Insurance Risk Study
Sixth Edition 2011
Contents
3
F
oreword
16 Correlation and the Pricing Cycle
4
G
lobal Risk Parameters
18 M
acroeconomic Correlation
6
U.S. Risk Parameters
19 Global Market Review
7
U
.S. Reserves
20 G
lobal Statistics: Motor
10 Innovations in Crop Insurance Modeling
21 G
lobal Statistics: Property
12 An Optimal Insurer in a Solvency II World
22 G
lobal Statistics: Liability
14 Innovations in Hedging
23 A
fterword: The Good Risk
About the Study
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 account volume, but they cannot reduce their volatility to zero. A certain
level of systemic insurance risk will always remain, due to factors such as the underwriting cycle, macroeconomic trends, 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.
Asset Portfolio Risk
Insurance Portfolio Risk
Portfolio Risk
Insurance Risk
Portfolio Risk
Systemic
Insurance
Risk
Systemic
Market Risk
Naïve Model
Number of Stocks
Volume
Foreword
Even before the start of the U.S. hurricane season in June, we had already seen a series of significant natural
catastrophe events which made 2011 a challenging insurance year: severe flooding in Australia, a 9.0-magnitude
earthquake and subsequent tsunami in Japan, two earthquakes in New Zealand, as well as severe weather losses in
the U.S., which rivaled the insured losses of moderate-sized hurricanes.
As a result of the “frequency of severity” of catastrophe losses, modeling technology for natural catastrophes
continues to advance very quickly. In contrast, modeling non-cat losses has not received nearly as much attention,
despite being a greater net exposure to the industry. Therefore, we present this sixth edition of Aon Benfield’s
Insurance Risk Study, which continues to highlight this important but somewhat neglected risk.
This Study marks a cornerstone of Aon Benfield Analytics’ integrated and comprehensive risk modeling and risk
assessment capabilities.
> Our reinsurance optimization framework, linking reinsurance to capital, volatility and valuation, 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.
The massive database underlying the Study is supported by more than 400 local professionals within Aon Benfield’s
global analytics team. Our team is available to work with you to customize the basic parameters reported in the Study
to answer your specific, pressing business questions.
As part of our continued efforts in innovation and improvement, we have included in this edition two recent
examples of Aon Benfield Analytics’ new technologies and modeling techniques to advance our understanding of
risk. The first example is our ACReS model — an application of detailed weather forecasts to project crop yields for use
in global agricultural insurance products. The second example is PathWise®, a family of products for hedging variable
annuity business that incorporates cutting-edge graphical processor technology.
The Aon Benfield Insurance Risk Study continues to be the industry’s leading set of risk parameters for modeling
and benchmarking underwriting risk. It is part of a suite of capabilities that help position Aon Benfield as the
leading advisor on growth and risk management in the global insurance business. 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
Insurance Risk Study
Global Risk Parameters
The 2011 Insurance Risk Study quantifies the systemic risk
by line for 47 countries worldwide, representing more
than 90 percent of global property casualty premium.
Systemic risk in the Study is the coefficient of variation of
loss ratio for a large book of business. Coefficient of
variation (CV) is a commonly used normalized measure
of risk defined as the standard deviation divided by the
mean. Systemic risk typically comes from nondiversifiable risk sources such as changing market rate
adequacy, unknown prospective frequency and severity
trends, weather-related losses, legal reforms and court
decisions, the level of economic activity and other
macroeconomic factors. It also includes the risk to
smaller and specialty lines of business caused by a lack of
credible data. For many lines of business systemic risk is
the major component of underwriting volatility.
The systemic risk factors for major lines by region appear
on the next page. Detailed charts comparing motor and
property risk by country appear below. The factors
measure the volatility of gross loss ratios. If gross loss
ratios are not available the net loss ratio is used.
Coefficient of Variation of Gross Loss Ratio by Country
Property
Motor
Hungary
Japan
Taiwan
South Korea
Israel
Austria
Turkey
Australia
France
Czech Republic
Switzerland
Uruguay
Mexico
Bolivia
Spain
India
Germany
Italy
Chile
Brazil
Netherlands
Venezuela
South Africa
Pakistan
China
Dominican Republic
Malaysia
Argentina
Vietnam
Poland
Colombia
U.S.
U.K.
El Salvador
Canada
Peru
Honduras
Ecuador
Denmark
Singapore
Romania
Indonesia
Slovakia
Panama
Hong Kong
Greece
Nicaragua
4%
5%
6%
7%
7%
7%
8%
8%
8%
8%
8%
9%
9%
9%
9%
10%
10%
11%
11%
12%
12%
12%
13%
13%
14%
14%
15%
15%
15%
15%
15%
16%
18%
18%
18%
19%
20%
21%
23%
24%
25%
28%
30%
32%
43%
46%
58%
Americas
4
Israel
Denmark
South Africa
Australia
Italy
Netherlands
Austria
Spain
Switzerland
Germany
U.K.
Canada
Turkey
Chile
Malaysia
Japan
Panama
China
India
France
Venezuela
Bolivia
El Salvador
Slovakia
Uruguay
Hungary
South Korea
Poland
Ecuador
Vietnam
U.S.
Argentina
Pakistan
Romania
Honduras
Colombia
Dominican Republic
Nicaragua
Indonesia
Hong Kong
Singapore
Greece
Brazil
Mexico
Peru
Taiwan
Asia Pacific
8%
13%
14%
16%
16%
17%
18%
18%
18%
19%
20%
23%
25%
26%
27%
28%
29%
30%
31%
32%
35%
38%
39%
40%
40%
40%
42%
42%
42%
42%
43%
45%
51%
55%
55%
56%
57%
58%
64%
65%
69%
69%
70%
92%
96%
96%
Europe, Middle East & Africa
Aon Benfield
Fidelity
& Surety
Workers
Compensation
Credit
Marine, Aviation
& Transit
General
Liability
Property –
Commercial
10%
Property –
Personal
40%
15%
Property
53%
38%
Motor –
Commercial
45%
9%
Motor –
Personal
15%
Motor
Accident
& Health
Underwriting Volatility for Major Lines by Country, Coefficient of Variation of Loss Ratio for Each Line
Americas
Argentina
Bolivia
176%
Brazil
12%
70%
48%
70%
50%
42%
50%
62%
Canada
18%
23%
20%
35%
39%
41%
47%
90%
Chile
11%
26%
41%
44%
23%
Colombia
15%
56%
31%
15%
76%
Dominican Republic
14%
57%
92%
64%
184%
117%
66%
Ecuador
21%
42%
52%
El Salvador
18%
39%
17%
96%
Honduras
20%
55%
5%
200%
Mexico
9%
92%
Nicaragua
58%
58%
80%
Panama
32%
29%
21%
Peru
19%
96%
9%
40%
Uruguay
U.S.
16%
Venezuela
12%
14%
24%
43%
65%
48%
34%
43%
149%
118%
64%
8%
21%
37%
53%
40%
35%
82%
27%
75%
69%
21%
159%
Asia Pacific
Australia
16%
23%
32%
54%
China
14%
8%
14%
30%
73%
23%
28%
20%
10%
16%
Hong Kong
43%
43%
65%
85%
23%
60%
30%
116%
81%
India
10%
12%
31%
14%
31%
Indonesia
28%
28%
64%
126%
55%
68%
5%
28%
11%
9%
17%
6%
Malaysia
15%
27%
119%
30%
36%
89%
Pakistan
13%
51%
Singapore
24%
Japan
South Korea
7%
Taiwan
6%
6%
Vietnam
15%
94%
36%
69%
7%
73%
47%
42%
33%
57%
42%
55%
96%
47%
26%
68%
42%
38%
11%
30%
20%
12%
20%
64%
Europe, Middle East & Africa
Austria
7%
Czech Republic
8%
Denmark
France
18%
12%
51%
23%
13%
15%
17%
15%
16%
33%
8%
32%
35%
26%
22%
25%
57%
20%
31%
29%
22%
22%
82%
84%
50%
24%
Germany
10%
19%
Greece
46%
69%
Hungary
4%
40%
Israel
7%
8%
53%
Italy
11%
16%
24%
13%
46%
38%
Netherlands
12%
17%
25%
58%
44%
32%
Poland
15%
42%
Romania
25%
55%
Slovakia
30%
40%
South Africa
13%
63%
33%
47%
31%
14%
32%
47%
21%
11%
50%
75%
24%
32%
21%
81%
80%
24%
28%
5%
Spain
9%
Switzerland
8%
Turkey
U.K.
14%
9%
10%
23%
18%
8%
18%
18%
18%
8%
25%
19%
20%
20%
44%
69%
134%
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.
5
Insurance Risk Study
U.S. Risk Parameters
The U.S. portion of the Insurance Risk Study uses data from ten years of NAIC annual statements for 2,308 individual
groups and companies. The database covers all 22 Schedule P lines of business and contains 1.5 million records of
individual company observations from accident years 1992 – 2010.
