br />
varies between -1 and +1, with +1<br />
indicating a perfect increasing linear<br />
relationship and -1 a perfect decreasing<br />
relationship. The closer the coefficient<br />
is to either +1 or -1 the stronger the<br />
linear association between the two<br />
variables. A value of 0 indicates no<br />
linear relationship whatsoever.<br />
All correlations in the <strong>Study</strong> are<br />
estimated using the Pearson<br />
sample correlation coefficient.<br />
In each table the correlations shown in<br />
bold are statistically different from zero<br />
at the 90 percent confidence level.
Germany<br />
Japan<br />
U.K.<br />
U.S.<br />
Accident<br />
& Health<br />
Commercial<br />
Auto<br />
Accident<br />
& Health<br />
Accident<br />
& Health<br />
Commercial<br />
Multi Peril<br />
Assistance<br />
Commercial<br />
Lines Liability<br />
General<br />
Liability<br />
Homeowners<br />
Medical<br />
Malpractice<br />
CM<br />
Other Liability<br />
CM<br />
Other Liability<br />
Occ<br />
Personal Auto<br />
Liability<br />
<strong>Aon</strong> Benfield<br />
Products<br />
Liability<br />
Occ<br />
Commercial Auto 53% 8% 73% 44% 67% 28% 72% 63%<br />
Commercial Multi Peril 53% 21% 56% 41% 48% 28% 40% 42%<br />
Homeowners 8% 21% 1% -2% -1% 8% 14% -7%<br />
Commercial<br />
Motor<br />
Marine,<br />
Aviation<br />
& Transit<br />
Accident & Health 27% 1% 50% 42% 53%<br />
General Liability 27% 0% 3% 32% 28%<br />
Marine, Aviation & Transit 1% 0% 16% 33% -4%<br />
Motor 50% 3% 16% 61% 43%<br />
Property 42% 32% 33% 61% 32%<br />
Workers Comp 53% 28% -4% 43% 32%<br />
Accident & Health 47% n/a 55% -49% 15% 55%<br />
Commercial Lines Liability 47% 72% 40% 69% 49% 56%<br />
Commercial Motor n/a 72% 51% -9% -14% 61%<br />
Commercial Property 55% 40% 51% 33% 57% 39%<br />
Financial Loss -49% 69% -9% 33% 16% -15%<br />
Household & Domestic 15% 49% -14% 57% 16% 30%<br />
Medical Malpractice CM 73% 56% 1% 72% 78% 58% 76% 71%<br />
Other Liability CM 44% 41% -2% 72% 57% 42% 29% 62%<br />
Other Liability Occ 67% 48% -1% 78% 57% 33% 66% 63%<br />
Personal Auto Liability 28% 28% 8% 58% 42% 33% 42% 33%<br />
Products Liability Occ 72% 40% 14% 76% 29% 66% 42% 63%<br />
Commercial<br />
Property<br />
Workers Comp 63% 42% -7% 71% 62% 63% 33% 63%<br />
Financial Loss<br />
Household<br />
& Domestic<br />
Private Motor 55% 56% 61% 39% -15% 30%<br />
Credit<br />
General<br />
Liability<br />
Legal<br />
Protection<br />
Motor<br />
Marine,<br />
Aviation<br />
& Transit<br />
Accident & Health 62% -37% 10% -13% -13% -12% -3%<br />
Assistance 62% n/a -40% 39% 83% -6% -40%<br />
Credit -37% n/a -3% -24% 45% 26% 31%<br />
General Liability 10% -40% -3% -10% 10% 6% 0%<br />
Legal Protection -13% 39% -24% -10% -48% 20% -21%<br />
Marine, Aviation & Transit -13% 83% 45% 10% -48% 20% 22%<br />
Motor -12% -6% 26% 6% 20% 20% 7%<br />
Property -3% -40% 31% 0% -21% 22% 7%<br />
Property<br />
Motor<br />
Property<br />
Workers<br />
Comp<br />
Private Motor<br />
Workers<br />
Comp<br />
13
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Correlation between Countries<br />
In addition to correlation between lines of business, global insurers must also consider the correlation of business<br />
written in different countries. We estimated these correlation coefficients based on country-level loss ratios by line<br />
by year. The following tables show results by region for motor and liability lines.<br />
Americas - Motor<br />
Europe - Motor<br />
14<br />
Argentina<br />
Asia Pacific - Motor<br />
Australia<br />
Austria<br />
Brazil<br />
China<br />
Belgium<br />
Canada<br />
Hong Kong<br />
France<br />
Chile<br />
Argentina 49% -62% -71% -14% 23% 10% -13% -14% -28%<br />
Brazil 49% -18% -5% -26% -3% -51% 34% 34% 32%<br />
Canada -62% -18% 61% 22% -24% -27% 10% 85% 43%<br />
Chile -71% -5% 61% 14% -13% -31% 14% 20% -4%<br />
Colombia -14% -26% 22% 14% -28% 16% 37% 34% -5%<br />
Mexico 23% -3% -24% -13% -28% 6% -47% -24% -58%<br />
Peru 10% -51% -27% -31% 16% 6% -18% -21% -32%<br />
Puerto Rico -13% 34% 10% 14% 37% -47% -18% 41% 7%<br />
United States -14% 34% 85% 20% 34% -24% -21% 41% 41%<br />
Venezuela -28% 32% 43% -4% -5% -58% -32% 7% 41%<br />
India<br />
Australia 63% 54% 44% -35% -49% 82% 8% -34% 21%<br />
China 63% 24% -3% -7% 14% -30% -10% -20% 12%<br />
Hong Kong 54% 24% 55% -46% -46% 5% 37% -67% -23%<br />
India 44% -3% 55% -45% -32% 97% 13% -34% 33%<br />
Japan -35% -7% -46% -45% 50% 85% -2% 68% -3%<br />
Malaysia -49% 14% -46% -32% 50% 72% -8% 41% 33%<br />
Russia 82% -30% 5% 97% 85% 72% 14% -35% -81%<br />
Singapore 8% -10% 37% 13% -2% -8% 14% -35% -37%<br />
South Korea -34% -20% -67% -34% 68% 41% -35% -35% 34%<br />
Taiwan 21% 12% -23% 33% -3% 33% -81% -37% 34%<br />
Germany<br />
Austria 52% 64% 65% 38% 87% 44% -12% 82% 12%<br />
Belgium 52% 63% 79% 63% 45% 61% -41% 7% 34%<br />
France 64% 63% 59% 41% 54% 45% -33% 49% 23%<br />
Germany 65% 79% 59% 7% 54% 35% -29% 23% 10%<br />
Italy 38% 63% 41% 7% -5% 71% -41% 17% 62%<br />
Netherlands 87% 45% 54% 54% -5% 37% 1% 80% -19%<br />
Norway 44% 61% 45% 35% 71% 37% -47% 41% 32%<br />
Spain -12% -41% -33% -29% -41% 1% -47% 30% 7%<br />
Switzerland 82% 7% 49% 23% 17% 80% 41% 30% -2%<br />
United Kingdom 12% 34% 23% 10% 62% -19% 32% 7% -2%<br />
Colombia<br />
Japan<br />
Italy<br />
Mexico<br />
Malaysia<br />
Netherlands<br />
Peru<br />
Russia<br />
Norway<br />
Puerto Rico<br />
Singapore<br />
Spain<br />
United States<br />
South Korea<br />
Switzerland<br />
Venezuela<br />
Taiwan<br />
United<br />
Kingdom
Americas - Liability<br />
Europe - Liability<br />
Argentina<br />
Asia Pacific - Liability<br />
Austria<br />
Australia<br />
Brazil<br />
Belgium<br />
China<br />
Canada<br />
Denmark<br />
Hong Kong<br />
France<br />
Germany<br />
Italy<br />
Norway<br />
Spain<br />
<strong>Aon</strong> Benfield<br />
Austria -46% 55% 86% 88% 49% 19% -36% 29% 65%<br />
Belgium -46% -29% -54% -35% -43% 21% -81% 13% -31%<br />
Denmark 55% -29% 65% 60% 71% 16% 10% 29% 36%<br />
France 86% -54% 65% 90% 61% 16% -26% 32% 59%<br />
Germany 88% -35% 60% 90% 54% 5% -41% 27% 65%<br />
Italy 49% -43% 71% 61% 54% 20% 15% 26% 5%<br />
Norway 19% 21% 16% 16% 5% 20% -39% 3% 4%<br />
Spain -36% -81% 10% -26% -41% 15% -39% -24% 12%<br />
Switzerland 29% 13% 29% 32% 27% 26% 3% -24% 10%<br />
United Kingdom 65% -31% 36% 59% 65% 5% 4% 12% 10%<br />
Japan<br />
Australia 81% 46% -33% -11% -15% -30% 6% 32%<br />
China 81% 36% -13% 22% 36% 91% -28% 17%<br />
Hong Kong 46% 36% 1% -9% 2% -31% -15% -28%<br />
Japan -33% -13% 1% 45% 7% 72% -3% 20%<br />
Malaysia -11% 22% -9% 45% -57% 70% -35% 26%<br />
