Risk. Reinsurance. Human Resources.
Aon Benfield
Insurance Risk StudyGrowth, profitability, and opportunity
Ninth edition 2014
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3
Global Premium, Profitability, and Opportunity . . . . . . . . . . . . . .5Global P&C Gross Written Premium and Growth Rates by Product Line . 6
Growth Markets and Over / Under Performers . . . . . . . . . . . . . . . . . . . . . . 8
Looking Ahead: Growth Projections . . . . . . . . . . . . . . . . . . . . . .10
Uncovering Growth Opportunities . . . . . . . . . . . . . . . . . . . . . . .15Country Opportunity Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Insurance Trends: Risks and Opportunities . . . . . . . . . . . . . . . . .17Auto Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
U .S . Health Insurance Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
U .S . Cyber Insurance Market . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
China Crop Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Global Risk Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22
U.S. Risk Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24
U.S. Profitability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25
U.S. Reserve Adequacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26
Global Correlation Between Lines . . . . . . . . . . . . . . . . . . . . . . . .28
Big Data and Insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30
Sources and Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33
Contacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35
Table of Contents
Aon Benfield 3
IntroductionThe 2014 Insurance Risk Study is focused on uncovering profitable growth opportunities in the insurance
market . There are many bright spots within today’s rapidly evolving insurance marketplace . Globally, property
casualty business produced an underwriting profit in 2013 with a combined ratio of 99 .1 percent . In 21 of the
top 50 markets combined ratios were below 95 percent, and in ten the combined ratios were below 90 percent .
Furthermore, 16 countries showed five year premium growth in excess of 10 percent, led by very strong growth in
China . The first section of the Study presents the country and line of business detail to identify these opportunities
and discusses how to move from coarse statistics to targeted growth strategies . The second section provides an
update of our global risk parameters .
Premium, capital, and profitability highlights
At year end 2013, global premium stands at an all-time high of
USD4 .9 trillion, an increase of 0 .9 percent over the prior year .
Property casualty premium increased 3 .5 percent, and
life premiums shrunk by 2 .0 percent while health premiums
grew 4 .5 percent .
Global insurance premium and capital, USD trillions
Premium Capital
Property & Casualty 1 .4 1 .3
Life & Health 3 .3 2 .1
Reinsurance 0 .2 0 .5
Total 4 .9 4 .0
Global capital increased 3 .9 percent year on year to USD4 .0
trillion . Property casualty insurance capital increased 7 .1 percent .
And reinsurance capital is at an all-time high, as we discuss at
greater length in Aon Benfield’s Reinsurance Market Outlook .
Property casualty penetration is 1 .9 percent of GDP, marginally
down from 2 .0 percent last year based on the top 50 countries .
Auto insurance accounts for 46 percent of property casualty
premium, while property accounts for 33 percent and liability
for 21 percent .
Catastrophe losses have been a driver of the growth in
property premiums in many parts of the world . Impact
Forecasting, Aon Benfield’s catastrophe model development
center, estimates that during 2013 insured catastrophe losses
totaled USD45 billion . In perspective, catastrophe losses
translated into 3 .2 percent of property casualty premium and a
“global catastrophe load” of 9 .9 percent of property premium .
The combined ratio for property casualty business in the
top 50 countries in 2013 was under 99 .1 percent compared
to 101 .1 percent last year . The global average is helped by
European countries, with an average combined ratio of 96 .7
percent, compared to 101 .0 percent in the Americas and
100 .4 percent in Asia Pacific . The five year average combined
ratio continued under 100 percent too, at 99 .8 percent . The
overall global combined ratio under 100 percent, and the
variation in results by country, clearly show there are many
desirable areas for profitable growth in the market today .
Where does the insurance market go from here? We
project that global premium will grow by 18 percent over
the next five years, reaching a total of USD 1 .6 trillion by
2018 . Auto will continue to be the largest line, driven
in part by strong growth in China . Detailed projections
by line and country are shown on pages 10 to 14 .
4 Insurance Risk Study
Growth and Big Data
The Insurance Risk Study is now in its ninth edition, and
there have been many changes in the industry since we
began research for the first edition in 2005 . After seven
major (category 3+) U .S . hurricane landfalls from 2004
to 2005 there have been no major hurricane landfalls
in the last eight years . Adverse loss development has
turned into a long stream of favorable development .
Premium growth and the locus of catastrophe losses
has shifted to the East . Today the emphasis is on
making efficient use of cheaper alternative capital and
on growth in the face of an often sluggish economic
environment—challenging themes that recent editions
of the Study have addressed with increasing detail .
The market continues to embrace and adopt “Big
Data” concepts in pricing and underwriting—a subject
we explore on pages 30 to 32 . Big data for insurance
often really means Behavioral Data, with the industry
engaging in an active search for more detailed and
more predictive variables to add to underwriting
and pricing algorithms . Aon Benfield, and Aon more
broadly, are spearheading several initiatives to help
bring more predictive variables and analytic insight to
clients, in areas including health and crop insurance .
The growth imperative continues to stress many
industries, particularly in mature markets . For insurers, the
efficiency gains from Big Data often serve to redistribute
risks, but not to grow the pie—creating clear winners and
losers . The first part of the Study now covers detailed
global information about insurance capital, premium, and
profitability . We continue to be the only comprehensive
view of combined ratio by country available in the public
domain, to the best of our knowledge . We also offer some
ideas for how to grow the pie . The Study includes global
growth projections for insurance and reinsurance as well
as sections on health, auto, crop, and cyber insurance .