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 | 1992-2010
All Risk
Private Passenger Auto
Auto Physical Damage
Commercial Auto
No Underwriting Cycle Risk
14%
Private Passenger Auto
13%
Auto Physical Damage
15%
16%
24%
Workers Compensation
27%
Warranty
29%
Commercial Auto
18%
Workers Compensation
18%
Warranty
Medical PL - Occurrence
33%
Medical PL - Occurrence
Commercial Multi Peril
34%
Commercial Multi Peril
Other Liability - Occurrence
37%
Other Liability - Occurrence
Special Liability
40%
Special Liability
Other Liability - Claims-Made
41%
Other Liability - Claims-Made
Medical PL - Claims-Made
42%
31%
32%
27%
25%
30%
27%
Medical PL - Claims-Made
30%
Products Liability - Occurrence
47%
Products Liability - Occurrence
33%
Homeowners
48%
Homeowners
Other
53%
Other
50%
Reinsurance - Liability
67%
Reinsurance - Liability
Fidelity and Surety
69%
Fidelity and Surety
International
70%
International
55%
Reinsurance - Property
55%
Reinsurance - Property
Reinsurance - Financial
85%
94%
Products Liability - Claims-Made
101%
Special Property
102%
Financial Guaranty
Reinsurance - Financial
Products Liability - Claims-Made
Special Property
154%
The U.S. Underwriting Cycle
The underwriting cycle acts simultaneously across
many lines of business, driving dependencies 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 claims-made increases by
54 percent and commercial auto liability by 39 percent.
Personal lines are more formula rated and thus show a
much lower cycle effect, with private passenger auto
volatility only increasing by seven percent because of
the cycle.
6
41%
45%
53%
59%
48%
60%
103%
Financial Guaranty
Impact of Pricing Cycle
Line
Impact of
Pricing Cycle
Other Liability — Claims-Made
54%
Reinsurance — Liability
50%
Workers Compensation
47%
Other Liability — Occurrence
46%
Medical PL — Claims-Made
39%
Commercial Auto
39%
Special Liability
31%
Commercial Multi Peril
23%
Homeowners
18%
Private Passenger Auto
7%
Aon Benfield
U.S. Reserves
Industry Reserve Adequacy:
The Party Continues
In 2010 the U.S. P&C industry enjoyed its fifth
consecutive year of favorable reserve development.
Despite several instances of individual companies taking
adverse development, the industry has released a total
of USD46 billion of reserves since 2006. This begs the
question: How much longer can this favorable
development continue?
Industry Reserve Development
25.0B
Favorable / (Adverse)
Reserve Development
20.0B
15.0B
10.0B
5.0B
On a comparative basis, this is exactly what we find:
applying standard actuarial reserving methods to the
2010 data indicates a redundancy of USD13.9 billion.
However, AIG, which represents 10 percent of total
U.S. statutory reserves, published additional
disclosures outlining adjustments to their statutory
Schedule P triangles in their 2010 combined annual
statement. These adjustments cover large portfolio
transfers, reinsurance commutations and additional
line of business splits, all of which can cause material
distortions to mechanically generated indications.
To incorporate the effect of these disclosures on the
industry reserve position, we separately analyzed the
industry excluding AIG and the disclosure-adjusted
AIG triangles to derive a total industry view.
After making these adjustments, the results indicate
that there are approximately USD22 billion of excess
reserves across all lines of business, leaving our
estimate of the industry position essentially
unchanged from last year.
0.0B
-0.0B
-10.0B
-15.0B
-20.0B
-25.0B
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Last year we estimated industry reserve redundancies
of USD21.9 billion. During the year, the industry
released USD10.5 billion, so it is natural to expect
deterioration in the overall redundancy.
The casualty market is unlikely to harden again as long
as the industry has more than adequate reserves. We
estimate that reserve redundancies will be depleted in
two to three years if reserve releases continue at the
pace of 2007 to 2010.
A summary of adequacy by major market segments appears below.
U.S. Reserve Estimated Adequacy (USD Billions)
Line
Personal Lines
Estimated
Reserves
Booked
Reserves
127.1
133.6
Remaining
Redundancy
Favorable / (Adverse) Development
2007
2008
2009
2010
Average
6.5
5.9
5.4
5.8
6.7
5.9
Years at
Run Rate
1.1
Commercial Property
40.5
41.9
1.5
1.7
2.6
2.4
2.7
2.3
0.6
Commercial Liability
227.0
236.8
9.9
1.0
5.2
3.8
2.4
3.1
3.2
n/a
Workers Compensation
111.2
117.7
6.5
1.0
1.1
(0.5)
(1.6)
0.0
Total Excl Financial Guaranty
505.7
530.1
24.4
9.5
14.4
11.5
10.1
11.4
2.1
32.6
30.2
(2.4)
(1.2)
(12.6)
7.0
0.4
(1.6)
n/a
538.3
560.2
22.0
8.3
18.6
10.5
9.8
2.2
Financial Guaranty
Total
1.7
The 2010 release of USD10.5 billion was at the 36th percentile of the estimated range of one-year outcomes.
The range is based on a Monte Carlo simulation for accident years 2009 and prior, calibrated to the December 31,
2009 statements. The 90th percentile range for 2010 emergence was from USD22 billion favorable to USD9 billion
adverse emergence.
See page 26 for a link to the Reserve Study and the Reserve Study Disclosure regarding compensation paid to Aon Benfield.
7
Insurance Risk Study
Reserve Risk: The Silent Killer
Largest U.S. P&C Industry Events (USD Billions)
Despite the U.S. P&C industry’s recent favorable
development and a seemingly redundant reserve
position, reserve risk remains one of the largest threats
to insurer solvency. According to A.M. Best’s 2011
Impairment Study, 54 percent of insurer impairments
from 1969 – 2010 can be attributed to loss reserve
deficiencies and rapid growth or inadequate pricing.
Individual accident year development accounts for
four of the ten largest U.S. P&C industry events.
Combined reserve development on the soft market
accident years 1998 – 2001 amounts to USD64 billion:
55 percent more than Katrina, the U.S. industry’s
largest natural catastrophe.
The ultimate loss estimate associated with asbestos and
environmental claims accounts for the largest U.S. P&C
industry loss event at USD117 billion. These losses
manifested themselves almost entirely in the form of
unforeseen adverse reserve development — suggesting
the potential volatility on initial reserve estimates.
Event
Nominal Loss
Asbestos & Environmental
117.0
Hurricane Katrina
41.1
AY 2000 Development*
22.0
AY 1999 Development*
19.3
September 11th
18.8
Hurricane Andrew
15.5
Northridge Earthquake
12.5
Hurricane Ike
12.5
AY 1998 Development*
12.1
AY 2001 Development*
10.5
* 10 years of development from Schedule P.
Companies looking to benchmark their internal reserve volatility estimates against industry based volatility estimates
should be cognizant of how size affects volatility. To illustrate this point, we have applied traditional stochastic
reserve methods to the paid loss triangles of more than 450 U.S. P&C insurers. As shown in the graph below, the one
year CV of private passenger auto reserves for large companies — those with at least USD500 million of carried
reserves — is 3.4 percent. The average one year CV of reserves of small companies — those with USD10 million to
USD100 million of carried reserves — is 10.2 percent. The difference is 300 percent.
Private Passenger Auto | One Year Net Reserve CV by Carried Reserve Size
25%
Small (USD10M - USD100M); 10.2%
Med (USD100M - USD500M); 6.4%
One Year Reserve CV
20%
Large (>USD500M); 3.4%
15%
10%
5%
0%
0
50
500
5,000
Carried Reserves (log scale) USD Millions
The relationship between size and reserve volatility holds across all lines of business and is consistent with the results
of our loss ratio volatility parameterization.
8
Aon Benfield
U.S. Reserve Volatility by Line, by Carried Reserve Size
One Year Reserve CV
Line
All Lines
Homeowners
Private Passenger Auto
Commercial Auto
Commercial Multi Peril
Workers Compensation
Medical PL — CM
Other Liability — CM
Other Liability — Occ
Products Liability — Occ
Small
USD10M –
USD100M
Medium
USD100M –
USD500M
Ultimate Reserve CV
Large
> USD500M
Small
USD10M –
USD100M
Medium
USD100M –
USD500M
Large
> USD500M
10.4%
8.4%
5.5%
13.4%
10.7%
7.3%
14.5%
12.2%
10.5%
16.9%
13.7%
12.2%
10.2%
6.4%
3.4%
12.8%
7.8%
4.1%
12.3%
7.0%
4.8%
16.0%
9.5%
6.5%
12.8%
10.0%
6.9%
17.0%
13.7%
9.1%
7.5%
4.8%
4.0%
10.1%
6.5%
6.4%
14.4%
13.7%
11.6%
17.6%
16.8%
14.2%
14.2%
13.2%
15.1%
17.4%
16.0%
19.2%
14.9%
11.6%
8.2%
18.6%
15.7%
12.2%
17.9%
11.2%
7.5%
25.0%
16.5%
18.8%
Ultimate reserve CV calculated using average of Mack and Over Dispersed Poisson (ODP) Bootstrap methods applied to paid loss triangles by line. One-year reserve
CV uses average of the Merz-Wuthrich and ODP Bootstrap methods. All methods adjusted to account for fail factor volatility and reserves more than 10 years old.
The table above shows the average measured reserve CVs for insurers with carried reserves within the ranges of
USD10 million to USD100 million, USD100 million to USD500 million and more than USD500 million, roughly
corresponding with small, medium and large carriers in each line respectively. CVs are shown on both a one year
basis, representing the volatility of reserves over the next calendar year, and an ultimate basis, representing the
volatility of reserve from their current balance until their full settlement. The all lines reserve CVs are estimated using
a single paid loss triangle across all lines of business.
To contextualize the USD64 billion industry reserve
development on accident years 1998-2001, we can
estimate the chance of another reserve event of this
magnitude. The table to the right summarizes the
implied ultimate reserve risk distribution for the
industry. This distribution is based on the P&C
industry reserve balance as of December 31, 2010
and assumes that reserves are distributed lognormally
with a CV equal to the all lines large company reserve
CV of 7.3 percent.
On an ultimate basis, adverse development of USD64
billion is a 1 in 15 year event, corresponding roughly to
one occurrence each underwriting cycle. These results
highlight the importance of developing better processes
to measure and manage reserve risk.