Russia -15% 36% 2% 7% -57% -60% 65% 47%<br />
Singapore -30% 91% -31% 72% 70% -60% -74% 10%<br />
South Korea 6% -28% -15% -3% -35% 65% -74% 6%<br />
Taiwan 32% 17% -28% 20% 26% 47% 10% 6%<br />
Chile<br />
Argentina 33% 18% -5% -67% 6% 3% 49% 30% 2%<br />
Brazil 33% 33% 18% -59% 22% 15% 75% 72% -7%<br />
Canada 18% 33% -17% -32% 25% 24% 26% 81% 5%<br />
Chile -5% 18% -17% -28% 44% -12% 26% -1% 31%<br />
Colombia -67% -59% -32% -28% -18% -26% -76% -67% -23%<br />
Mexico 6% 22% 25% 44% -18% -3% 14% 23% -20%<br />
Peru 3% 15% 24% -12% -26% -3% 0% 44% -18%<br />
Puerto Rico 49% 75% 26% 26% -76% 14% 0% 69% 2%<br />
United States 30% 72% 81% -1% -67% 23% 44% 69% 18%<br />
Venezuela 2% -7% 5% 31% -23% -20% -18% 2% 18%<br />
Colombia<br />
Malaysia<br />
Mexico<br />
Russia<br />
Peru<br />
Singapore<br />
Puerto Rico<br />
United States<br />
South Korea<br />
Switzerland<br />
Venezuela<br />
Taiwan<br />
United<br />
Kingdom<br />
15
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Modeling Dependence<br />
Dependence is a core component of economic capital<br />
modeling. <strong>Risk</strong> managers frequently discuss correlation,<br />
and this <strong>Study</strong> includes numerous correlation matrices.<br />
But correlation alone does not fully describe<br />
dependence. There are many ways to combine two<br />
variables to have the same linear correlation coefficient.<br />
For example, the familiar symmetric, elliptical contours<br />
of the normal copula can have the same linear<br />
correlation as a more pinched distribution, and<br />
pinching can occur either on the left, the right or both<br />
sides. The impact of dependence is most clearly seen in<br />
the distribution of the sum (or portfolio return) of the<br />
two variables, with extreme tail correlation producing<br />
an aggregate distribution with much fatter tails.<br />
Variables in financial markets often exhibit such<br />
extreme tail correlation, as seen in the left chart<br />
below. In this plot, the outliers at the 10.0 percent<br />
and 1.0 percent significance levels assuming a<br />
multivariate normal distribution comprise 11.9 percent<br />
and 2.1 percent of the observations. This kind of<br />
behavior has led many analysts to reject the normal<br />
distribution as a model for dependence.<br />
Academics and risk managers have introduced many<br />
different copulas as means of modeling dependence<br />
with flexible tail behavior. But the appropriateness of<br />
different copulas for insurance losses has been less<br />
well tested.<br />
Daily Stock Returns of Two Financial<br />
Stocks Through the Crisis<br />
16<br />
3.0<br />
2.0<br />
1.0<br />
-1.0<br />
-2.0<br />
-3.0<br />
Normal Transformed Data<br />
0<br />
-3.0 -2.0 -1.0 0 1.0 2.0 3.0<br />
Our study of U.S. data shows that apart from<br />
correlation driven by property catastrophe events there<br />
is little evidence of multi-line extreme correlation. The<br />
right chart below compares results for other liability<br />
occurrence and workers compensation. In this case, the<br />
outliers at the 10.0 percent and 1.0 percent significance<br />
levels assuming multivariate normal distribution<br />
represent 10.9 percent and 1.0 percent of the<br />
observations — well within expectations. Analysis of<br />
other U.S. lines shows similar results.<br />
There is still the possibility that events with long return<br />
periods are not shown in our 18-year data sample — for<br />
example, the impact of asbestos on other liability<br />
occurrence and products liability. We may yet observe<br />
extreme tail correlation in insurance results. But its<br />
absence during the past 18 years suggests that it is not<br />
nearly as commonplace as in financial markets.<br />
We conclude that while the traditional approach to<br />
modeling dependence using the normal copula has<br />
known limitations, it is not rejected by the data as a<br />
model for correlation between non-catastrophe<br />
insurance lines. Catastrophe simulation models address<br />
this issue for catastrophe lines by including correlation<br />
as a model output.<br />
Products Liability Occurrence vs.<br />
Workers Compensation<br />
3.0<br />
2.0<br />
1.0<br />
-1.0<br />
-2.0<br />
-3.0<br />
Normal Transformed Data<br />
0<br />
-3.0 -2.0 -1.0 0 1.0 2.0 3.0
Size and Correlation<br />
Insurers of different sizes face different levels of<br />
correlation across their portfolios. For small insurers,<br />
the process risk in each line of business may keep the<br />
correlation observed between lines relatively low. In<br />
contrast, large insurers are exposed primarily to the<br />
systemic risk in each line, but correlation in systemic<br />
risk will drive observed correlations across the portfolio.<br />
The U.S. correlation coefficients published earlier in<br />
the <strong>Study</strong> represent an average level of correlation for<br />
companies with premium volume above a threshold<br />
Workers Compensation vs. Other Liability — Occurrence Commercial Auto vs. Other Liability — Occurrence<br />
Correlation Above Threshold<br />
1.0<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
0<br />
10 100 1,000 10,000<br />
Size Threshold, $M<br />
Correlation<br />
<strong>Risk</strong> <strong>Study</strong> Coefficient<br />
Correlation Above Threshold<br />
1.0<br />
0.9<br />
0.8<br />
0.7<br />
0.6<br />
0.5<br />
0.4<br />
0.3<br />
0.2<br />
0.1<br />
<strong>Aon</strong> Benfield<br />
of $100 million. We selected this threshold as<br />
representative of the size of a typical product division<br />
within a medium to large insurance company. The<br />
observed level of correlation varies within this threshold,<br />
as shown below for several pairs of lines. Companies<br />
with volume exceeding $100 million will observe an<br />
increasing level of correlation between lines. For<br />
example, between workers compensation and other<br />
liability occurrence, the correlation at $100 million is<br />
63 percent, at $500 million it is 72 percent, and at<br />
$1 billion it is 80 percent.<br />
0<br />
10 100 1,000 10,000<br />
Size Threshold, $M<br />
Correlation<br />
The table below shows the measured correlation coefficients at different premium thresholds between U.S.<br />
Schedule P lines. In each case, both premium amounts exceed the threshold.<br />
<strong>Risk</strong> <strong>Study</strong> Coefficient<br />
Line of Business Correlation by Premium Size Threshold<br />
Line A Line B $25M $50M $100M $250M $500M $1,000M<br />