Using the study
Insurance risk remains core to the Study and pages 22
to 30 contain our comprehensive view of risk by line
and geography using the techniques we have been
applying consistently since 2005 . The Insurance Risk
Study continues to be the industry’s leading set of risk
parameters for modeling and benchmarking underwriting
risk and global profitability . Beyond risk modeling, we can
also provide our clients with very granular, customized
market intelligence to create business plans that are
realistic, fact-based, and achievable . With a global fact
base and broad access to local market practitioners, we
are equipped to provide insight across a spectrum of
lines, products, and geographies . Inpoint, the consulting
division of Aon Benfield, helps insurers and reinsurers
address these challenges, from sizing market opportunities
to identifying distribution channel dynamics, assessing
competitor behavior, and understanding what it takes to
compete and win . Our approach leverages Aon Benfield’s
USD130 million annual investment in analytics, data,
and modeling to help our clients grow profitably .
All of our work at Aon Benfield is motivated by client
questions . We continue to be grateful to clients who have
invited us to share in the task of helping them analyze
their most complex business problems . Dynamic and
interactive working groups always lead to innovative,
and often unexpected, solutions . If you have questions
or suggestions for items we could explore in future
editions, please contact us through your local Aon Benfield
broker or one of the contacts listed on the back page .
Aon Benfield 5
An abundance of capital is providing new lower cost alternatives to traditional equity risk financing, opening new avenues for growth . After many years of catastrophe risk management, often implemented as exposure reductions, clients are now looking more aggressively at growth opportunities to leverage this new cheaper capacity . To help guide growth decisions, Aon Benfield has worked through a mass of market data from many different sources to produce the consistent, country-level profitability statistics we introduce in this section .
Our strategic decision framework identifies accessible
markets and high-potential customer segments to formulate
growth programs tailored to an insurer’s capabilities and risk
appetite . Working with our broker network and our investment
bank, Aon Benfield Securities, we can develop and help
execute growth plans through organic growth, acquisition,
reinsurance, and joint ventures, singly or in combination .
Part of our job is to make connections and draw comparisons
that others do not see . In that spirit, we begin this section
with the map below, which overlays country names on
states with approximately equivalent premium volumes .
California is the largest U .S . state in terms of premium,
and if it were independent it would follow the U .K . as
the seventh largest insurance market in the world . Texas,
Florida, and New York would also sit among the top
10 as independent countries, having roughly the same
premium as Canada, Italy, and Australia respectively .
This section presents our unique, detailed analysis of
global capital, premium, and profitability, as well as
snapshots of trends and emerging risks that we expect to
create both risk and opportunity in the coming years .
Global P&C premium compared to U.S. state premium
U.K.
Morocco
Romania
Luxembourg
Romania Greece
Singapore
Austria
ThailandSingapore
TaiwanIreland India
DenmarkSaudi Arabia
Canada
Poland
ThailandAustria
Colombia
Mexico
Malaysia
Poland
Switzerland
Italy
Sweden
Netherlands
Portugal
South Africa
Thailand
India
Brazil Austria
Venezuela
SwitzerlandBrazil
Greece
Russia Australia
Russia
South Africa
South Africa Argentina
Morocco
Romania
Romania
Nigeria
Israel
Turkey
MoroccoPacific Region = China
Mid-Atlantic Region = China
East North Central Region = Germany
West South Central Region = U.K.
West North Central Region = Canada
Mountain Region = Australia
East South Central Region = Spain
New England Region = Spain
South Atlantic Region = Japan
Global Premium, Profitability, and Opportunity
6 Insurance Risk Study
Premium by product line
Notes: All statistics 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., 44%
U.S., 34%
Middle East & Africa
Rest of Europe
Rest of Euro AreaU.K.
GermanyFrance, 4%
Rest of APAC
South Korea
Japan
China, 10%
Rest of Americas
Canada
Motor: USD 633 billion
Property: USD 453 billion
Brazil
Middle East & Africa
Rest of Europe
Rest of Euro Area
U.K.
Germany
France, 6%
Rest of APACSouth Korea
JapanChina, 3%
Rest of AmericasCanada
Brazil
Liability: USD 296 billion
U.S., 45%
Middle East& Africa
Rest of Europe Rest of Euro Area
U.K.