U.S. P&C Industry Reserve Risk Distribution
All Lines, All Years
Ultimate Development
Return Period
%
USD Billions
500
23.2%
129.9
250
21.1%
118.6
100
18.3%
102.5
50
15.9%
89.4
25
13.4%
75.1
10
9.6%
53.6
5
6.1%
34.1
Assumptions: Large (>USD500M) All Lines CV with LogNormal Distribution.
9
Insurance Risk Study
Innovations in Crop Insurance Modeling
World population growth will continue to increase
demand for food, energy and clothing. With emerging
middle classes in Asia and elsewhere, consumers are
increasing their per capita consumption of staple foods
and also consuming larger quantities of meat, dairy
products and vegetable oils. These factors contribute
to amplified demand for aggregate grains and oilseeds.
Meanwhile, U.S. ethanol policy has increased the
demand for corn.
Crop Insurance Premium, USD Billions
12
U.S.
China
10
8
6
4
2
All these factors point toward a future with higher crop
prices and higher volatility. Data from the Chicago
Mercantile Exchange shows that prices for staple crops
have more than doubled in the last 10 years. The
volatilities implied by options prices spiked in 2008 and
have remained significantly above historical levels.
Commodity Prices
4.0
Corn
Soybeans
3.5
Wheat
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011,
Est.
U.S. Focus
The U.S. crop insurance program is the largest in the
world with USD110 billion in insured value. Driven by the
rise in commodities prices, premiums for 2011 are
expected to be more than triple the levels of 10 years
ago. No other U.S. insurance product matches this
premium growth during the last decade.
3.0
2.5
2.0
1.5
1.0
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Base Year 1987 = 1.00
Implied Volatility for Crop Commodities
50.0
All of these factors mean that relying on historical
performance alone to guide risk management decisions is
no longer a viable option. To maximize profitability and
manage risk, insurers must understand the risk-reward
relationship of each underwritten policy.
Corn
Soybeans
45.0
Wheat
40.0
35.0
30.0
25.0
20.0
15.0
10.0
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
Crop insurance is growing rapidly worldwide, both due
to recognition of the aforementioned risks and due to
additional stimulus from government subsidies.
10
Approved insurance providers now face a wide variety of
risks through their participation in multi-peril crop
insurance policies. The Risk Management Agency of the
U.S. Department of Agriculture has significantly changed
its rating and underwriting over time, and the new
Standard Reinsurance Agreement threatens future returns.
Moreover, the popularity of revenue-based products
exposes insurers to substantial commodity price risk at a
time when price volatilities are at historically high levels.
Aon Benfield Crop Reinsurance Solution (ACReS) provides
innovative risk assessment tools for managing the
agricultural exposures of today’s crop programs. ACReS is
designed to provide clients with cutting-edge unit level
risk analytics within a scalable and customizable platform.
ACReS contains two probabilistic models designed to
simulate many crop seasons for each insured unit.
Premium, loss and underwriting gain are calculated for
each simulated crop season.
Aon Benfield
ACReS Advantages
> Provides a comprehensive reflection of current risk,
including the current products, premium rates,
underwriting methods, unit structure, crop mix and
market conditions.
> Combines econometric relationships with futures
market expectations, maintaining the supply and
demand relationships which cause yields and prices
to be correlated.
> Models risk at the unit level, recognizing differences
between individual crop producers. This is also the
level at which fund allocation decisions are made.
> Provides a long-term view on risk using 115 years
of weather data, giving appropriate context for
abnormal events.
> Allows for accurate forecasting of insured acreage
and premium. Many risk management decisions
including fund allocation must be made before the
crop is planted, when key information such as insured
acreage and premium is not available.
Beginning in spring 2011, Aon Benfield has partnered
with Planalytics®, the business weather intelligence firm,
to more accurately forecast weather and yields for the
next growing season. We can now provide a forward
looking fund allocation analysis using Planalytics’s
forecasting capabilities, in addition to the standard
analysis giving equal weight to all historical years.
An ACReS model has also been developed for China and
it represents a significant step forward in the technology
available for risk assessment in this rapidly emerging
market. There are several key features that distinguish
the ACReS model from currently applied risk assessment
methodology in China.
> Risk is modeled at the county level, using yield data
for each of China’s 2,876 counties. This granular data
significantly improves the accuracy of the model’s
results compared to currently applied methods.
> The ACReS model generates results based on
the current policy terms and conditions, yield
productivity levels, premium rates, underwriting
methods, crop types insured and loss adjustment
practices. An analysis based on historical data alone
will not capture material changes in these variables.
> Explicit consideration is given to the risk of low
frequency events. For portfolios that have already
experienced a major loss, the model will place this loss
into appropriate perspective. For newly established
portfolios, appropriate treatment of disaster events will
improve accuracy and bring stability to premium rates,
coverage and reinsurance arrangements.
Sample of ACReS China Risk Mappings —
Heilongjiang Province
Crop Model Simulation Results
Most Risky
Risky
Neutral
Less Risky
Least Risky
China Focus
China’s agricultural insurance market has experienced
rapid growth as a result of increased government
subsidy and program improvements, with nationwide
premiums increasing dramatically from USD100 million
in 2006 to USD2 billion in 2010. It has grown to become
the second largest crop insurance market in the world.
Currently, only 30 percent of the total value of
production is insured, suggesting that China’s program
could see considerable growth for years to come.
Insurers and reinsurers face significant challenges in
evaluating the risk associated with these insurance
programs. In conjunction with their rapid expansion,
insurance is being offered in additional locations and
the coverage provided is continually changing.
Furthermore, insurers have limited historical
underwriting results to draw on for ratemaking.
Juisan
Hegang
Jixi
Harbin
11
Insurance Risk Study
An Optimal Insurer in a Solvency II World
Solvency II (SII) is changing the way in which regulatory
capital is assessed for European insurers. The results of
the latest impact assessment study, QIS 5, suggest that
the average solvency ratio for non-life European insurers
will drop from over 200 percent to 165 percent.
Additionally, unlike the existing Solvency I regime, SII
uses a risk-based approach to set the level of each
insurer’s solvency capital — thus requiring more capital
to be held for riskier insurance and investment activities.
This means that insurers who take a higher level of risk,
as measured by SII, will suffer a far greater fall in
solvency ratio than those with less risky portfolios
(whose solvency ratio may even improve).
Despite presenting clear challenges, SII also offers
insurers the opportunity to improve their business
strategy by better allocating risk and capital to target
opportunities that provide the highest return per unit of
risk. SII encourages firms to view risk, capital and value
from an enterprise-wide perspective.
At Aon Benfield, we recognize that insurers must set
strategy in accordance with two sets of constraints
simultaneously: the capital constraints imposed by
regulators, and the economic constraints imposed by
stakeholders, including shareholders, policyholders and
management. To maximize performance, insurers must
pursue a combined strategy for both sides of the
balance sheet — a strategy that comprehends the
potential dependence between insurance and asset
risk behavior.
To date, very few organizations have optimized their
allocation of risk and capital using a framework that
captures these important dependencies. In practice,
assets and liabilities have been managed by several
business units, without a full understanding of the
impact on enterprise level risk and capital. For example,
credit insurance losses are highly correlated with
economic risks; to set asset strategy without considering
the impact on insurance risks may result in a strategy
that increases overall risk to the firm.
Aon Benfield has developed an optimization process
for setting consistent strategy across asset and liability
risks, recognizing all relevant economic and capital
constraints. We believe this process will support insurers
to better manage their risk and capital under SII. A brief
description of the process follows, with sample exhibits
for a hypothetical insurer.
12
1) Risk Tolerance, Capital Target
& Drivers of Value
The binding capital metric for many insurers will
be the Solvency II capital requirement (SCR) under
the Standard Formula. We measure the capital
utilization of insurance and asset risks by their
contribution to the overall SCR.
Insurance company management must set the
company’s overall risk appetite, target capital and
return levels. Risk appetite is often set to maximize
shareholder value. Aon Benfield’s price-to-book
regression study (see page 25) points to a volatility
measure of risk as best capturing investor risk
tolerances. For example, the insurer may select an
overall risk tolerance of 10 percent volatility of
surplus and a 165 percent SII ratio as the long term
capital target.
2) Identify Optimal Allocation
of Insurance Risk
Shareholders of non-life insurers normally desire
firms with a carefully selected portfolio of insurance
risks and an asset strategy that supports their
liabilities and enhances their risk-adjusted return.
Therefore, when optimizing the strategy of an insurer,
the first stage is to optimize the insurance portfolio.
An insurer will determine upper and lower bounds
for premium by class of business, creating a range
of possible portfolios for a given premium volume
(see table below).
Sample of Premium Allocation by Line
Allocation
LOB
Initial
Min
Max
Motor, Vehicle Lliability
33.2%
28.0%
38.0%
Motor, Other Classes
18.0%
15.5%
21.0%
Marine, Aviation & Transport
3.7%
2.5%
4.5%
Property
30.1%
25.5%
34.5%
General Liability
11.5%
8.5%
14.5%
Credit And Suretyship
3.5%
2.5%
4.5%
Aon Benfield
We create an internal model of the insurer and,
using our proprietary optimization framework,
determine the economic and capital efficient
frontiers of insurance portfolios — the portfolios
that provide the maximum expected profit for a
specified level of economic volatility or SII capital
utilization, respectively.
> The asset strategy that is numerically optimal may
still lack desirable qualitative features. We can
refine our optimal asset portfolio with qualitative
constraints, such as a minimum allocation to
cash equivalents for liquidity purposes and a
maximum permissible asset/liability mismatch at
key rate durations.