Homeowners Private Passenger Auto 10% 11% 8% 17% 33% 33%<br />
Commercial Multi Peril Commercial Auto 33% 37% 53% 55% 73% 58%<br />
Commercial Multi Peril Workers Compensation 27% 31% 42% 48% 48% 59%<br />
Commercial Multi Peril Other Liability — Occ 22% 27% 48% 46% 53% 53%<br />
Commercial Auto Workers Compensation 49% 60% 63% 71% 73% 85%<br />
Commercial Auto Other Liability — Occ 51% 54% 67% 78% 82% 78%<br />
Workers Compensation Other Liability — Occ 44% 51% 63% 67% 72% 80%<br />
Other Liability — Occ Other Liability — CM 45% 50% 57% 55% 59% 65%<br />
Medical PL — CM Other Liability — CM 65% 72% 72% 64% 68% n/a<br />
Medical PL — CM Workers Compensation 47% 72% 71% 73% 77% n/a<br />
The larger the company, the more important correlation becomes for the company. Regulators and rating agencies<br />
scrutinize correlation assumptions in their evaluations of capital adequacy. <strong>Aon</strong> Benfield Analytics can help<br />
companies understand the sensitivity of their model results to correlation assumptions and guide them during the<br />
rating agency review process.<br />
17
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Macroeconomic Correlation<br />
Correlation among macroeconomic factors is a very<br />
important consideration in risk modeling. The<br />
interaction of inflation and GDP growth with loss<br />
ratios and investment returns has a profound effect on<br />
insurer financial health and stability.<br />
The matrix below shows correlation coefficients for<br />
various macroeconomic variables that impact an<br />
insurer’s balance sheet.<br />
The Consumer Price Index (CPI-U) and Producer Price<br />
Index (PPI) are highly correlated, but they do not<br />
show particularly strong correlation with other factors.<br />
This may be because inflation has been relatively tame<br />
for the last 25 years.<br />
GDP growth shows strong negative correlation with<br />
changes in unemployment. When GDP drops — or<br />
unemployment increases — credit spreads tend to<br />
increase, property values fall and the VIX increases. We<br />
were surprised not to see stronger correlation between<br />
GDP and stock returns without a lag.<br />
Treasury yields and corporate bond spreads are inversely<br />
correlated; financial market fears may push investors to<br />
flee corporates for the safety of treasuries, causing<br />
corporate yields to rise and treasury yields to fall.<br />
Macroeconomic Correlations<br />
18<br />
Inflation<br />
(CPI-U)<br />
Inflation (PPI)<br />
GDP Growth<br />
Unemployment<br />
Change<br />
Stock volatility measured by the VIX Index is sensitive to<br />
fear and directionally has the appropriate signs: positive<br />
correlation with spreads and unemployment, negative<br />
correlation with GDP and stock returns.<br />
These coefficients represent only the beginning of an<br />
analysis of macroeconomic dependency. Lags may be<br />
appropriate among certain variables. For example,<br />
GDP and stock returns show the strongest correlation<br />
when stock returns lead GDP by two quarters,<br />
suggesting that stock prices adjust as soon as<br />
expectations for GDP change.<br />
It is also important to consider values that shift over<br />
time. In successive eight-quarter periods, stock returns<br />
and property returns showed zero or negative<br />
correlations until the recent financial crisis when<br />
correlations turned strongly positive. This fact alone<br />
suggests that a simplistic view of correlation across the<br />
balance sheet will expose insurers to significant risks.<br />
Model output is only as good as the assumptions used,<br />
and with the prevalence of DFA modeling and<br />
economic scenario generators there is potential for<br />
naïve assumptions to drive decision making. In the next<br />
section, we look more closely at the potential impact of<br />
inflation on insurer balance sheets.<br />
Inflation (CPI-U) 78% -3% -2% 32% 26% -11% -25% -12% -23% 13%<br />
Inflation (PPI) 78% 4% -7% 30% 11% -4% -20% -7% -22% 14%<br />
GDP Growth -3% 4% -70% -4% 25% -64% -69% 5% -44% 52%<br />
Unemployment Change -2% -7% -70% -3% -27% 62% 77% -1% 57% -51%<br />
3-Month T-Bill Rate 32% 30% -4% -3% 98% -34% -58% -6% -25% 13%<br />
1-3 Year T-Bill 26% 11% 25% -27% 98% -39% -61% 19% -28% 10%<br />
AAA-AA 3-5 Year Spread -11% -4% -64% 62% -34% -39% 85% -43% 62% -66%<br />
BBB 3-5 Year Spread -25% -20% -69% 77% -58% -61% 85% -36% 67% -53%<br />
S&P 500 Returns -12% -7% 5% -1% -6% 19% -43% -36% -51% 17%<br />
Stock Volatility Index, VIX -23% -22% -44% 57% -25% -28% 62% 67% -51% -32%<br />
Property Returns 13% 14% 52% -51% 13% 10% -66% -53% 17% -32%<br />
3-Month<br />
T-Bill Rate<br />
1-3 Year<br />
T-Bill<br />
AAA-AA 3-5<br />
Year Spread<br />
BBB 3-5<br />
Year Spread<br />
S&P 500<br />
Returns<br />
VIX<br />
Property<br />
Returns
Managing Inflation <strong>Risk</strong><br />
<strong>Risk</strong> managers today recognize inflation as a potential<br />
threat in the years ahead, but struggle to quantify the<br />
risk and identify ways to mitigate it. The historical record<br />
reminds us that periods of high inflation have occurred<br />
repeatedly in virtually every economy. But during the<br />
last 25 years, inflation has been contained at low levels<br />
in the U.S. and other developed economies. As a result,<br />
the time series available to measure inflation’s impact on<br />
the current insurance industry will not serve us well in<br />
anticipating the next potential inflation shock.<br />
Inflation 1914-2009<br />
20%<br />
15%<br />
10%<br />
5%<br />
0%<br />
-5%<br />
-10%<br />
-15%<br />
1910<br />
Impact on Insurers<br />
Periods of high inflation, and high inflation volatility in<br />
particular, have generally preceded periods of rising<br />
accident year combined ratios.<br />
Inflation and Combined Ratio<br />
14%<br />
12%<br />
10%<br />
8%<br />
6%<br />
4%<br />
2%<br />
0%<br />
1970<br />
1975<br />
1930<br />
1980<br />
1985<br />
1950<br />
1990<br />
1970<br />
1995<br />
This lagged relationship between inflation and<br />
combined ratios is driven by three factors:<br />
> Lags between the incidence of inflation rate changes<br />
and recognition in loss reserving systems and rate<br />
indications<br />
> Lags between attempts to raise rates and actual<br />
rate changes due to regulatory and competitive<br />
limitations<br />
> Immediate impact on balance sheets<br />
1990<br />
Combined Ratio<br />
Inflation<br />
2000<br />
2005<br />
2010<br />
125%<br />
120%<br />
115%<br />
110%<br />
105%<br />