Germany
France, 6%
Rest of APAC
South KoreaJapan
China, 2%Rest of Americas
CanadaBrazil
Five-year average annual growth rate
Property: 4.0% annual growth
Liability: 1.6% annual growth
Motor: 3.6% annual growth
-10
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Global P&C gross written premium and growth rates by product line
Aon Benfield 7
Top 50 P&C markets ranked by gross written premium by region
P&C
GW
P (U
SD M
)
Prem
ium
/ G
DP
Rati
o
Annualized Premium Growth Cumulative Net Loss Ratio Cumulative Net Expense Ratio Cumulative Net Combined Ratio
1yr 3yr 5yr 1yr 3yr 5yr 1yr 3yr 5yr 1yr 3yr 5yr
Americas
U .S . 531,838 3 .0% 5 .6% 5 .3% 2 .5% 74 .8% 75 .6% 74 .8% 27 .3% 27 .2% 27 .0% 102 .1% 102 .9% 101 .9%
Canada 42,179 2 .4% 0 .8% 7 .0% 4 .6% 65 .3% 69 .6% 70 .3% 29 .0% 28 .7% 28 .6% 94 .3% 98 .3% 99 .0%
Brazil 23,647 1 .1% -3 .1% 3 .2% 5 .9% 53 .0% 53 .6% 55 .3% 35 .3% 34 .4% 30 .4% 88 .3% 88 .1% 85 .7%
Argentina 11,835 2 .9% 13 .8% 19 .9% 16 .9% 71 .2% 68 .4% 67 .5% 36 .4% 37 .3% 37 .3% 107 .6% 105 .7% 104 .8%
Mexico 10,415 0 .8% 11 .9% 10 .7% 6 .2% 61 .7% 64 .0% 65 .9% 30 .4% 30 .2% 31 .0% 92 .2% 94 .2% 96 .9%
Venezuela 6,732 2 .0% 27 .2% 1 .6% 12 .2% 58 .8% 61 .5% 63 .7% 37 .7% 35 .2% 33 .1% 96 .5% 96 .7% 96 .9%
Colombia 4,425 1 .1% 0 .5% 11 .2% 11 .2% 63 .5% 61 .4% 61 .4% 48 .7% 48 .0% 47 .6% 112 .2% 109 .4% 108 .9%
Chile 3,708 1 .4% -0 .3% 11 .8% 10 .2% 50 .4% 51 .4% 51 .6% 45 .0% 43 .0% 44 .0% 95 .4% 94 .4% 95 .6%
Ecuador 1,547 1 .5% 16 .5% 16 .3% 14 .0% 53 .0% 52 .3% 53 .2% 35 .8% 34 .3% 33 .7% 88 .8% 86 .6% 86 .9%
Subtotal 636,325 2.6% 5.3% 5.6% 3.1% 72.7% 73.8% 73.4% 28.3% 28.2% 27.7% 101.0% 101.9% 101.1%
Europe, Middle East & Africa
Germany 71,432 1 .8% 3 .1% 2 .3% 0 .5% 73 .3% 74 .6% 73 .9% 25 .3% 25 .7% 25 .2% 98 .6% 100 .3% 99 .0%
U .K . 65,538 2 .3% 1 .3% 3 .5% -3 .0% 65 .2% 67 .1% 66 .9% 34 .3% 33 .9% 34 .3% 99 .5% 101 .0% 101 .2%
France 66,918 2 .3% -6 .5% -1 .8% -0 .3% 73 .5% 74 .5% 74 .3% 24 .2% 24 .5% 24 .6% 97 .7% 99 .0% 98 .9%
Italy 37,397 1 .7% -2 .4% -2 .2% -4 .2% 71 .5% 74 .2% 75 .2% 23 .7% 23 .6% 23 .6% 95 .2% 97 .7% 98 .8%
Spain 28,826 2 .0% 0 .0% -1 .7% -4 .9% 71 .2% 71 .6% 71 .5% 21 .4% 21 .0% 21 .1% 92 .6% 92 .6% 92 .5%
Russia 19,199 0 .9% 8 .2% 14 .7% 5 .9% 63 .0% 64 .7% 65 .8% 28 .6% 24 .5% 22 .9% 91 .6% 89 .3% 88 .7%
Netherlands 13,366 1 .6% -7 .9% -3 .5% -1 .0% 88 .8% 88 .6% 88 .0% 12 .0% 12 .7% 13 .0% 100 .9% 101 .3% 101 .0%
Switzerland 14,682 2 .1% 2 .4% 5 .3% 5 .1% 68 .6% 70 .0% 70 .9% 26 .6% 26 .1% 26 .2% 95 .2% 96 .1% 97 .1%
Belgium 10,880 2 .0% -4 .2% 0 .9% 1 .7% 67 .2% 70 .1% 71 .3% 28 .1% 28 .0% 27 .7% 95 .2% 98 .1% 99 .0%
Norway 9,454 1 .8% 9 .5% 7 .9% 5 .7% 71 .4% 73 .5% 73 .8% 14 .2% 15 .1% 15 .7% 85 .7% 88 .7% 89 .6%
Austria 9,767 2 .2% 5 .9% 3 .3% 0 .4% 70 .2% 70 .8% 70 .6% 28 .3% 28 .7% 28 .5% 98 .5% 99 .5% 99 .0%
Sweden 7,669 1 .3% -1 .3% 5 .2% 0 .8% 74 .1% 73 .9% 73 .4% 18 .4% 17 .7% 17 .8% 92 .4% 91 .6% 91 .1%
Denmark 8,473 2 .4% 2 .7% 1 .8% -0 .1% 71 .4% 76 .3% 76 .2% 17 .2% 17 .2% 17 .3% 88 .6% 93 .5% 93 .5%
Turkey 7,770 1 .0% 14 .2% 13 .0% 5 .3% 79 .0% 77 .7% 78 .2% 26 .7% 27 .8% 26 .9% 105 .7% 105 .5% 105 .1%
Poland 7,439 1 .4% 3 .5% 3 .4% -0 .2% 60 .8% 65 .9% 64 .1% 30 .6% 30 .7% 31 .7% 91 .4% 96 .7% 95 .8%
South Africa 9,968 2 .8% -3 .0% 9 .2% 11 .7% 61 .0% 61 .3% 63 .3% 24 .9% 25 .0% 24 .2% 86 .0% 86 .4% 87 .6%