The SII capital efficient frontier differs from the
economic frontier, and capital allocations that are
efficient under the proposed Standard Formula can
be suboptimal from an economic perspective. This is
because the proposed SII Standard Formula assesses
capital based on prescribed volatilities and correlations
for non-cat underwriting risk and prescribed events
for natural and man-made catastrophes — these
prescribed factors are not based on economic best
estimates and are often conservative.
For a model insurer based on the average non-life
company in Europe, the overall financial and
economic impact of the balance sheet optimization is
an increase to expected profit of EUR1.64 million on a
EUR100 million premium base, an improvement of
shareholder return from 13.27 to 14.70 percent and
no increase in volatility or required capital under SII.
Economic and Capital Efficient Frontiers
4.1
Economic
3.9
Having selected the optimal insurance portfolio, we
now optimize the asset strategy within the remaining
risk and capital budget for the firm. Since the insurer
already has a target SCR level and we know the
contribution from the optimal insurance portfolio,
we can infer the contribution to the SCR from market
risk. We then determine a constrained efficient
frontier of asset portfolios with the required market
risk capital at this level. The optimal asset portfolio
will then be the one on the efficient frontier that
achieves our overall target surplus volatility.
Several factors are important to consider at this stage:
> The asset strategy optimization must be
performed in the context of the overall balance
sheet, so that we capture correlation among
economic liabilities and the interaction of liability
uncertainty with economic risk.
Profit
3.5
3.3
Initial Portfolio
A
3.1
2.9
2.7
2.5
8.0%
8.1%
8.2%
8.3%
8.4%
8.5%
8.6%
8.7%
8.8%
Economic Volatility
Optimization of Insurance Risk Under
Economic Risk Measures
Portfolio
Initial
Economic Volatility
Statistics
3) Optimization of Asset Strategy
3.7
Profit
8.62%
3.3
1
2
8.0%
8.4%
2.6
3
Optimal
4
8.65%
8.77%
3.4
3.8
4.0
56.86
Non-Life SCR
56.8
54.1
55.7
Sharpe Ratio
10.2%
22.3%
31.1%
31.0%
31.0%
5.9%
4.8%
6.2%
6.7%
6.9%
Motor, Vehicle Liability
33.2%
28.0%
28.0%
28.0%
28.0%
Motor, Other Classes
18.0%
18.0%
15.5%
15.5%
15.5%
3.7%
2.5%
4.5%
4.5%
4.5%
Property
30.1%
25.5%
26.9%
31.1%
33.0%
General Liability
11.5%
8.5%
10.5%
13.7%
14.5%
3.5%
2.5%
4.5%
4.5%
4.5%
Return on Capital
Allocation
The goal of our optimization is to identify portfolios
of insurance risk that are efficient from both capital
and economic perspectives, i.e. portfolios where
the two frontiers overlap (see points A and B on the
graph). We then select the insurance portfolio on
the economic efficient frontier with the highest
Sharpe ratio and ROE that is also acceptable given
our capital constraint, leading us to select point B.
In the next table, this is portfolio 3.
B
Capital
Marine, Aviation
& Transport
Credit and Suretyship
57.3
13
Insurance Risk Study
Innovations in Hedging
Variable Annuity Basics
The variable annuity (VA) market globally has total
deposits of approximately USD2.0 trillion of which
roughly USD400 billion have embedded guarantees.
The largest VA markets are the U.S. and Japan. A typical
guarantee on a VA contract provides a financial
guarantee such as a death benefit, living benefit or an
income benefit. Insurance companies typically charge
policyholders a rider fee of between 60 and 200 basis
points of account value for providing these guarantees.
The fee is intended to cover the economic cost of the
guarantee, which amounts to the cost of replicating the
guarantee in the financial market using derivatives and
other financial instruments.
In order to properly hedge the embedded market risks
in theses guarantees, insurers employ various strategies,
including dynamic hedging. For a dynamic hedge to be
effective, the underlying assets need to be rebalanced
as their market prices change. In practice, however,
insurance companies typically set trading tolerances
and only rebalance if the risk breaches these tolerances.
The main market risk factors affecting the value of VA
guarantees are changes in equity markets, changes in
interest rates and changes in implied volatility. It is
common to refer to these sensitivities as Greeks: Delta
for the effect of changes in equity markets, Rho for
changes in interest rates and Vega for changes in
implied volatility.
To calculate these risk factors on large portfolios
representing millions of policyholders, insurers have
been required to deploy massive amounts of computing
power. Sometimes thousands of processors are used to
produce estimates of the risks factors required for
hedging. Until recently, the software used to estimate
the risk required eight hours or more to produce a
range of Greek estimates under various market
scenarios. Given the slow runtime, insurers will often
run these large computations overnight and use only
rough approximations to estimate the risks during the
next trading day.
14
These estimation techniques are prone to substantial
errors, which grow larger the more that markets
move — just when the greatest accuracy is required.
The impact of miscalculating the actual risk can translate
into significant gains or losses due to the large quantity
of assets used to hedge a VA portfolio. We estimate that
North American VA writers lost USD3 billion due to
hedge estimation errors during the 2007 – 2009
financial crisis.
Introducing Pathwise®
To help our clients better manage these hedge
positions, Aon Benfield’s Annuity Solutions Group
developed PathWise, the industry’s fastest VA risk
management system. By leveraging the latest
technology and employing cutting-edge parallel
processing techniques, the Annuity Solutions Group
can perform computations which typically take eight
hours of runtime in just a few minutes.
What does this mean for risk management? By using
PathWise, insurers can substantially reduce the risk
profile of their VA exposure by managing their portfolio
in real-time. Trading is now based on timely and
accurate intraday information about the liabilities and
assets rather than being based on yesterday’s
information and potentially large estimation errors.
A Case Study in Hedging
The following study compares three dynamic hedging
strategies. The first strategy uses information from nightly
simulations and, using Taylor approximation techniques,
estimates the Greeks at the end of the next day — this is
the method used by many insurance companies today.
The second strategy uses real-time simulation to calculate
Greeks once at the end of the trading day and rebalances
once per day. The third strategy uses real-time simulation
to calculate Greeks intraday and executes trades based
on real-time risk factors.
Aon Benfield
Weekly Net Hedged P&L
20
Real-Time Simulation, Intraday Rebalance
Real-Time Simulation, End of Day Rebalance
Taylor Approximation Greeks, End of Day Rebalance
10
Net P&L (USD Millions)
0
-10
-20
-30
-40
-50
7/1/07
1/1/08
7/1/08
1/1/09
7/1/09
The chart and table illustrate the performance of each of these strategies employed by a hypothetical insurer with a
USD10 billion VA portfolio from the middle of 2007 through the end of 2009.
The hypothetical insurer’s profit/loss volatility is 56 to 88 percent lower using real-time simulation techniques in their
hedging strategy. Further, these strategies have reduced their total hedging loss by 24 to 54 percent. Executing an
accurate real-time hedging strategy requires cutting-edge tools and sophisticated computing power. Pathwise makes
such a strategy possible.
Comparison of VA Hedging Strategies (USD Millions)
Statistic
Standard Deviation of P&L
Taylor Approximation Greeks
End of Day Rebalance
Real-Time Simulation End
of Day Rebalance
Real-Time Simulation
Intraday Rebalance
2.5
1.1
0.3
56%
88%
218 bps
130 bps
24%
54%
Standard Deviation improvement
(relative to Taylor Approximation)
Cumulative Loss
Cumulative Loss Improvement
(relative to Taylor Approximation)
285 bps
Aon Benfield’s Annuity Solutions Group believes that new technology can be leveraged by VA product carriers to
improve computation run time and to create the ability to execute data driven hedges in real-time. Even though VA risk
management is complex and multifaceted, the advantages of speed translate directly to improved product design and
competitiveness as well as substantial bottom line savings.
PathWise and all other securities-related advice, products and services of Aon Benfield are offered and distributed solely through Aon Benfield’s affiliate,
Aon Benfield Securities, Inc. (“Aon Benfield Securities”) which is a member of the Financial Industry Regulatory Authority and is registered as both a brokerdealer and investment adviser with the Securities and Exchange Commission. The above information regarding PathWise and variable annuity hedging is
not intended, nor should be considered, as (1) an offer to sell any security, loan or financial product, (2) a solicitation or basis for any contract for purchase of
any security, loan or other financial product, (3) an official confirmation, or (4) a statement of Aon Benfield Securities or any of its affiliates. No representation
is made that the products or services described are suitable or appropriate for any party, in any location or jurisdiction. Potential users of such products or
services are advised to undertake an independent review of applicable legal, tax, regulatory, actuarial and accounting issues. Any offer of such products or
services will be made only through definitive agreements and related documents provided by Aon Benfield Securities or an appropriately licensed affiliate.
15
Insurance Risk Study
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 more and more significant for larger and larger companies. Modeling is invariably performed using an
analysis-synthesis paradigm: analysis is carried out at the product or business unit level and is then aggregated to the
company level. In most applications, results are more significantly impacted by the correlation and dependency
assumptions made during the synthesis step than by all the detailed assumptions made during the analysis step.
The Study determines correlations between lines within each country. Although not shown here, we have also
calculated confidence intervals for each correlation coefficient.
Correlation between lines is computed by examining the results from larger companies that write pairs of lines in the
same country. The tables below show a sampling of the results available for Australia, China, Germany, Japan, U.K.
and U.S. Strong positive correlation is evident between most pairs of standard lines in these matrices.