100%<br />
95%<br />
90%<br />
Industry Balance Sheet Impact<br />
<strong>Aon</strong> Benfield<br />
In the table below we demonstrate the last of these<br />
three effects for U.S. insurers using the 2009 industry<br />
balance sheet and the sensitivity of bond holdings,<br />
equity holdings and nominal loss reserves to changes<br />
in inflation. We show that a 200 basis point increase<br />
in inflation could result in a $70.9 billion impact on<br />
surplus, a 13.7 percent decrease.<br />
Impact of Inflation Increase on Industry Balance Sheet<br />
Assets<br />
Balance<br />
($B)<br />
Pre-tax<br />
Sensitivity<br />
%<br />
After-Tax<br />
Sensitivity<br />
%<br />
After-<br />
Tax Impact<br />
($B)<br />
Bonds 866.3 -7.3% -4.7% -40.9<br />
Stocks 227.0 -6.0% -3.9% -8.9<br />
Other Assets 398.9 0.0% 0.0%<br />
Total Assets 1,492.2 -5.1% -3.3% -49.7<br />
Liabilities<br />
Net Loss Reserves 564.0 5.8% 3.8% 21.2<br />
Other Liabilities 411.4 0.0% 0.0%<br />
Total Liabilitites 975.4 3.3% 2.2% 21.2<br />
Surplus 516.8 -21.1% -13.7% -70.9<br />
The primary drivers of these changes are bonds, stocks<br />
and loss reserves.<br />
Bonds — Changes in inflation affect bond yields<br />
differently for bonds of different maturities. Overall, a<br />
200 basis point increase in inflation would be expected<br />
to decrease the value of the industry bond portfolio by<br />
7.3 percent.<br />
Stocks — Stock portfolios are often assumed to have<br />
a high sensitivity to changes in bond yields. However,<br />
since empirically 80 percent of changes in inflation<br />
expectations ultimately flow through the S&P 500 as<br />
higher nominal dividends, this significantly offsets the<br />
effect of discounting these dividends at higher yields.<br />
The overall affect is approximately a 6.0 percent decline<br />
in the value of a diversified equity portfolio for a 200<br />
basis point change in inflation.<br />
Loss Reserves — The impact of inflation, particularly<br />
when measured as changes in the broad CPI-U,<br />
varies by line of business. Many short-tailed lines are<br />
impacted directly, though modestly, as a result of<br />
their quick settlement. In contrast, long-tailed lines<br />
are impacted more significantly by components of the<br />
CPI-U, which results in a more muted relationship to<br />
general inflation. Overall, for the industry reserves, we<br />
estimate a 5.8 percent increase in undiscounted loss<br />
reserves for a 200 basis point increase in inflation.<br />
19
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Managing Inflation <strong>Risk</strong><br />
Insurers can seek to manage this risk in several ways.<br />
Interest Rate <strong>Risk</strong> Management — An interest rate risk<br />
management process should distinguish between<br />
inflation duration and real interest rate duration, thus<br />
enhancing an asset-liability management framework.<br />
It is often assumed that asset and liability portfolios<br />
with equal present values and with the same duration<br />
will respond similarly on a discounted basis to changes<br />
in bond yields. However, this may not be the case if the<br />
changes are driven by changes in inflation rates. In the<br />
previous balance sheet example, a 200 basis point<br />
increase in inflation caused a $21.2 billion increase in<br />
the undiscounted loss reserves, and if interest rates rose<br />
as well, then there would also have been an increase in<br />
the amount of discounting leaving discounted reserves<br />
approximately unchanged.<br />
On the asset side, a bond portfolio of comparable size<br />
and duration would have decreased by $17.9 billion as<br />
a result of the same change in interest rates. Despite<br />
being “matched”, the net effect would be a three<br />
percent decrease in surplus.<br />
Asset Allocation — Several asset classes, including<br />
inflation-indexed bonds (TIPS), commodities and real<br />
estate, offer varying degrees of inflation hedging and<br />
can be considered as a natural part of insurers’ asset<br />
portfolios. However, careful consideration must be<br />
given to the impact on expected investment<br />
returns — in the case of TIPS especially — and to the<br />
additional volatility and portfolio management skills<br />
needed for these asset classes.<br />
Equity Allocations — The conventional wisdom is that<br />
equities are a natural hedge against inflation, since<br />
companies can pass rising costs along to consumers.<br />
But over short horizons, equities have not always<br />
outperformed inflation. In the 1970s, inflation soared<br />
well above 10 percent even as real equity returns were<br />
negative. An investment in the S&P 500 made in 1973<br />
would only have broken even in real dollar terms in<br />
1986. When operating performance is measured over<br />
a five-year or ten-year period, these results suggest<br />
that insurers holding equities may face flat or even<br />
negative investment returns while their liabilities<br />
increase in value.<br />
20<br />
Inflation CPI-U, 1970-2009<br />
15%<br />
10%<br />
5%<br />
0%<br />
1970<br />
1975<br />
1980<br />
1985<br />
S&P 500 Year 1-Year Change<br />
40%<br />
20%<br />
0%<br />
-20%<br />
-40%<br />
1970<br />
1975<br />
1980<br />
1985<br />
1990<br />
1990<br />
1995<br />
1995<br />
2000<br />
2000<br />
S&P 500 Cumulative Real Index Value vs. CPI-U<br />
4%<br />
3%<br />
2%<br />
1%<br />
0%<br />
1970<br />
Base Year = 1970<br />
1975<br />
1980<br />
1985<br />
1990<br />
1995<br />
2000<br />
2005<br />
2005<br />
2005<br />
The experience of the 1970’s suggests that a broad index<br />
such as the S&P 500 may be less effective than a more<br />
carefully constructed equity portfolio. Sectors such as<br />
energy, medical services and defense offer a greater<br />
degree of inflation hedging than other sectors; value<br />
stocks also tend to perform better in inflationary<br />
environments than growth stocks. Equities do offer a<br />
degree of natural inflation hedging, but history suggests<br />
that risk managers should pay careful attention to sector<br />
and style allocations.<br />
Reinsurance — Inflation is just one of many sources of<br />
volatility for liabilities. Rather than isolate and manage<br />
this risk separately, an alternative could be to<br />
incorporate aggregate stop loss or adverse<br />
development covers into reinsurance programs with<br />
coverage terms selected to respond appropriately to<br />
adverse inflation impacts on current or prior accident<br />
year claims.