Finland 5,107 1 .9% 15 .6% 7 .6% 3 .2% 78 .2% 81 .7% 80 .1% 20 .5% 20 .7% 20 .6% 98 .7% 102 .4% 100 .6%
Ireland 3,548 1 .5% -9 .2% -6 .2% -6 .1% 73 .2% 72 .1% 72 .6% 29 .3% 29 .2% 28 .4% 102 .5% 101 .3% 101 .0%
Israel 4,326 1 .4% 13 .3% 6 .2% 4 .0% 74 .3% 76 .2% 77 .2% 32 .2% 32 .2% 31 .5% 106 .5% 108 .4% 108 .7%
Czech Republic 3,935 2 .0% 4 .1% -0 .7% -2 .5% 62 .5% 63 .0% 63 .1% 29 .9% 29 .3% 27 .9% 92 .4% 92 .3% 91 .0%
U .A .E . 3,424 0 .8% -8 .9% -1 .2% 0 .1% 70 .4% 71 .5% 70 .5% 22 .0% 19 .9% 17 .6% 92 .4% 91 .4% 88 .1%
Portugal 4,165 1 .8% -8 .6% -4 .4% -5 .0% 71 .7% 71 .2% 70 .0% 23 .3% 22 .8% 22 .7% 95 .1% 94 .0% 92 .7%
Greece 2,840 1 .1% -4 .9% -2 .2% 0 .4% 56 .4% 58 .3% 62 .1% 40 .5% 38 .8% 38 .6% 96 .9% 97 .1% 100 .7%
Saudi Arabia 3,067 0 .4% 27 .8% 19 .6% 15 .8% 79 .1% 74 .2% 73 .4% 15 .0% 18 .3% 18 .1% 94 .1% 92 .5% 91 .5%
Romania 1,929 1 .0% 9 .2% -2 .0% -6 .9% 72 .1% 72 .7% 75 .0% 42 .5% 40 .5% 36 .8% 114 .6% 113 .2% 111 .8%
Morocco 1,638 1 .4% 11 .5% 5 .9% 10 .8% 57 .9% 61 .2% 64 .5% 33 .2% 33 .8% 33 .2% 91 .1% 95 .1% 97 .8%
Nigeria 1,136 0 .4% 8 .5% 2 .1% 17 .2% 51 .0% 49 .3% 48 .6% 31 .4% 31 .2% 31 .7% 82 .5% 80 .5% 80 .2%
Luxembourg 951 1 .5% -3 .2% 2 .2% -13 .0% 66 .0% 65 .3% 64 .7% 37 .2% 37 .5% 37 .2% 103 .2% 102 .8% 101 .9%
Bulgaria 917 1 .7% 7 .6% 0 .3% -3 .9% 54 .6% 55 .0% 54 .8% 34 .8% 35 .8% 35 .5% 89 .5% 90 .8% 90 .3%
Subtotal 425,763 1.8% 0.4% 1.8% -0.5% 72.0% 73.2% 73.3% 24.8% 24.6% 24.6% 96.7% 97.8% 97.9%
Asia Pacific
Japan 94,825 2 .0% 2 .7% 7 .3% 7 .8% 69 .1% 71 .0% 69 .1% 33 .2% 33 .9% 34 .4% 102 .3% 105 .0% 103 .5%
China 84,431 0 .8% 18 .1% 26 .1% 26 .3% 64 .4% 64 .6% 66 .8% 34 .5% 33 .3% 31 .9% 98 .9% 97 .9% 98 .7%
Australia 34,097 2 .4% 0 .1% 11 .6% 11 .0% 64 .1% 69 .8% 71 .6% 27 .7% 27 .8% 28 .1% 91 .8% 97 .6% 99 .7%
S . Korea 13,298 1 .0% -12 .5% -0 .7% 0 .0% 78 .5% 77 .8% 77 .9% 23 .6% 23 .1% 23 .1% 102 .1% 100 .9% 101 .1%
India 9,200 0 .5% 9 .3% 12 .1% 10 .2% 82 .8% 87 .7% 87 .4% 28 .7% 30 .1% 31 .1% 111 .6% 117 .9% 118 .5%
Thailand 5,651 1 .5% 14 .7% 18 .4% 14 .6% 75 .6% 69 .6% 64 .6% 34 .4% 35 .5% 36 .4% 110 .0% 105 .1% 101 .0%
Malaysia 4,442 1 .3% 4 .5% 8 .3% 8 .4% 59 .0% 61 .9% 62 .3% 30 .6% 28 .5% 28 .4% 89 .7% 90 .4% 90 .7%
Taiwan 3,917 0 .8% 2 .7% 7 .5% 3 .1% 59 .6% 58 .9% 56 .8% 37 .3% 37 .7% 40 .5% 96 .9% 96 .6% 97 .2%
New Zealand 3,886 2 .0% 10 .6% 16 .1% 12 .1% 59 .5% 90 .7% 84 .4% 36 .6% 35 .3% 36 .1% 96 .1% 126 .1% 120 .5%
Indonesia 3,404 0 .4% 3 .5% 15 .4% 11 .6% 53 .3% 54 .3% 55 .0% 33 .0% 33 .3% 33 .2% 86 .3% 87 .6% 88 .2%
Hong Kong 3,187 1 .1% 29 .6% 15 .7% 11 .6% 61 .1% 59 .9% 59 .5% 45 .8% 38 .9% 39 .1% 106 .8% 98 .8% 98 .6%
Singapore 2,501 0 .8% 2 .1% 6 .8% 7 .5% 53 .8% 55 .0% 55 .7% 32 .8% 33 .2% 33 .1% 86 .6% 88 .3% 88 .9%
Subtotal 262,839 1.2% 6.5% 12.9% 12.2% 69.4% 70.8% 70.7% 31.0% 30.9% 31.0% 100.4% 101.8% 101.7%
Top 50 1,324,927 1 .9% 3 .9% 5 .6% 3 .3% 71 .6% 72 .8% 72 .8% 27 .4% 27 .3% 27 .0% 99 .1% 100 .1% 99 .8%
8 Insurance Risk Study
Growth markets and over or under performersAon Benfield examined premium growth and loss ratio
performance by country across motor, property, and liability
lines of business as well as premium growth and combined ratio
performance by country for all lines . The quadrant plots below
summarize the results of that analysis and identify countries as
either low growth or high growth and as loss ratio (by line) or
combined ratio (total) out performers or under performers .