Motor
Property
Workers
Comp
General Liability
Marine,
Aviation & Transit
General
Liability
Australia
19%
21%
-24%
21%
31%
-3%
21%
25%
14%
Marine, Aviation & Transit
19%
Motor
21%
31%
Property
-24%
-3%
25%
Workers Comp
21%
21%
14%
-6%
-6%
Motor
Property
24%
Marine,
Aviation & Transit
Credit
General
Liability
23%
Engineering
23%
Agriculture
Credit
Accident & Health
Agriculture
Accident & Health
China
24%
12%
27%
15%
45%
56%
46%
8%
28%
13%
22%
-3%
68%
18%
17%
30%
23%
36%
37%
48%
24%
33%
52%
44%
42%
18%
46%
Engineering
12%
8%
68%
General Liability
27%
28%
18%
36%
Marine, Aviation & Transit
15%
13%
17%
37%
Motor
45%
22%
30%
48%
52%
42%
Property
56%
-3%
23%
24%
44%
18%
16
33%
52%
52%
Correlation is a measure of association
between two random quantities. It
varies between -1 and +1, with +1
indicating a perfect increasing linear
relationship and -1 a perfect decreasing
relationship. The closer the coefficient
is to either +1 or -1 the stronger the
linear association between the two
variables. A value of 0 indicates no
linear relationship whatsoever.
All correlations in the Study are
estimated using the Pearson
sample correlation coefficient.
In each table the correlations shown
in bold are statistically different from
zero at the 90% confidence level.
Aon Benfield
General
Liability
Legal
Protection
Marine,
Aviation
& Transit
Motor
Property
46%
34%
43%
-28%
-8%
15%
Accident
& Health
Credit
Germany
Accident & Health
Credit
46%
General Liability
34%
53%
53%
n/a
-14%
25%
19%
34%
28%
2%
24%
-48%
-34%
13%
42%
28%
Legal Protection
43%
n/a
34%
Marine, Aviation & Transit
-28%
-14%
28%
-48%
Motor
-8%
25%
2%
-34%
42%
Property
15%
19%
24%
13%
28%
7%
7%
Workers
Comp
Marine, Aviation & Transit
Property
General Liability
Motor
Accident & Health
Marine,
Aviation
& Transit
General
Liability
Accident
& Health
Japan
2%
48%
35%
50%
-1%
3%
33%
26%
15%
33%
-2%
58%
41%
22%
22%
2%
-1%
Motor
48%
3%
15%
Property
35%
33%
33%
58%
Workers Comp
50%
26%
-2%
41%
31%
31%
Accident & Health
Commercial Lines Liability
50%
50%
Private
Motor
Household
& Domestic
Financial
Loss
Commercial
Property
Commercial
Motor
Commercial
Lines Liability
Accident
& Health
U.K.
74%
57%
9%
17%
52%
53%
36%
-1%
44%
52%
-11%
23%
69%
-26%
60%
37%
Commercial Motor
74%
53%
Commercial Property
57%
36%
55%
Financial Loss
9%
-1%
-11%
-26%
Household & Domestic
17%
44%
23%
60%
8%
Private Motor
52%
52%
69%
37%
13%
55%
8%
13%
14%
14%
Homeowners
Medical
Malpractice
CM
Other Liability
CM
Other Liability
Occ
Personal Auto
Liability
Products
Liability Occ
Workers Comp
Commercial Auto
Commercial
Multi Peril
Commercial
Auto
U.S.
54%
9%
72%
44%
66%
30%
72%
60%
22%
58%
42%
48%
29%
41%
42%
1%
-2%
1%
8%
14%
-4%
71%
76%
48%
73%
68%
58%
39%
31%
61%
33%
67%
62%
43%
32%
Commercial Multi Peril
54%
Homeowners
9%
22%
Medical Malpractice CM
72%
58%
1%
Other Liability CM
44%
42%
-2%
71%
Other Liability Occ
66%
48%
1%
76%
58%
Personal Auto Liability
30%
29%
8%
48%
39%
33%
Products Liability Occ
72%
41%
14%
73%
31%
67%
43%
Workers Comp
60%
42%
-4%
68%
61%
62%
32%
63%
63%
17
Insurance Risk Study
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 — as the market reaction to
a potential double dip recession in August shows.
The following matrix shows correlation coefficients for
various macroeconomic variables that impact an
insurer’s balance sheet.
The Consumer Price Index and Producer Price Index
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 CBOE Volatility
Index (VIX) increases.
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.
The VIX 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. This result is consistent
with the Efficient Market Hypothesis.
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 among
macroeconomic factors will significantly underestimate
material balance sheet risks.
GDP Growth
Unemployment
Change
3-Month T-Bill Rate
1-3 Year Treasuries
AAA-AA 3-5 Year
Spread
BBB 3-5 Year Spread
S&P 500 Returns
VIX
Property Returns
Inflation (CPI-U)
Inflation (PPI)
Inflation (CPI-U)
Macroeconomic Correlations
78%
-3%
-2%
32%
27%
-11%
-26%
-13%
-23%
13%
4%
-7%
30%
9%
-4%
-20%
-7%
-22%
12%
-70%
-4%
24%
-64%
-69%
5%
-44%
51%
-2%
-25%
62%
76%
-1%
57%
-49%
98%
-32%
-58%
-8%
-24%
16%
-38%
-60%
12%
-27%
13%
85%
-42%
62%
-63%
-33%
67%
-53%
-50%
9%
Inflation (PPI)
78%
GDP Growth
-3%
4%
Unemployment Change
-2%
-7%
-70%
3-Month T-Bill Rate
32%
30%
-4%
-2%
1-3 Year Treasuries
27%
9%
24%
-25%
98%
AAA-AA 3-5 Year Spread
-11%
-4%
-64%
62%
-32%
-38%
BBB 3-5 Year Spread
-26%
-20%
-69%
76%
-58%
-60%
85%
S&P 500 Returns
-13%
-7%
5%
-1%
-8%
12%
-42%
-33%
VIX
-23%
-22%
-44%
57%
-24%
-27%
62%
67%
-50%
Property Returns
13%
12%
51%
-49%
16%
13%
-63%
-53%
9%
18
-30%
-30%
Aon Benfield
Global Market Review
With rates continuing to soften and investment yields depressed, insurers are under intense pressure to expand their
top lines. The next several 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
Top 50 Markets by Gross Written Premium
Country
Motor: USD532B
Brazil
Canada
Rest of Americas
China
U.S.
Japan
South Korea
Rest of APAC
France
Germany
Middle East & Africa
U.K.
Rest of Europe
Rest of Euro Area
Property: USD381B
Brazil
U.S.
Canada
Rest of Americas
China
Japan
South Korea
Rest of APAC
France
Germany
U.K.
Rest of Euro Area
Middle East & Africa
Rest of Europe
Liability: USD268B
Brazil Canada
Rest of Americas
China
Japan
U.S.
South Korea
Rest of APAC
France
Germany
U.K.
Middle East & Africa
Rest of Europe
Rest of Euro Area
Notes: Numbers presented are the latest available.
“Motor” includes all motor insurance coverages.
“Property” includes construction, engineering, marine,
aviation and transit insurance as well as property.
“Liability” includes general liability, workers compensation,
surety, bonds, credit and miscellaneous coverages.
U.S.
Japan
Germany
U.K.
France
China
Italy
South Korea
Canada
Spain
Australia
Brazil
Netherlands
Russia
Switzerland
Belgium
Norway
Austria
Mexico
Sweden
Denmark
Poland
India
Venezuela
Turkey
Argentina
South Africa
Ireland
Czech Republic
Finland
Portugal
Iran
U.A.E.