Global Market Review<br />
With rates continuing to soften and investment yields<br />
depressed, insurers are under intense pressure to find<br />
profitable areas to grow. The following pages present a<br />
summary of global insurance markets: the size of each<br />
market by premium, premium relative to GDP<br />
(insurance penetration ratio), loss ratios, and volatility<br />
of loss ratios. We have segmented premium into motor,<br />
property and liability lines for the top 50 markets.<br />
Global Premium by Product Line<br />
Motor: $513B<br />
U.S.<br />
Middle East & Africa<br />
Rest of Europe<br />
Property: $388B<br />
U.S.<br />
Middle East & Africa<br />
Liability: $277B<br />
U.S.<br />
Middle East & Africa<br />
Rest of Europe<br />
Brazil<br />
Brazil<br />
Rest of Europe<br />
Brazil<br />
Canada<br />
Rest of Americas<br />
China<br />
U.K.<br />
Japan<br />
Germany<br />
Rest of EUR Area<br />
U.K.<br />
South Korea<br />
Rest of APAC<br />
France<br />
Canada<br />
Rest of Americas<br />
China<br />
Japan<br />
South Korea<br />
Rest of APAC<br />
France<br />
Germany<br />
Rest of EUR Area<br />
Canada<br />
Rest of Americas<br />
China<br />
Japan<br />
South Korea<br />
U.K.<br />
Rest of EUR Area<br />
Rest of APAC<br />
France<br />
Germany<br />
Top 50 Markets by Gross Written Premium<br />
Country<br />
P&C GWP<br />
($B)<br />
GDP ($B)<br />
Premium/<br />
GDP Ratio<br />
<strong>Aon</strong> Benfield<br />
GDP Per<br />
Capita<br />
U.S. 462.33 14,256.30 3.2% 45,954<br />
Japan 76.12 5,067.53 1.5% 39,963<br />
U.K. 71.93 2,174.53 3.3% 35,482<br />
Germany 67.10 3,346.70 2.0% 40,673<br />
France 62.66 2,649.39 2.4% 41,359<br />
Italy 44.09 2,112.78 2.1% 36,370<br />
China 42.10 4,909.28 0.9% 3,691<br />
Spain 33.88 1,460.25 2.3% 36,012<br />
S. Korea 32.55 929.12 3.5% 19,104<br />
Canada 29.02 1,336.07 2.2% 39,576<br />
Australia 21.40 924.84 2.3% 42,984<br />
Brazil 17.02 1,571.98 1.1% 7,817<br />
Netherlands 15.67 792.13 2.0% 47,198<br />
Russia 12.48 1,230.73 1.0% 8,829<br />
Switzerland 11.39 500.26 2.3% 65,621<br />
Belgium 10.58 468.55 2.3% 44,952<br />
Austria 9.13 384.91 2.4% 46,859<br />
Norway 8.64 381.77 2.3% 81,638<br />
Denmark 8.20 309.60 2.7% 56,131<br />
Mexico 7.41 874.90 0.8% 7,779<br />
Sweden 6.89 406.07 1.7% 44,751<br />
Venezuela 6.34 314.15 2.0% 11,540<br />
Poland 5.89 430.08 1.4% 11,181<br />
Argentina 5.76 308.74 1.9% 7,468<br />
Turkey 5.61 617.10 0.9% 7,931<br />
India 5.01 1,296.09 0.4% 1,105<br />
Ireland 4.80 227.19 2.1% 53,455<br />
South Africa 4.66 285.98 1.6% 5,823<br />
Portugal 4.49 227.68 2.0% 21,207<br />
Czech Republic 4.24 190.27 2.2% 18,651<br />
Finland 4.20 237.51 1.8% 45,197<br />
U.A.E. 3.41 198.69 1.7% 39,933<br />
Iran 3.38 286.06 1.2% 4,267<br />
Greece 3.32 355.88 0.9% 33,105<br />
Israel 3.28 194.79 1.7% 26,488<br />
Malaysia 2.84 191.60 1.5% 7,324<br />
Thailand 2.80 263.86 1.1% 3,973<br />
Taiwan 2.77 355.47 0.8% 15,438<br />
Luxembourg 2.73 52.45 5.2% 105,417<br />
Colombia 2.54 230.84 1.1% 5,222<br />
Ukraine 2.39 113.55 2.1% 2,500<br />
Romania 2.29 161.11 1.4% 7,263<br />
Indonesia 2.24 540.28 0.4% 2,224<br />
New Zealand 2.05 125.16 1.6% 29,434<br />
Hong Kong 1.98 215.36 0.9% 30,376<br />
Chile 1.95 163.67 1.2% 9,773<br />
Hungary 1.93 128.96 1.5% 13,053<br />
Singapore 1.81 182.23 1.0% 38,764<br />
Puerto Rico 1.74 67.90 2.6% 17,070<br />
Saudi Arabia 1.68 369.18 0.5% 12,640<br />
Grand Total 1,148.72 54,419.49 2.1% 11,416<br />
21
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Global Premium, Loss Ratio and Volatility: Motor<br />
22<br />
Country<br />
Gross Written Premium Average Loss Ratio Graph<br />
Latest ($M)<br />
5 Yr Annual<br />
Growth<br />
1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR<br />
Argentina 2,692 16.4% 66% 68% 67% 4% 6%<br />
Australia 7,726 4.3% 98% 98% 92% 9% 9%<br />
Austria 3,923 3.0% 71% 66% 65% 8% 11%<br />
Belgium 4,425 4.3% 91% 79% 76% 10% 12%<br />
Brazil 9,996 19.3% 65% 59% 59% 5% 9%<br />
Canada 13,607 -3.3% 76% 74% 73% 6% 8%<br />
Chile 523 14.0% 69% 66% 66% 4% 6%<br />
China 49,055 49.7% 60% 55% 56% 4% 6%<br />
Colombia 1,177 16.6% 56% 55% 54% 4% 7%<br />
Czech Republic 2,120 9.2% 51% 51% 52% 4% 8%<br />
Denmark 2,363 4.0% 65% 58% 62% 11% 16%<br />
Finland 1,778 6.5% 78% 78% 78% 6% 7%<br />
France 52,439 22.1% 82% 82% 81% 3% 4%<br />
Germany 27,924 0.0% 96% 95% 92% 5% 5%<br />
Hong Kong 342 -1.2% 60% 62% 58% 9% 15%<br />
Hungary 1,045 1.2% 60% 59% 59% 2% 4%<br />
India 2,755 10.2% 76% 70% 76% 17% 19%<br />
Indonesia 1,367 29.1% 49% 50% 47% 5% 10%<br />
Iran 4,957 45.9% 76% 83% 82% 6% 7%<br />
Ireland 4,417 15.2% 87% 74% 69% 15% 18%<br />
Israel 2,075 4.7% 107% 100% 98% 8% 8%<br />
Italy 28,370 1.5% 79% 76% 75% 6% 8%<br />
Japan 83,411 15.2% 69% 67% 66% 2% 3%<br />
Luxembourg 1,012 27.2% 68% 67% 68% 5% 7%<br />
Malaysia 1,490 7.9% 82% 77% 71% 8% 12%<br />
Mexico 3,462 3.0% 73% 72% 72% 3% 4%<br />
Netherlands 12,729 21.4% 72% 67% 68% 5% 7%<br />
New Zealand 753 1.8% 67% 70% 69% 2% 4%<br />
Norway 2,606 5.2% 70% 70% 68% 4% 6%<br />
Poland 3,757 8.0% 78% 69% 67% 4% 6%<br />
Portugal 2,365 -0.4% 71% 68% 69% 3% 4%<br />
Puerto Rico 510 -9.2% 59% 59% 60% 4% 7%<br />
Romania 1,835 29.0% 47% 59% 60% 6% 11%<br />
Russia 2,718 -2.5% 58% 58% 54% 9% 18%<br />
S. Korea 18,976 24.4% 70% 71% 72% 4% 6%<br />
Saudi Arabia 1,356 46.1% 59% 54% 55% 6% 11%<br />
Singapore 731 12.7% 75% 84% 77% 10% 13%<br />
South Africa 4,857 29.1% 69% 70% 69% 4% 5%<br />
Spain 16,205 3.4% 73% 70% 76% 9% 12%<br />
Sweden 2,831 -0.5% 74% 78% 84% 11% 12%<br />
Switzerland 10,283 23.3% 59% 59% 62% 5% 7%<br />
Taiwan 1,521 -1.6% 59% 57% 58% 3% 4%<br />
Thailand 1,908 9.0% 57% 57% 56% 3% 5%<br />
Turkey 3,223 10.8% 76% 73% 70% 7% 11%<br />
U.A.E. 2,381 45.3% 70% 68% 67% 8% 10%<br />
U.K. 47,582 17.0% 81% 79% 79% 5% 6%<br />
U.S. 186,586 -0.4% 63% 62% 60% 5% 8%<br />
Ukraine 758 -20.5% 53% 44% 42% 8% 19%<br />
Venezuela 8,015 64.0% 53% 52% 51% 9% 16%<br />
Grand Total 648,939 22.0% 71% 69% 68% 5% 8%<br />
0% 50% 100%
Global Premium, Loss Ratio and Volatility: Property<br />
Country<br />
<strong>Aon</strong> Benfield<br />