To measure performance, the first three quadrant plots use loss
ratio for each line of business while the right-most plot shows
combined ratio for all lines of business . Each plot also provides
the gross written premium size, in USD millions, of each country .
For all quadrant plots, growth is determined based on five
year annualized premium growth . Countries with values
greater than 7 .5 percent are classified as high growth .
Loss ratio and combined ratio performance is determined
based on five year average loss ratio and five year average
combined ratio, respectively . Each country’s loss ratio
performance is compared against its income level peers,
using a USD30,000 GDP per capita split between high income
and low income companies . Combined ratio performance
is compared against the global combined ratio . Countries
with five year loss ratios lower than the average of their
income peers, or combined ratios below the global
combined ratio, are classified as out performers .
Property
Loss ratio performance
Motor
Loss ratio performance
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Australia 11,644Brazil* 1,692Chile* 742China* 7,007Colombia* 1,078Ecuador* 337Hong Kong* 1,425India* 1,764Indonesia* 487New Zealand* 421Russia* 2,231Saudi Arabia 251Singapore* 784South Africa 1,144Turkey* 595Venezuela* 1,166
Austria 2,313Bulgaria 33Canada 6,607Czech Republic 1,182France 9,945Greece* 356Japan 17,034Malaysia* 480Mexico 1,591Netherlands 2,941Nigeria 294Poland 1,209Romania 192Spain 6,322Switzerland* 3,330Taiwan 397Thailand 294U.A.E.* 1,137
Belgium 3,151Denmark 1,026Finland 1,375Germany 16,331Ireland 812Israel 622Italy 5,392Luxembourg 193Portugal 1,047S. Korea 1,797U.K. 16,617U.S. 133,766
Argentina 4,830Morocco 408Norway 1,594Sweden 203
China* 13,902Colombia* 1,365Ecuador* 616Indonesia* 1,721Mexico 3,631Morocco* 307Nigeria* 548Russia* 5,860Saudi Arabia* 1,121South Africa 4,289
Brazil* 7,745Bulgaria 210Greece 730Hong Kong 745India 2,223Luxembourg 277Malaysia 1,573Romania 456Singapore 744Spain 10,216Switzerland* 5,165Taiwan 1,266Turkey* 2,700U.A.E.* 1,106U.K. 25,132U.S. 191,255Venezuela* 1,059
Austria 3,455Belgium 3,307Canada 15,803Czech Republic 1,141Denmark 4,544Finland 1,682France 25,672Germany 23,030Ireland 1,216Israel 1,181Italy 7,206Japan 20,501Netherlands 4,943Norway 4,916Poland 1,868Portugal 978S. Korea 2,605Sweden 4,179
Argentina* 1,844Australia 11,598Chile 1,784New Zealand 2,287Thailand 1,503
China* 63,522Colombia* 1,982Ecuador* 593Indonesia* 1,196Nigeria* 294Singapore 974South Africa 4,535Thailand* 3,854Venezuela* 4,506
Austria 3,999Bulgaria 673Czech Republic 1,829Denmark 2,903Hong Kong 481Japan 57,290New Zealand 1,179Norway* 3,369Switzerland 6,449U.S. 206,817
Belgium 4,422Brazil* 14,209Canada 19,768Finland 2,051France 24,785Germany 30,671Greece 2,006Ireland 1,520Israel 2,401Italy 24,799Luxembourg 481Mexico 5,193Netherlands 5,483Poland 4,361Portugal 1,739Romania 1,281Russia 11,127S. Korea 8,896Spain 12,717Sweden 3,287Taiwan 2,254Turkey 4,475U.A.E.* 1,227U.K. 23,789
Argentina 5,160Australia 12,854Chile 1,181India 5,213Malaysia 2,388Morocco 931Saudi Arabia 1,695
Australia 34,097Chile* 3,708China 84,431Ecuador* 1,547Hong Kong 3,187Indonesia* 3,404Malaysia* 4,442Morocco 1,638Nigeria* 1,136Saudi Arabia* 3,067Singapore* 2,501South Africa 9,968Venezuela* 672
Austria 9,767Belgium 10,880Brazil* 23,647Bulgaria 917Canada 42,179Czech Republic 3,935Denmark 8,473France 66,918Germany 71,432Italy 37,397Mexico 10,415Norway 9,454Poland 7,439Portugal 4,165Russia* 19,199Spain 28,826Sweden 7,669Switzerland 14,682Taiwan 3,917 U.A.E.* 3,424
Finland 5,107Greece 2,840Ireland 3,548Israel 4,326Luxembourg 951Netherlands 13,366Romania 1,929S. Korea 13,298Turkey 7,770U.K. 65,538U.S. 531,838
Argentina 11,835Colombia 4,425India 9,200Japan 94,825New Zealand 3,886Thailand* 5,651
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Australia 11,644Brazil* 1,692Chile* 742China* 7,007Colombia* 1,078Ecuador* 337Hong Kong* 1,425India* 1,764Indonesia* 487New Zealand* 421Russia* 2,231Saudi Arabia 251Singapore* 784South Africa 1,144Turkey* 595Venezuela* 1,166