Israel
Thailand
Malaysia
Greece
Colombia
Taiwan
Luxembourg
Ukraine
Chile
Indonesia
Hong Kong
Romania
New Zealand
Singapore
Slovenia
Puerto Rico
Hungary
Grand Total
P&C GWP
GDP
(USD Billions) (USD Billions)
455.98
76.93
67.79
62.66
59.76
45.83
42.10
33.13
32.95
32.23
21.76
21.40
14.87
14.14
11.65
10.58
9.03
8.91
7.67
7.67
6.72
6.61
6.57
6.42
6.31
5.83
5.54
4.60
4.41
4.30
4.11
3.92
3.82
3.53
3.41
3.41
3.39
3.32
3.18
2.96
2.57
2.07
2.07
2.05
2.05
2.04
1.82
1.80
1.37
1.28
1,148.51
14,657.80
5,458.87
3,315.64
2,247.46
2,582.53
5,878.26
2,055.11
1,007.08
1,574.05
1,409.95
1,235.54
2,090.31
783.29
1,465.08
523.77
465.68
414.46
376.84
1,039.12
455.85
310.76
468.54
1,537.97
290.68
741.85
370.27
357.26
204.26
192.15
239.23
229.34
331.02
301.88
213.15
318.85
237.96
305.42
285.51
204.26
54.95
136.42
203.32
706.74
85.31
161.63
126.68
222.70
47.85
67.90
128.96
58,119.49
Population
(Millions)
Premium /
GDP Ratio
GDP per
Capita
313.2
126.5
81.5
62.7
65.3
1,336.7
61.0
48.8
34.0
46.8
21.8
203.4
16.8
138.7
7.6
10.4
4.7
8.2
113.7
9.1
5.5
38.4
1,189.2
27.6
78.8
41.8
49.0
4.7
10.2
5.3
10.8
77.9
5.1
7.5
66.7
28.7
10.8
44.7
23.1
0.5
45.1
16.9
245.6
7.1
21.9
4.3
4.7
2.0
4.0
10.0
4,798.9
3.1%
1.4%
2.0%
2.8%
2.3%
0.8%
2.0%
3.3%
2.1%
2.3%
1.8%
1.0%
1.9%
1.0%
2.2%
2.3%
2.2%
2.4%
0.7%
1.7%
2.2%
1.4%
0.4%
2.2%
0.9%
1.6%
1.6%
2.3%
2.3%
1.8%
1.8%
1.2%
1.3%
1.7%
1.1%
1.4%
1.1%
1.2%
1.6%
5.4%
1.9%
1.0%
0.3%
2.4%
1.3%
1.6%
0.8%
3.8%
2.0%
1.0%
2.0%
46,795
43,161
40,697
35,846
39,541
4,398
33,681
20,656
46,254
30,156
56,763
10,275
46,494
10,560
68,557
44,641
88,337
45,860
9,137
50,155
56,196
12,188
1,293
10,518
9,416
8,865
7,290
43,730
18,857
45,488
21,313
4,250
58,633
28,522
4,779
8,283
28,384
6,384
8,853
109,179
3,022
12,039
2,877
11,978
7,379
29,527
46,976
23,923
17,020
12,927
12,111
Note: Ranks are based on total P&C Written Premium
19
Insurance Risk Study
Global Statistics: Motor
Gross Written Premium
Country
Latest
(USD Millions)
5 Yr Annual
Growth
Average Loss Ratio
Graph
1 Yr
3 Yr
5 Yr
Argentina
3,038
17.1%
66.2%
65.6%
66.8%
Australia
8,046
5.2%
90.4%
94.9%
90.7%
7.2%
7.8%
Austria
3,742
1.3%
71.8%
67.2%
65.4%
6.0%
8.8%
Belgium
10 Yr SD
3.5%
10 Yr CV
4,336
3.9%
73.8%
81.0%
77.9%
9.5%
12.1%
Brazil
13,079
17.8%
63.2%
61.5%
60.0%
4.0%
6.5%
Canada
15,653
-0.6%
74.2%
73.5%
72.1%
6.2%
8.3%
682
14.4%
72.0%
68.6%
66.9%
4.5%
6.5%
6.4%
Chile
China
31,546
28.7%
55.4%
55.6%
55.5%
3.6%
Colombia
1,469
16.4%
52.1%
54.9%
53.7%
2.4%
4.4%
Czech Republic
2,131
9.3%
51.5%
50.7%
51.3%
3.4%
6.5%
Denmark
2,798
7.6%
68.7%
63.6%
62.1%
9.9%
14.8%
Finland
1,779
4.6%
62.5%
70.8%
73.5%
6.1%
8.1%
France
24,729
2.5%
88.2%
83.9%
82.8%
3.4%
4.1%
Germany
27,136
-0.2%
98.5%
97.1%
94.4%
4.7%
5.1%
374
1.2%
47.5%
57.4%
57.3%
6.0%
10.6%
934
-2.8%
62.4%
60.3%
59.3%
2.1%
3.6%
3,385
32.7%
69.7%
70.5%
58.8%
39.2%
54.3%
11.6%
Hong Kong
Hungary
India
Indonesia
609
6.1%
54.0%
51.6%
50.0%
5.4%
Iran
2,870
20.8%
73.4%
77.3%
79.9%
6.9%
8.4%
Ireland
1,896
-2.3%
94.8%
85.0%
76.6%
14.2%
18.1%
8.1%
Israel
2,082
4.9%
107.9%
106.2%
100.4%
8.0%
Italy
27,962
1.2%
85.6%
80.2%
77.9%
5.5%
7.1%
Japan
45,515
0.9%
70.4%
69.0%
67.5%
2.6%
4.0%
Luxembourg
494
6.7%
74.6%
70.7%
69.7%
4.2%
6.0%
Malaysia
1,837
9.5%
84.0%
77.0%
74.9%
8.2%
11.9%
Mexico
3,962
4.1%
71.3%
71.7%
72.4%
3.1%
4.4%
818
10.3%
73.3%
73.5%
76.9%
8.4%
11.3%
6.6%
Morocco
Netherlands
5,907
1.6%
76.6%
71.9%
69.8%
4.7%
762
2.0%
66.7%
70.0%
68.8%
2.4%
3.5%
Norway
2,867
5.8%
70.7%
70.9%
69.9%
3.3%
4.8%
Poland
4,166
6.4%
74.9%
72.0%
69.3%
4.8%
7.0%
Portugal
1,916
-5.0%
80.3%
76.4%
72.5%
4.5%
6.3%
6.1%
New Zealand
Puerto Rico
670
-4.6%
61.0%
60.1%
61.8%
3.9%
Romania
1,573
14.7%
62.1%
64.1%
63.7%
4.4%
7.1%
Russia
7,778
13.2%
68.7%
66.0%
60.2%
13.0%
23.0%
Saudi Arabia
864
15.3%
58.1%
56.5%
54.7%
4.8%
8.8%
Singapore
862
16.0%
73.1%
78.3%
78.4%
10.2%
13.0%
6.3%
Slovenia
786
8.8%
70.8%
67.2%
65.2%
4.1%
South Africa
2,527
13.3%
70.9%
70.0%
69.2%
3.9%
5.4%
South Korea
8,382
3.4%
74.2%
71.1%
72.5%
4.1%
5.8%
Spain
15,879
3.0%
78.0%
72.9%
74.5%
6.4%
8.5%
Switzerland
5,112
4.4%
68.5%
62.3%
62.1%
4.4%
6.9%
Taiwan
1,664
-1.9%
61.9%
59.2%
58.6%
2.7%
4.4%
Thailand
2,354
14.4%
53.1%
55.5%
56.0%
2.2%
3.8%
Turkey
3,099
9.9%
85.0%
76.5%
75.2%
9.3%
13.1%
U.A.E
1,191
26.5%
70.2%
68.2%
67.9%
7.6%
10.6%
U.K.
19,659
-3.5%
95.0%
83.7%
81.6%
5.9%
7.5%
U.S.
187,171
-0.5%
62.4%
62.7%
61.1%
4.2%
6.8%
Ukraine
Venezuela
Grand Total
5 Yr LR
5.2%
665
13.3%
48.5%
48.7%
45.4%
7.5%
16.4%
4,913
40.6%
60.8%
55.8%
54.8%
6.9%
12.5%
522,856
2.3%
70.3%
70.1%
68.2%
2.6%
3.9%
0%
20
50%
100%
Aon Benfield
Global Statistics: Property
Gross Written Premium
Country
Argentina
Latest
(USD Millions)
690
Average Loss Ratio
Graph
5 Yr Annual
Growth
1 Yr
3 Yr
5 Yr
10 Yr SD
10 Yr CV
0.6%
94.2%
59.6%
52.7%
17.2%
34.5%
Australia
6,652
7.9%
68.3%
73.0%
66.2%
12.0%
17.9%
Austria
3,121
3.8%
61.1%
71.9%
71.0%
8.6%
12.8%
Belgium
3,062
6.8%
58.0%
60.0%
56.7%
7.0%
12.5%
Brazil
7,260
19.0%
39.4%
43.8%
45.0%
16.2%
34.6%
11,716
6.9%
67.1%
63.8%
62.1%
7.9%
13.1%
Chile
Canada
1,319
15.3%
774.4%
317.5%
236.4%
231.4%
136.7%
China
8,093
27.9%
54.4%
55.0%
54.5%
8.4%
16.4%
Colombia
1,043
11.6%
22.9%
30.1%
31.3%
5.5%
18.3%
986
7.1%
59.4%
53.5%
51.2%
36.6%
58.3%
18.5%
Czech Republic
Denmark
4,223
7.7%
81.7%
79.0%
77.0%
13.7%
Finland
1,187
2.6%
66.2%
65.9%
67.9%
8.0%
11.4%
France
26,674
5.3%
75.0%
69.2%
68.6%
16.4%
22.6%
Germany
25,219
4.8%
68.4%
72.5%
70.1%
8.2%
11.5%
698
3.4%
28.6%
40.7%
40.1%
7.3%
19.9%
Hong Kong
Hungary
696
3.1%
63.1%
46.4%
42.2%
8.9%
22.0%
India
1,782
8.5%
40.7%
40.5%
37.7%
9.5%
24.9%
Indonesia
1,202
4.4%
47.4%
43.0%
41.8%
16.9%
33.5%
691
15.0%
28.7%
27.2%
28.3%
5.7%
21.9%
1,383
-0.8%
91.5%
79.1%
66.3%
18.4%
30.1%
848
4.9%
67.9%
62.8%
61.4%
11.2%
18.1%
7,951
4.3%
76.3%
68.3%
64.0%
7.3%
11.5%
Iran
Ireland
Israel
Italy
Japan
16,689
2.9%
35.8%
36.7%
38.2%
11.9%
28.3%
Luxembourg
1,502
41.9%
70.4%
71.5%
69.3%
15.5%
23.3%
Malaysia
1,169
7.3%
36.8%
31.5%
26.9%
12.5%
52.4%
Mexico
2,500
5.9%
60.8%
49.5%
53.0%
32.6%
55.4%
249
8.8%
37.5%
48.1%
46.6%
36.5%
60.0%
Netherlands
5,260
5.9%
62.1%
60.8%
57.6%
5.1%
8.9%
New Zealand
1,044
3.1%
50.5%
57.5%
53.3%
7.3%
14.7%
Norway
4,253
4.9%
54.2%
47.1%
42.8%
7.5%
18.0%
Poland
1,445
8.9%
72.8%
55.7%
49.9%
11.8%
25.8%
Morocco
Portugal
960
0.6%
62.4%
54.7%
51.5%
8.8%
18.1%
Puerto Rico
757
-1.5%
20.9%
21.1%
21.5%
2.3%
11.2%
Romania
385
12.3%
19.0%
22.4%
21.2%
4.2%
20.9%
5,860
14.4%
22.0%
19.8%
17.0%
5.8%
35.8%
Saudi Arabia
795
13.5%
33.5%
37.0%
35.2%
15.6%
57.0%
Singapore
587
4.3%
32.3%
35.4%
32.2%
7.7%
22.8%
22.0%
Russia
339
11.8%
77.4%
83.1%
76.2%
15.3%
South Africa
Slovenia
2,192
13.1%
63.6%
59.3%
56.8%
7.5%
12.6%
South Korea
2,050
3.7%
52.6%
52.8%
46.1%
10.0%
21.5%
Spain
9,805
8.3%
56.9%
56.1%
59.0%
9.2%
14.9%
Switzerland
3,915
4.4%
46.7%
48.4%
52.2%
5.2%
9.8%
988
-3.7%
66.2%
48.2%
41.8%
21.4%
51.3%
21.2%
Taiwan
Thailand
841
10.8%
33.0%
36.3%
38.9%
8.1%
2,064
12.3%
41.6%
36.9%
36.2%
6.4%
16.9%
967
23.3%
54.3%
54.3%
54.1%
16.0%
30.4%
U.K.