Gross Written Premium Average Loss Ratio Graph<br />
Latest ($M)<br />
5 Yr Annual<br />
Growth<br />
1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR<br />
Argentina 1,113 11.9% 50% 47% 43% 8% 18%<br />
Australia 6,349 7.9% 75% 75% 66% 13% 20%<br />
Austria 3,213 5.3% 83% 75% 71% 11% 15%<br />
Belgium 3,172 7.6% 67% 57% 54% 7% 13%<br />
Brazil 4,955 19.5% 46% 47% 46% 16% 30%<br />
Canada 10,437 4.5% 66% 63% 62% 8% 13%<br />
Chile 993 11.8% 21% 41% 50% 19% 40%<br />
China 14,703 48.2% 59% 54% 53% 7% 14%<br />
Colombia 855 8.4% 30% 35% 34% 8% 23%<br />
Czech Republic 2,166 28.3% 51% 52% 49% 40% 62%<br />
Denmark 3,338 2.7% 75% 73% 72% 23% 29%<br />
Finland 1,264 5.0% 66% 70% 68% 8% 10%<br />
France 4,724 -25.5% 79% 70% 69% 17% 22%<br />
Germany 23,062 3.0% 70% 73% 70% 8% 11%<br />
Hong Kong 635 1.0% 44% 46% 42% 7% 19%<br />
Hungary 701 5.1% 18% 31% 32% 7% 21%<br />
India 1,103 3.2% 52% 43% 38% 14% 34%<br />
Indonesia 2,595 22.9% 39% 40% 47% 18% 33%<br />
Iran 1,285 35.0% 31% 26% 29% 6% 24%<br />
Ireland 2,958 16.8% 88% 65% 56% 16% 26%<br />
Israel 786 4.1% 62% 62% 65% 14% 22%<br />
Italy 6,984 1.6% 66% 61% 59% 5% 9%<br />
Japan 31,062 18.4% 37% 38% 45% 12% 27%<br />
Luxembourg 1,874 48.4% 74% 76% 61% 16% 27%<br />
Malaysia 1,021 5.9% 34% 23% 20% 16% 64%<br />
Mexico 2,911 7.9% 33% 52% 71% 33% 54%<br />
Netherlands 10,942 26.4% 60% 58% 55% 5% 9%<br />
New Zealand 1,031 2.8% 51% 57% 53% 7% 15%<br />
Norway 3,961 6.2% 45% 41% 38% 7% 17%<br />
Poland 1,251 8.8% 49% 46% 41% 6% 15%<br />
Portugal 1,063 3.0% 49% 48% 43% 8% 17%<br />
Puerto Rico 711 -2.1% 19% 21% 21% 9% 39%<br />
Romania 362 18.7% 14% 20% 19% 4% 22%<br />
Russia 9,420 21.9% 54% 40% 29% 15% 71%<br />
S. Korea 4,453 23.1% 54% 47% 43% 16% 31%<br />
Saudi Arabia 1,305 29.4% 27% 30% 26% 21% 69%<br />
Singapore 567 6.6% 42% 34% 31% 8% 23%<br />
South Africa 4,195 28.8% 59% 56% 56% 7% 12%<br />
Spain 10,037 10.3% 58% 57% 67% 14% 21%<br />
Sweden 3,768 2.3% 52% 53% 57% 5% 8%<br />
Switzerland 7,421 20.6% 48% 52% 54% 5% 9%<br />
Taiwan 977 -4.3% 47% 39% 40% 20% 48%<br />
Thailand 628 5.9% 39% 42% 43% 8% 22%<br />
Turkey 2,196 13.7% 34% 34% 35% 11% 25%<br />
U.A.E. 1,934 41.7% 54% 53% 52% 16% 31%<br />
U.K. 55,442 20.6% 57% 61% 59% 9% 14%<br />
U.S. 160,396 3.9% 57% 56% 62% 13% 20%<br />
Ukraine 907 -21.9% 5% 18% 10% 9% 106%<br />
Venezuela 783 16.0% 19% 23% 22% 57% 114%<br />
Grand Total 418,011 12.3% 56% 56% 58% 8% 13%<br />
0% 50% 100%<br />
23
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Global Premium, Loss Ratio and Volatility: Liability<br />
24<br />
Country<br />
Gross Written Premium Average Loss Ratio Graph<br />
Latest ($M)<br />
5 Yr Annual<br />
Growth<br />
1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR<br />
Argentina 1,959 27.7% 62% 60% 61% 3% 5%<br />
Australia 6,476 2.4% 72% 58% 52% 14% 31%<br />
Austria 2,060 10.9% 66% 60% 57% 5% 8%<br />
Belgium 4,111 9.7% 154% 105% 97% 22% 24%<br />
Brazil 1,625 18.1% 33% 35% 38% 9% 23%<br />
Canada 5,360 5.6% 53% 49% 50% 7% 12%<br />
Chile 433 17.3% 77% 66% 57% 15% 26%<br />
China 3,498 13.3% 56% 52% 47% 10% 21%<br />
Colombia 509 19.1% 28% 27% 28% 4% 15%<br />
Czech Republic 2,117 40.6% 36% 38% 43% 6% 13%<br />
Denmark 1,250 7.1% 57% 68% 76% 12% 15%<br />
Finland 1,160 2.9% 80% 82% 85% 8% 9%<br />
France 11,253 5.5% 57% 56% 56% 8% 13%<br />
Germany 16,116 3.8% 74% 69% 68% 6% 9%<br />
Hong Kong 1,001 3.8% 47% 51% 50% 16% 27%<br />
Hungary 187 3.1% 43% 40% 37% 6% 18%<br />
India 1,154 3.2% 34% 33% 35% 10% 25%<br />
Indonesia 515 30.2% 27% 27% 25% 9% 39%<br />
Iran 527 30.6% 54% 51% 55% 11% 23%<br />
Ireland 2,536 14.6% 62% 47% 55% 22% 31%<br />
Israel 418 -5.0% 98% 94% 89% 8% 9%<br />
Italy 8,739 10.6% 75% 73% 74% 6% 8%<br />
Japan 27,029 11.4% 33% 29% 28% 6% 26%<br />
Luxembourg 856 26.2% 96% 68% 54% 25% 50%<br />
Malaysia 324 7.7% 40% 28% 27% 19% 54%<br />
Mexico 1,040 5.6% 36% 30% 30% 8% 22%<br />
Netherlands 7,775 28.6% 55% 53% 56% 5% 8%<br />
New Zealand 245 9.9% 58% 48% 42% 9% 24%<br />
Norway 983 -11.5% 69% 47% 42% 12% 31%<br />
Poland 880 20.3% 30% 26% 27% 5% 18%<br />
Portugal 1,059 -3.1% 77% 73% 69% 15% 25%<br />
Puerto Rico 601 8.2% 43% 41% 43% 5% 10%<br />
Romania 93 5.7% 41% 47% 50% 13% 30%<br />
Russia 1,012 2.7% 28% 24% 21% 10% 80%<br />
S. Korea 41,722 34.7% 80% 80% 81% 27% 28%<br />
Saudi Arabia 283 39.6% 17% 25% 24% 13% 50%<br />
Singapore 510 -0.2% 38% 37% 44% 9% 18%<br />
South Africa 1,435 22.8% 50% 56% 58% 7% 11%<br />
Spain 7,640 5.6% 63% 55% 65% 12% 19%<br />
Sweden 287 7.3% 181% 236% 227% 66% 32%<br />
Switzerland 5,066 22.3% 47% 45% 44% 8% 16%<br />
Taiwan 271 -6.3% 57% 42% 46% 15% 29%<br />
Thailand 264 3.3% 35% 30% 40% 16% 40%<br />
Turkey 187 2.6% 19% 21% 20% 4% 23%<br />
U.A.E. 2,507 46.5% 32% 30% 40% 18% 33%<br />
U.K. 40,830 17.5% 58% 54% 55% 4% 8%<br />
U.S. 120,533 -0.4% 67% 66% 61% 9% 14%<br />
Ukraine 723 12.6% 44% 44% 28% 14% 65%<br />
Venezuela 1,553 57.1% 8% 9% 11% 16% 70%<br />
Grand Total 338,712 7.2% 64% 60% 59% 5% 9%<br />
0% 50% 100%
Afterword: The Greatest <strong>Risk</strong><br />
In last year’s conclusion we highlighted the<br />
significance of reserve risk to an insurance company’s<br />
balance sheet. In 2010, U.S. industry reserve levels<br />
appear strong. Companies continue to release reserves<br />
from prior accident years and we expect releases to<br />
continue for the next two years or so. Reserve risk is,<br />
however, a retrospective manifestation of prospective<br />
pricing risk, and pricing risk truly is the greatest risk<br />
facing insurance companies globally. Surprisingly,<br />
despite its severity, pricing risk is often poorly<br />
modeled, inadequately monitored, and insufficiently<br />
scrutinized, especially when compared to catastrophe<br />
risk. The factors provided in this <strong>Study</strong> are specifically<br />
designed to help quantify pricing risk. Working with<br />
<strong>Aon</strong> Benfield brokers, our Analytics team can help<br />
structure risk transfer solutions to manage pricing risk<br />
to appropriate levels.<br />
Pricing <strong>Risk</strong>: The Greatest <strong>Risk</strong><br />