Austria 2,313Bulgaria 33Canada 6,607Czech Republic 1,182France 9,945Greece* 356Japan 17,034Malaysia* 480Mexico 1,591Netherlands 2,941Nigeria 294Poland 1,209Romania 192Spain 6,322Switzerland* 3,330Taiwan 397Thailand 294U.A.E.* 1,137
Belgium 3,151Denmark 1,026Finland 1,375Germany 16,331Ireland 812Israel 622Italy 5,392Luxembourg 193Portugal 1,047S. Korea 1,797U.K. 16,617U.S. 133,766
Argentina 4,830Morocco 408Norway 1,594Sweden 203
China* 13,902Colombia* 1,365Ecuador* 616Indonesia* 1,721Mexico 3,631Morocco* 307Nigeria* 548Russia* 5,860Saudi Arabia* 1,121South Africa 4,289
Brazil* 7,745Bulgaria 210Greece 730Hong Kong 745India 2,223Luxembourg 277Malaysia 1,573Romania 456Singapore 744Spain 10,216Switzerland* 5,165Taiwan 1,266Turkey* 2,700U.A.E.* 1,106U.K. 25,132U.S. 191,255Venezuela* 1,059
Austria 3,455Belgium 3,307Canada 15,803Czech Republic 1,141Denmark 4,544Finland 1,682France 25,672Germany 23,030Ireland 1,216Israel 1,181Italy 7,206Japan 20,501Netherlands 4,943Norway 4,916Poland 1,868Portugal 978S. Korea 2,605Sweden 4,179
Argentina* 1,844Australia 11,598Chile 1,784New Zealand 2,287Thailand 1,503
China* 63,522Colombia* 1,982Ecuador* 593Indonesia* 1,196Nigeria* 294Singapore 974South Africa 4,535Thailand* 3,854Venezuela* 4,506
Austria 3,999Bulgaria 673Czech Republic 1,829Denmark 2,903Hong Kong 481Japan 57,290New Zealand 1,179Norway* 3,369Switzerland 6,449U.S. 206,817
Belgium 4,422Brazil* 14,209Canada 19,768Finland 2,051France 24,785Germany 30,671Greece 2,006Ireland 1,520Israel 2,401Italy 24,799Luxembourg 481Mexico 5,193Netherlands 5,483Poland 4,361Portugal 1,739Romania 1,281Russia 11,127S. Korea 8,896Spain 12,717Sweden 3,287Taiwan 2,254Turkey 4,475U.A.E.* 1,227U.K. 23,789
Argentina 5,160Australia 12,854Chile 1,181India 5,213Malaysia 2,388Morocco 931Saudi Arabia 1,695
Australia 34,097Chile* 3,708China 84,431Ecuador* 1,547Hong Kong 3,187Indonesia* 3,404Malaysia* 4,442Morocco 1,638Nigeria* 1,136Saudi Arabia* 3,067Singapore* 2,501South Africa 9,968Venezuela* 672
Austria 9,767Belgium 10,880Brazil* 23,647Bulgaria 917Canada 42,179Czech Republic 3,935Denmark 8,473France 66,918Germany 71,432Italy 37,397Mexico 10,415Norway 9,454Poland 7,439Portugal 4,165Russia* 19,199Spain 28,826Sweden 7,669Switzerland 14,682Taiwan 3,917 U.A.E.* 3,424
Finland 5,107Greece 2,840Ireland 3,548Israel 4,326Luxembourg 951Netherlands 13,366Romania 1,929S. Korea 13,298Turkey 7,770U.K. 65,538U.S. 531,838
Argentina 11,835Colombia 4,425India 9,200Japan 94,825New Zealand 3,886Thailand* 5,651
* Indicates country was a high growth out performer in 2013 Insurance Risk Study Bold indicates country outperforms in all four quadrant plots.
Aon Benfield 9
Twenty countries are high growth, loss ratio outperformers
in at least one line of business . Of these twenty countries,
five appear in each of the lines of business analyzed
as high growth out performers: China, Colombia,
Ecuador, Indonesia, and South Africa . All but China and
South Africa were similarly distinguished last year .
If we compare these countries on the basis of overall combined
ratio, four of the five are outperformers globally . The exception
is Colombia, which underperforms its peers with a five year
net combined ratio of 108 .9 percent, driven by a higher than
average expense ratio . In addition to the four outperforming
countries mentioned above, nine additional countries
outperform the global averages for both growth
and profitability . Singapore, as an example, outperforms for
both motor and liability insurance, and with an all lines five year
combined ratio of 88 .9 percent, it has been a significantly
more profitable market than its overall Asia Pacific peer group .
(See the Top 50 P&C Markets table, page 7 for more details .)
Using combined ratio in addition to loss history allows us
to further analyze and target high growth opportunities .