23,911
-0.6%
56.6%
63.0%
60.0%
8.7%
14.5%
U.S.
158,646
2.6%
51.8%
57.9%
55.6%
13.0%
21.6%
518
-0.9%
8.6%
21.8%
18.4%
8.0%
69.7%
848
13.2%
29.0%
23.4%
23.6%
13.5%
48.8%
373,676
3.8%
57.5%
57.9%
56.2%
6.9%
11.9%
Turkey
U.A.E
Ukraine
Venezuela
Grand Total
5 Yr LR
236%
0%
50%
100%
21
Insurance Risk Study
Global Statistics: Liability
Gross Written Premium
Country
Argentina
Latest
(USD Millions)
2,106
Average Loss Ratio
Graph
5 Yr Annual
Growth
1 Yr
3 Yr
5 Yr
10 Yr SD
10 Yr CV
22.3%
73.2%
64.7%
64.2%
4.7%
7.4%
25.7%
Australia
6,703
2.1%
61.5%
54.6%
50.3%
11.3%
Austria
2,049
8.9%
62.6%
60.0%
58.4%
3.8%
6.5%
Belgium
3,179
4.2%
65.2%
104.8%
96.6%
24.6%
26.6%
Brazil
1,422
19.0%
28.9%
34.9%
36.8%
9.7%
24.7%
Canada
4,864
3.6%
59.2%
50.8%
51.2%
6.9%
12.7%
567
18.3%
47.2%
59.2%
58.2%
12.2%
21.8%
Chile
China
Colombia
Czech Republic
2,458
4.7%
47.9%
53.0%
49.6%
8.8%
19.8%
668
20.5%
30.0%
28.4%
27.1%
4.2%
14.9%
12.2%
726
13.5%
47.8%
41.4%
42.3%
5.4%
Denmark
1,184
5.9%
62.1%
62.8%
68.7%
13.4%
17.7%
Finland
1,148
1.0%
73.4%
78.7%
80.3%
8.8%
10.3%
France
11,253
8.8%
56.8%
55.9%
57.7%
8.8%
14.2%
Germany
15,661
2.4%
69.0%
69.2%
67.6%
5.8%
8.3%
1,001
6.2%
45.7%
48.8%
49.6%
11.0%
20.8%
Hong Kong
Hungary
170
-1.3%
38.7%
38.7%
38.8%
6.0%
16.8%
1,403
-13.7%
48.8%
40.9%
65.1%
51.8%
73.8%
Indonesia
238
14.5%
32.5%
27.7%
27.7%
7.7%
31.5%
Iran
260
9.3%
48.7%
48.0%
50.9%
9.8%
19.2%
1,022
-5.5%
87.1%
63.8%
59.1%
19.5%
28.9%
575
1.3%
103.9%
96.7%
90.5%
9.0%
9.8%
6,996
5.8%
76.4%
72.9%
73.8%
6.3%
8.1%
14,722
-2.4%
34.2%
33.2%
30.7%
6.9%
27.8%
Luxembourg
803
24.6%
108.4%
81.9%
76.6%
30.6%
45.2%
Malaysia
334
9.7%
22.0%
23.3%
24.0%
22.3%
66.4%
1,206
6.8%
27.8%
30.0%
28.7%
4.9%
15.6%
370
11.7%
65.0%
70.4%
74.9%
9.6%
12.2%
3,705
7.4%
58.5%
54.6%
54.8%
4.7%
8.1%
248
10.2%
57.8%
48.0%
43.3%
9.1%
23.0%
India
Ireland
Israel
Italy
Japan
Mexico
Morocco
Netherlands
New Zealand
Norway
Poland
Portugal
1,909
0.8%
42.9%
37.6%
36.8%
3.2%
9.1%
994
17.9%
32.8%
28.4%
27.3%
4.6%
16.2%
20.9%
1,046
-3.9%
70.0%
74.4%
73.0%
14.4%
Puerto Rico
393
-0.7%
51.2%
42.1%
38.8%
7.2%
16.8%
Romania
109
-1.3%
38.3%
47.9%
56.0%
15.9%
30.7%
Russia
1,048
5.5%
30.9%
29.5%
24.3%
11.6%
68.2%
Saudi Arabia
145
23.4%
18.1%
21.8%
24.2%
10.1%
42.9%
Singapore
588
14.1%
47.7%
40.8%
41.3%
8.4%
17.9%
Slovenia
151
10.7%
72.8%
68.2%
70.4%
9.2%
12.7%
South Africa
643
4.5%
38.5%
50.2%
53.4%
10.1%
17.2%
South Korea
22,522
15.9%
79.6%
79.8%
80.2%
42.1%
47.7%
Spain
7,643
5.6%
69.3%
61.0%
61.9%
15.3%
25.4%
Switzerland
2,618
4.9%
53.3%
47.9%
46.7%
7.8%
16.3%
Taiwan
307
-5.1%
41.0%
46.5%
43.4%
13.4%
27.4%
Thailand
212
-1.8%
44.0%
32.4%
35.7%
15.3%
39.5%
Turkey
249
17.1%
30.1%
21.8%
21.4%
5.1%
24.3%
U.A.E
1,254
27.6%
32.0%
31.0%
37.0%
17.7%
40.1%
U.K.
16,187
-4.2%
65.2%
57.8%
56.1%
5.0%
8.7%
U.S.
110,164
-3.0%
67.3%
71.4%
63.1%
9.6%
14.7%
Ukraine
768
13.9%
41.7%
14.8%
14.1%
12.4%
88.3%
Venezuela
661
25.2%
9.1%
9.1%
9.6%
14.5%
99.6%
264,148
0.6%
62.1%
64.8%
60.2%
5.5%
9.0%
Grand Total
5 Yr LR
0%
22
50%
100%
Aon Benfield
Afterword: The Good Risk
A central character in 1990’s underwriter folklore was
the “good risk”. The good risk had many archetypal
characteristics in the underwriter’s mind. It had low
frequency. It had higher than average persistency. It may
have been part of an account that could be rounded out
with products from the underwriter’s carrier. It did not
have severity potential. Nor was it correlated with other
risks through geographic proximity or economic ties. In
short, the good risk had characteristics associated, in the
conventional wisdom, with long-term profitability. It
stood to reason, therefore, that it was worth competing
for the good risk. Underwriters competed on price and
coverage to win a good risk. And this led to a paradox of
insurance: the unprofitable “good risk”.
Since the trough of the last soft market in 1999 – 2000,
when unprofitable good risks were common, we have
learned a lot about pricing and risk management and
have greatly reduced the prevalence of unprofitable
good risks. Predictive modeling, in particular, has driven
a disciplined approach to underwriting in personal and
small commercial lines. But the real lesson of
unprofitable good risks is that it is impossible to define a
good risk without discussing price. There is a price at
which any risk can become a good risk: not necessarily a
sellable price, but at least a technical price. Underwriters
must know this technical price. It turns out there are also
a number of risk management parallels to the
unprofitable good risk today.
Defining Risk Tolerance
Risk tolerance is a central concept in risk management
and something discussed in every presentation on
Enterprise Risk Management (ERM). The folklore of
risk tolerance is similar to that of the good risk.
Tolerances need to be defined. They must be
“drilled down” to provide granular guidelines at
every level of the organization. Risks must be
identified and measured; exposures monitored a
nd managed relative to detailed tolerances.
Tolerances are generally set from a tail-centric,
solvency perspective: “What is the largest risk I want
on the balance sheet that is consistent with the promises
I have made to stakeholders — especially to other
policyholders?” A risk tolerance is a wall around the
balance sheet. Rating agencies and regulators are
rightfully concerned with the position, height and
integrity of this wall. But is the risk tolerance wall a good
risk management concept for a going concern insurer?
Very often risk tolerance is expressed solely in terms of
limits: the organization has a tolerance to lose up to one
quarter’s income in a single catastrophe event, for
example. But what if the price of catastrophe risk
doubled, or tripled? Or if the cost to reinsure cat risk
doubled or halved? Does the tolerance stay the same?
Do underwriting actions stay the same?
Having defined a risk tolerance in terms of tail risk,
management turns to optimization to maximize return
for a given tolerance. Economics tells us to compare
marginal revenue with marginal cost. For insurance the
revenue side of the equation is easy; most companies
are price takers and their underwriters have a good idea
of the market price available for a given risk. Marginal
cost is more difficult. For catastrophe risk, cost of capital
becomes a material part of total costs, and is where the
difficulties begin. A tail-centric risk tolerance, driven by
the 100 or 250 year PML, for example, simply does not
“see” smaller risks; they have no impact on the PML. As
a result they are not allocated any capital in a marginal
approach. At Aon Benfield we have seen two important
examples of how this drives real behavior in the
industry: global reinsurance pricing and U.S. severe
weather pricing.