In its annual study of insurance company impairments,<br />
A.M. Best identifies two symptoms of pricing risk,<br />
deficient loss reserves and excessive growth, as the<br />
primary cause in 52 percent of the 1,028 U.S.<br />
impairments it has tracked since 1969. By comparison,<br />
they identify only 7.6 percent of impairments as<br />
primarily caused by catastrophe losses. These statistics<br />
do not reflect the relative riskiness of pricing vs.<br />
catastrophe loss; instead they reflect the quality of<br />
models and the risk management practices underlying<br />
the two perils.<br />
Catastrophe modeling technology, while not perfect,<br />
represents a very successful application of science to<br />
the question of risk quantification. It has revolutionized<br />
underwriting, pricing and risk management practice.<br />
Its general acceptance within the industry has also<br />
revolutionized the provision of risk capacity: the flow of<br />
capital to bear catastrophe risk, whether through<br />
insurance-linked securities or more traditional solutions,<br />
is predicated in large degree on the agreed and<br />
objective view of risk provided by scientifically-based<br />
catastrophe models. The lack of analogous models and<br />
agreement for non-catastrophe property and casualty<br />
lines has many important ramifications.<br />
<strong>Aon</strong> Benfield<br />
<strong>Risk</strong> Management, Regulatory and Rating<br />
Agency Treatment of Pricing <strong>Risk</strong><br />
There is no more risky unit of premium than an<br />
under-priced unit. Paradoxically, the less volatile a line<br />
of business, the more true this statement becomes — to<br />
the extent that for many predictable lines of business<br />
pricing risk is the number one risk component.<br />
Internal, regulatory, and rating agency risk and capital<br />
models often equate risk with tail risk and attempt to<br />
model risk from a loss-only perspective. For catastrophe<br />
perils this paradigm has been very successful. Outside<br />
property catastrophe lines the approach is far less<br />
successful. Frequency and severity models of loss<br />
incorporating pricing risk, while common for internal<br />
models, rarely underlie regulatory or rating agency<br />
models, which tend to use simple factor-based<br />
approaches, with default factors by line and geography<br />
applied to premiums.<br />
An obvious problem with a factor-based approach to<br />
underwriting risk is that it does not use an objective<br />
measure of exposure: an inadequate (lower) premium<br />
generates a smaller risk charge in a model which simply<br />
applies a factor to premium! One important pricing<br />
adequacy statistic tracked in this <strong>Study</strong> is the ratio of<br />
net written premium to gross domestic product for the<br />
U.S., both in nominal dollars. A time series for the ratio<br />
back to 1970 is shown in the next chart. It clearly<br />
shows three market cycles in the last forty years: with<br />
turns after 1974, 1984 and 2001. It also shows that,<br />
relative to GDP as an exposure measure, premium<br />
levels today are at historically low levels, being below<br />
3.0 percent for the first time since 1970. In 2010, we<br />
reach a tipping point: will the industry repeat the<br />
excesses of the 1998-2000 soft market, or will it<br />
stabilize risk pricing, ushering in a period of barely<br />
adequate returns? Hoping for a severe catastrophe loss<br />
to solve the industry’s over-capacity is not a strategy.<br />
25
<strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong><br />
Industry NWP % of GDP<br />
4.5%<br />
4.0%<br />
3.5%<br />
3.0%<br />
2.5%<br />
1970 1975 1980 1985 1990 1995 2000 2005 2010<br />
Two expected outcomes of the financial crisis were a<br />
heightened respect for risk and a drive towards<br />
objective and consistent risk measures. <strong>Aon</strong> Benfield<br />
anticipated a move by regulators to supplement the<br />
trend towards internal capital models with stronger,<br />
exposure factor-based capital requirements. But in<br />
Europe, Solvency II has continued almost unchanged<br />
since the crisis, with no apparent pause caused by the<br />
failure of the ”certify-yourself” risk management<br />
processes used in banking. We have seen heightened<br />
respect for risk, but it has been driven from within<br />
companies rather than being imposed externally.<br />
Pricing Uncertainty and Reinsurance<br />
Reinsurance is not a solution to inadequate pricing. It<br />
can, however, be used to provide an effective hedge<br />
against pricing uncertainty. New products and<br />
emerging geographies generate important<br />
opportunities for needed revenue and income growth,<br />
but both also generate heightened levels of pricing<br />
uncertainty. Reinsurance can be used to effectively<br />
26<br />
3.0% 3.0%<br />
3.0% 2.9%<br />
manage this pricing uncertainty and to facilitate<br />
underlying growth. Reinsurance provides objective,<br />
third-party validation of underlying rate levels and<br />
policy forms — backed up by an effective financial<br />
guarantee. It allows a more aggressive approach to<br />
growth and expansion, enabling the innovation and<br />
experimentation companies need to prosper in today’s<br />
hyper-competitive global market.<br />
Recently, <strong>Aon</strong> Benfield has been working with clients<br />
globally to understand what primary policy coverage<br />
enhancements and product differentiation could be<br />
used to stop, and potentially reverse, the erosion of<br />
primary rate levels. Many of these enhancements can<br />
be safely and effectively reinsured by an eager<br />
reinsurance market suffering the same overcapitalization<br />
and top line erosion as primary<br />
companies. We believe we are unique in advocating<br />
growth through reinsurance-backed innovation, and<br />
recommend that you contact your local <strong>Aon</strong> Benfield<br />
broker to learn more about the exciting prospects of<br />
this strategy.