Liability
Loss ratio performance
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Australia 11,644Brazil* 1,692Chile* 742China* 7,007Colombia* 1,078Ecuador* 337Hong Kong* 1,425India* 1,764Indonesia* 487New Zealand* 421Russia* 2,231Saudi Arabia 251Singapore* 784South Africa 1,144Turkey* 595Venezuela* 1,166
Austria 2,313Bulgaria 33Canada 6,607Czech Republic 1,182France 9,945Greece* 356Japan 17,034Malaysia* 480Mexico 1,591Netherlands 2,941Nigeria 294Poland 1,209Romania 192Spain 6,322Switzerland* 3,330Taiwan 397Thailand 294U.A.E.* 1,137
Belgium 3,151Denmark 1,026Finland 1,375Germany 16,331Ireland 812Israel 622Italy 5,392Luxembourg 193Portugal 1,047S. Korea 1,797U.K. 16,617U.S. 133,766
Argentina 4,830Morocco 408Norway 1,594Sweden 203
China* 13,902Colombia* 1,365Ecuador* 616Indonesia* 1,721Mexico 3,631Morocco* 307Nigeria* 548Russia* 5,860Saudi Arabia* 1,121South Africa 4,289
Brazil* 7,745Bulgaria 210Greece 730Hong Kong 745India 2,223Luxembourg 277Malaysia 1,573Romania 456Singapore 744Spain 10,216Switzerland* 5,165Taiwan 1,266Turkey* 2,700U.A.E.* 1,106U.K. 25,132U.S. 191,255Venezuela* 1,059
Austria 3,455Belgium 3,307Canada 15,803Czech Republic 1,141Denmark 4,544Finland 1,682France 25,672Germany 23,030Ireland 1,216Israel 1,181Italy 7,206Japan 20,501Netherlands 4,943Norway 4,916Poland 1,868Portugal 978S. Korea 2,605Sweden 4,179
Argentina* 1,844Australia 11,598Chile 1,784New Zealand 2,287Thailand 1,503
China* 63,522Colombia* 1,982Ecuador* 593Indonesia* 1,196Nigeria* 294Singapore 974South Africa 4,535Thailand* 3,854Venezuela* 4,506
Austria 3,999Bulgaria 673Czech Republic 1,829Denmark 2,903Hong Kong 481Japan 57,290New Zealand 1,179Norway* 3,369Switzerland 6,449U.S. 206,817
Belgium 4,422Brazil* 14,209Canada 19,768Finland 2,051France 24,785Germany 30,671Greece 2,006Ireland 1,520Israel 2,401Italy 24,799Luxembourg 481Mexico 5,193Netherlands 5,483Poland 4,361Portugal 1,739Romania 1,281Russia 11,127S. Korea 8,896Spain 12,717Sweden 3,287Taiwan 2,254Turkey 4,475U.A.E.* 1,227U.K. 23,789
Argentina 5,160Australia 12,854Chile 1,181India 5,213Malaysia 2,388Morocco 931Saudi Arabia 1,695
Australia 34,097Chile* 3,708China 84,431Ecuador* 1,547Hong Kong 3,187Indonesia* 3,404Malaysia* 4,442Morocco 1,638Nigeria* 1,136Saudi Arabia* 3,067Singapore* 2,501South Africa 9,968Venezuela* 672
Austria 9,767Belgium 10,880Brazil* 23,647Bulgaria 917Canada 42,179Czech Republic 3,935Denmark 8,473France 66,918Germany 71,432Italy 37,397Mexico 10,415Norway 9,454Poland 7,439Portugal 4,165Russia* 19,199Spain 28,826Sweden 7,669Switzerland 14,682Taiwan 3,917 U.A.E.* 3,424
Finland 5,107Greece 2,840Ireland 3,548Israel 4,326Luxembourg 951Netherlands 13,366Romania 1,929S. Korea 13,298Turkey 7,770U.K. 65,538U.S. 531,838
Argentina 11,835Colombia 4,425India 9,200Japan 94,825New Zealand 3,886Thailand* 5,651
All Lines
Combined ratio performance
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Lowgrowth
Highgrowth
Out performers
Under performers
Australia 11,644Brazil* 1,692Chile* 742China* 7,007Colombia* 1,078Ecuador* 337Hong Kong* 1,425India* 1,764Indonesia* 487New Zealand* 421Russia* 2,231Saudi Arabia 251Singapore* 784South Africa 1,144Turkey* 595Venezuela* 1,166
Austria 2,313Bulgaria 33Canada 6,607Czech Republic 1,182France 9,945Greece* 356Japan 17,034Malaysia* 480Mexico 1,591Netherlands 2,941Nigeria 294Poland 1,209Romania 192Spain 6,322Switzerland* 3,330Taiwan 397Thailand 294U.A.E.* 1,137
Belgium 3,151Denmark 1,026Finland 1,375Germany 16,331Ireland 812Israel 622Italy 5,392Luxembourg 193Portugal 1,047S. Korea 1,797U.K. 16,617U.S. 133,766
Argentina 4,830Morocco 408Norway 1,594Sweden 203
China* 13,902Colombia* 1,365Ecuador* 616Indonesia* 1,721Mexico 3,631Morocco* 307Nigeria* 548Russia* 5,860Saudi Arabia* 1,121South Africa 4,289
Brazil* 7,745Bulgaria 210Greece 730Hong Kong 745India 2,223Luxembourg 277Malaysia 1,573Romania 456Singapore 744Spain 10,216Switzerland* 5,165Taiwan 1,266Turkey* 2,700U.A.E.* 1,106U.K. 25,132U.S. 191,255Venezuela* 1,059
Austria 3,455Belgium 3,307Canada 15,803Czech Republic 1,141Denmark 4,544Finland 1,682France 25,672Germany 23,030Ireland 1,216Israel 1,181Italy 7,206Japan 20,501Netherlands 4,943Norway 4,916Poland 1,868Portugal 978S. Korea 2,605Sweden 4,179
Argentina* 1,844Australia 11,598Chile 1,784New Zealand 2,287Thailand 1,503
China* 63,522Colombia* 1,982Ecuador* 593Indonesia* 1,196Nigeria* 294Singapore 974South Africa 4,535Thailand* 3,854Venezuela* 4,506
Austria 3,999Bulgaria 673Czech Republic 1,829Denmark 2,903Hong Kong 481Japan 57,290New Zealand 1,179Norway* 3,369Switzerland 6,449U.S. 206,817
Belgium 4,422Brazil* 14,209Canada 19,768Finland 2,051France 24,785Germany 30,671Greece 2,006Ireland 1,520Israel 2,401Italy 24,799Luxembourg 481Mexico 5,193Netherlands 5,483Poland 4,361Portugal 1,739Romania 1,281Russia 11,127S. Korea 8,896Spain 12,717Sweden 3,287Taiwan 2,254Turkey 4,475U.A.E.* 1,227U.K. 23,789
Argentina 5,160Australia 12,854Chile 1,181India 5,213Malaysia 2,388Morocco 931Saudi Arabia 1,695
Australia 34,097Chile* 3,708China 84,431Ecuador* 1,547Hong Kong 3,187Indonesia* 3,404Malaysia* 4,442Morocco 1,638Nigeria* 1,136Saudi Arabia* 3,067Singapore* 2,501South Africa 9,968Venezuela* 672
Austria 9,767Belgium 10,880Brazil* 23,647Bulgaria 917Canada 42,179Czech Republic 3,935Denmark 8,473France 66,918Germany 71,432Italy 37,397Mexico 10,415Norway 9,454Poland 7,439Portugal 4,165Russia* 19,199Spain 28,826Sweden 7,669Switzerland 14,682Taiwan 3,917 U.A.E.* 3,424
Finland 5,107Greece 2,840Ireland 3,548Israel 4,326Luxembourg 951Netherlands 13,366Romania 1,929S. Korea 13,298Turkey 7,770U.K. 65,538U.S. 531,838
Argentina 11,835Colombia 4,425India 9,200Japan 94,825New Zealand 3,886Thailand* 5,651
10 Insurance Risk Study
For the growth-seeking insurance enterprise, an analysis of historical growth trends and relative profitability will provide a good indication of where to initially target opportunities . However, the key is to be able to understand what is driving the trends and how they might change over the near term, and what these changes may mean for an evolving global insurance marketplace .