A feature of global reinsurance pricing is that, for a given
probability of attachment and expected loss, it is far
more expensive to lay off U.S. wind exposure — the
global peak exposure — than it is for Australian or
Japanese risks. This feature is a direct result of using a
marginal capital allocation approach and a tail-driven
capital measure. Reinsurers must use tail-driven capital
measures because one of their key constraints is the
rating agency catastrophe risk charge, and this is
expressed in terms of PMLs. A tail driven capital measure
will tend not to allocate much capital to an Australian or
Japanese risk. The risks are seen as diversifying, attract a
low marginal capital allocation and are written much
more cheaply, per unit of volatility transferred, than U.S.
risks. Market pricing follows these predictions.
23
Insurance Risk Study
It remains to be seen whether there is any rebalancing
in risk loads as a result of the recent international
catastrophe risk experience. In part, the fact that U.S.
pricing for Aon Benfield clients decreased on average
for the June and July 2011 renewals validates the theory:
reinsurers did not allocate more capital to the U.S. but
they did for the rest of the world.
The second real-world validation comes from our
experience building Cat ScoreTM, our proprietary
policy-level cat risk pricing tool. Cat Score calculates
three components for a cat risk premium: the average
annual loss, the cost of reinsurance and an internal cost
of capital to support net risk. The average annual loss
component is a characteristic of each individual risk and
is independent of the company writing the risk. It is
generally obtained from a catastrophe risk model.
The other two components are much more interesting.
To understand an appropriate allocation of cost of
reinsurance, we ran numerous regressions on the full
Aon Benfield database of reinsurance contracts. We
found that reinsurer risk margins in the U.S. were best
predicted by looking at a combination of the volatility of
risk transferred and the correlation of that risk with the
aggregate U.S. portfolio. There is a cost for ceding risk,
but consistent with the marginal tail capital global
reinsurance pricing model, it is more expensive in areas
that impact reinsurer tail capital measures the most:
Florida costs more than the Gulf or Northeast, which in
turn cost more than the smaller exposures in the
mid-Atlantic. Reinsurers may not individually price using
this algorithm, but it fits their collective behavior well.
Our approach to the internal cost of capital to support
net risk initially followed the marginal tail risk approach.
The result did not allocate capital to smaller exposures,
such as Midwest severe weather perils in books with
hurricane exposure. This result does not agree with how
risk is perceived. Market pricing charges all risks some
load, even if the risk had no impact on marginal tail risk.
We found that a volatility (standard deviation) driven
approach, accounting for correlations within each client
book, most closely matched realistic market pricing.
There are two lessons to draw from these experiences.
First, reinsurers’ more modern tail-centric methods have
not necessarily driven optimal portfolios, and are being
re-thought in the light of 2011 catastrophe experience.
And second, volatility has a cost regardless of whether it
impacts a tail risk measure.1
What do these observations mean for defining a risk
tolerance?
Valuation Maximization
Just as the unprofitable good risk ignored pricing,
tail risk tolerances ignore volatility and profitability.
If risk tolerance is a protective wall around the balance
sheet, then we need a gate keeper to weigh each
potential policy’s impact on profitability and risk and
to decide whether or not it should be placed on the
balance sheet. The gate keeper represents risk
appetite. Just as pricing adds a new dimension to
defining a “good risk”, so risk appetite adds a
quantification of the value of writing a risk.
What do investors say about risk in their pricing of
insurance company stocks? We have reported on our
price-to-book regression study before in this Study.
The latest, international, iteration is illustrated on
the next page.
1
There are situations where the tail-centric approach is appropriate. One is evaluation of mainframe catastrophe risk programs. These corporate-wide
programs are generally driven by ratings agency or regulator aggregate portfolio tail PML measures and comparing the marginal capital benefit of
such a program to its cost is a good decision framework that will lead to appropriate conclusions.
24
Aon Benfield
Price to Book, Prospective ROE Link
4.00
No Hit
+1 Hit
3.50
Price to Book Ratio
3.00
2.50
2.00
1.50
Prospective
ROE
1.00
0.50
Price to Book Assuming
1+ Hits
No Hits
Delta
10.0%
1.06
1.30
22%
15.0%
1.42
1.78
25%
0
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
an
er
To
l
sk
Ri
Risk Appetite
rn
How should volatility-driven valuation be combined
with tail-centric risk tolerances into a risk appetite, and
what are the implications for pricing and retention
strategies? There is no “one size fits all” answer to
these important questions. Working them out is a
tu
The relationship of risk tolerance, volatility and premium
level to risk appetite is summarized in the Risk Triangle.
Re
The graphic shows the relationship between pro forma
return on equity (based on analysts’ earnings estimates)
and price-to-book ratio. There is, as expected, an
upward sloping relationship: companies with higher
prospective ROEs are more efficient users of capital and
investors are willing to pay more for them. The data
points are further differentiated according to whether
each company has avoided a quarter with a net income
loss over the last 24 quarters (excluding the financial
crisis quarters Q3 and Q4 2008). As we have seen
consistently since the inception of this study in 2006,
companies with no income hits are valued materially
more highly than those with one or more hits. For a
company with a ten percent prospective ROE the
difference on average is 22 percent, a price-to-book
ratio of 1.30 vs 1.06. The clear message is that the
valuation differential is volatility driven and not solely
tail or solvency capital driven, just as the Cat Score
solution led us to expect.
ce
Prospective 2011 ROE
Decision framework, trading tail risk
and volatility risk against return, to
determine which risks are taken and
retained on the balance sheet.
Volatility Risk
client-by-client endeavor and Aon Benfield Analytics
continues to engage with numerous clients on exactly
these questions.
The technological, theoretical and empirical
developments in risk management laid out in this Study
illustrate how vibrant and innovative the insurance
market has become. Aon Benfield is proud to partner
with clients in the vanguard of these developments,
helping build innovative, value-creating solutions to
pressing business problems.
25
Insurance Risk Study
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, Chicago Mercantile Exchange, Comisión Nacional de Seguros y Fianzas (Mexico), Comisióncional de Bancos y
Seguros de Honduras, 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), The Insurance Association of Pakistan, ISO PCS, Korea Financial Supervisory Service, Monetary Authority
of Singapore, MSA Research Inc. (Canada), Quest Data Report (South Africa), Romanian Insurance Asociation, SNL (U.S.), Standard & Poor’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.
Reserve Study Disclosure: Section 17(b) of the Securities Act of 1933 requires that any person that publishes a description of a security for a
consideration must disclose the character and amount of the consideration. Aon Benfield has received from American International Group, Inc.
or its affiliates a consulting fee for various services, of which USD1.0 million is attributable to the U.S. P&C Industry Statutory Reserve Study.
A copy of the Reserve Study can be found at: http://www.aon.com/attachments/reinsurance/201106_analytics_reserve_adequacy_study.pdf
© 2011 Aon Benfield. This document is intended for general information purposes only and should not be construed as advice or opinions on any
specific facts or circumstances. The comments in this summary are based upon Aon Benfield’s preliminary analysis of publicly available
information. The content of this document is made available on an “as is” basis, without warranty of any kind. Aon Benfield disclaims any legal
liability to any person or organization for loss or damage caused by or resulting from any reliance placed on that content. Aon Benfield reserves all
rights to the content of this document.
Scan here to access all editions of the Insurance Risk Study.
About Aon Benfield
Aon Benfield, a division of Aon Corporation (NYSE: AON), is the world’s leading reinsurance intermediary and full-service
capital advisor. We empower our clients to better understand, manage and transfer risk through innovative solutions and
personalized access to all forms of global reinsurance capital across treaty, facultative and capital markets. As a trusted
advocate, we deliver local reach to the world’s markets, an unparalleled investment in innovative analytics, including
catastrophe management, actuarial and rating agency advisory. Through our professionals’ expertise and experience, we
advise clients in making optimal capital choices that will empower results and improve operational effectiveness for their
business. With more than 80 offices in 50 countries, our worldwide client base has access to the broadest portfolio of
integrated capital solutions and services. To learn how Aon Benfield helps empower results, please visit aonbenfield.com.
26
Aon Benfield
For more information on the Insurance Risk Study, ReMetrica®, 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
John Moore
Head of Analytics, International
+44 (0) 20 7522 3973
john.moore@aonbenfield.com
Parr Schoolman
Risk & Capital Strategy
+1 312 381 5330
parr.schoolman@aonbenfield.com
Paul Maitland
ReMetrica, International
+44 (0) 20 7522 3932
paul.maitland@aonbenfield.com
Michael McClane
ReMetrica U.S.
+1 215 751 1596
michael.mcclane@aonbenfield.com
Americas
Asia Pacific
Brian Alvers
+1 312 381 5355
brian.alvers@aonbenfield.com
Will Gardner
+61 2 9650 0390
will.gardner@aonbenfield.com
EMEA & U.K.
David Maneval
+65 6239 7675
david.maneval@aonbenfield.com
Marc Beckers
+ 44 (0) 20 7086 0394
marc.beckers@aonbenfield.com
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+44 (0) 20 7522 3810
paul.kaye@aonbenfield.com
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+65 6239 8739
george.attard@aonbenfield.com
Global Agricultural Practice
Annuity Solutions Group
Avery Cook
+1 312 381 5342
avery.cook@aonbenfield.com
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+1 416 598 7133
peter.phillips@aonbenfield.com
27
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