For more information on the <strong>Insurance</strong> <strong>Risk</strong> <strong>Study</strong>, ReMetrica ® , S2Metrica SM Solvency II<br />
Model or our analytic capabilities, please contact your local <strong>Aon</strong> Benfield broker or:<br />
Stephen Mildenhall<br />
Chief Executive Officer, <strong>Aon</strong> Benfield Analytics<br />
+1 312 381 5880<br />
stephen.mildenhall@aonbenfield.com<br />
Parr Schoolman<br />
<strong>Risk</strong> & Capital Strategy<br />
+1 312 381 5330<br />
parr.schoolman@aonbenfield.com<br />
Michael McClane<br />
ReMetrica, U.S.<br />
+1 215 751 1596<br />
michael.mcclane@aonbenfield.com<br />
Americas<br />
Brian Alvers<br />
+1 312 381 5355<br />
brian.alvers@aonbenfield.com<br />
EMEA & U.K.<br />
Marc Beckers<br />
+44 (0)20 7086 0394<br />
marc.beckers@aonbenfield.com<br />
Paul Kaye<br />
+44 (0)20 7522 3810<br />
paul.kaye@aonbenfield.com<br />
About <strong>Aon</strong> Benfield<br />
John Moore<br />
Head of Analytics, International<br />
+ 44 (0)20 7522 3973<br />
john.moore@aonbenfield.com<br />
Paul Maitland<br />
ReMetrica, International<br />
+44 (0)20 7522 3932<br />
paul.maitland@aonbenfield.com<br />
Asia Pacific<br />
Will Gardner<br />
+61 2 9650 0390<br />
will.gardner@aonbenfield.com<br />
David Maneval<br />
+61 2 9650 0395<br />
david.maneval@aonbenfield.com<br />
George Attard<br />
+65 6239 8739<br />
george.attard@aonbenfield.com<br />
<strong>Aon</strong> Benfield<br />
As the industry leader in treaty, facultative and capital markets, <strong>Aon</strong> Benfield is redefining the role of the reinsurance intermediary<br />
and capital advisor. Through our unmatched talent and industry-leading proprietary tools and products, we help our clients to<br />
redefine themselves and their success. <strong>Aon</strong> Benfield offers unbiased capital advice and customized access to more reinsurance and<br />
capital markets than anyone else. As a trusted advocate, we provide local reach to the world’s markets, an unparalleled investment<br />
in innovative analytics, including catastrophe management, actuarial, and rating agency advisory, and the right professionals<br />
to advise clients in making the optimal capital choice for their business. With an international network of more than 4,000<br />
professionals in 50 countries, our worldwide client base is able to access the broadest portfolio of integrated capital solutions and<br />
services. Learn more at aonbenfield.com.<br />
Sources: A.M. Best, ANIA (Italy), Association of Vietnam Insurers, Axco <strong>Insurance</strong> Information Services, BaFin (Germany), Banco Central del Uruguay, Bank Negara<br />
Malaysia , Bloomberg, Bureau of Economic Analysis (U.S.), Bureau of Labor Statistics (U.S.), CADOAR (Dominican Republic), Cámara de Aseguradores de Venezuela,<br />
Comisión Nacional de Bancos y Seguros de Honduras, Comisión Nacional de Seguros y Fianzas (Mexico), Danish FSA, Dirección General de Seguros (Spain),<br />
DNB (Denmark), E&Y Annual Statements (Israel), Finma (Switzerland), FMA (Austria), FSA Returns (U.K.), “Handbook on Indian <strong>Insurance</strong> Statistics” (ed. IDRA),<br />
HKOCI (Hong Kong), http://www.bapepam.go.id/perasuransian/index.htm (Indonesia), ICA (Australia), Korea Financial Supervisory Service, Monetary Authority<br />
of Singapore, MSA Research Inc. (Canada), Quest Data Report (South Africa), Romanian <strong>Insurance</strong> Association, SNL (U.S.), “The Statistics of Japanese Non-Life<br />
<strong>Insurance</strong> Business” (ed. <strong>Insurance</strong> Research Institute), Superintendencia de Banca y Seguros (Peru), Superintendencia de Bancos y Otras Instituciones Financieras<br />
de Nicaragua, Superintendencia de Bancos y Seguros (Ecuador), Superintendencia de Pensiones de El Salvador, Superintendencia de Pensiones, Valores y Seguros<br />
(Bolivia), Superintendencia de Seguros de la Nación (Argentina), Superintendencia de Seguros Privados (Brazil), Superintendencia de Seguros y Reaseguros<br />
de Panama, Superintendencia de Valores y Seguros de Chile, Superintendencia Financiera de Colombia, Taiwan <strong>Insurance</strong> Institution, Turkish <strong>Insurance</strong> and<br />
Reinsurance Companies Association, Yahoo! Finance, Yearbooks of China’s <strong>Insurance</strong>, and annual financial statements.<br />
27
200 E. Randolph Street, Chicago, Illinois 60601<br />
t: +1 312 381 5300 | f: +1 312 381 0160 | aonbenfield.com<br />
Copyright <strong>Aon</strong> Benfield Inc. 2010 | #4561 - 08/2010