We have projected global property casualty insurance premium
growth for the next five years, for the overall insurance market,
and for motor, property, and liability . These projections
are based on a weighting of historic premium growth rates
with projected country GDP and population estimates .
By 2018, we expect the global insurance market to grow by
18 percent to a total direct written premium of USD1 .6 trillion .
Motor insurance will remain the largest property casualty
segment, accounting for 47 percent of total direct written
premium, followed by property (33 percent) and liability
(21 percent) .
The United States will remain the largest property casualty
insurance market, representing an estimated 37 percent
of global premium . China will surpass Japan to become
the second largest market, with an expected 9 percent of
premium . But note that the U .S . projected annual growth
rate is 2 .7 percent while China’s is over 11 percent .
Digging deeper into each line reveals similar trends . In each
line the U .S . will remain the largest property casualty insurance
market, but with relatively limited growth prospects .
Global 2018 premium projections
2013 2018 Projected Projected annual growth %
Country Rank DWP (USD B) Rank DWP (USD B)
United States 1 531 .8 1 607 .8 2 .7%
China* 3 84 .5 2 143 .7 11 .2%
Japan* 2 92 .4 3 108 .8 2 .8%
Germany 4 73 .7 4 81 .9 2 .1%
France 5 69 .3 5 74 .8 1 .5%
China will become the second largest insurance market in the world by 2018 and account for over 10% of global DWP *2013 DWP unavailable; 2012 used as proxy
Motor47%
2018 projected premium mixProjected direct written premium by line
Total DWP: $1,627 Total Growth: 18%
Liability21%
Property33%
0
100
200
300
400
500
600
700
800
MotorLiabilityProperty
453
17%
529
296341
633
757
2013 2018 2013 2018 2013 2018
20%
15%
Looking Ahead: Growth Projections
Aon Benfield 11
Motor Motor, which accounts for USD633 billion of global premium
today, will experience continued rapid growth with a
20 percent five year rate increasing to USD757 billion of direct
written premium . Such projections are easy to understand,
given that we expect continued strong population growth,
particularly in developing markets—and an early sign of middle
class life is owning a car, usually with auto insurance as a
compulsory addition .
China is already the second largest auto market, and will almost
certainly retain this position given its projected 11 .3 percent
annual growth . Yet we must also express a note of caution: the
widely expected partial de-tariffing of China motor business
later this year has the potential to shake-up the world’s fastest
growing insurance market . Companies are struggling with
the data and modeling implications of the change, as well
as the potential market reaction to new pricing flexibility . An
extremely competitive market reaction could lower the growth
rate through an adjustment period . Long term growth that is
driven by economic fundamentals is, however, unlikely to be
significantly impacted .
We project no change in rank amongst the top five global motor
markets . Despite more limited population growth, wealth
generation continues in these countries at a rapid pace, with
more families owning multiple cars, supporting continued
steady growth; developing countries have a long way to go to
catch up with motor penetration in these top markets .
Later in the Study we discuss the changing dynamics of the
U .S . motor insurance market . We do anticipate similar changes
globally, but further off in the future; the technologies gaining
momentum in the U .S . will be slower to make their way into the
developing markets . As such, despite slow projected growth in
the U .S ., global growth will remain strong .
Motor 2018 premium projections
2013 2018 Projected Projected annual growth %
Country Rank DWP (USD B) Rank DWP (UD B)
United States 1 206 .8 1 234 .0 2 .5
China* 2 63 .5 2 108 .4 11 .3
Japan* 3 57 .3 3 66 .3 3 .0
Germany 4 30 .7 4 33 .8 2 .0
France 5 26 .3 5 28 .8 1 .9
*2013 DWP unavailable; 2012 used as proxy
0%
2%
4%
6%
8%
10%
12%
Argentin
a
ColombiaChile
Indonesia
Thail
and
Ecuad
or
Malaysi
aIndia
Saudi A
rabia
China
108.4 2.3 5.40.83.47.5 1.7 1.6 6.72.6 796
U.S.
2018 est. premiumfor country
Middle East & Africa
Rest of Europe
Rest of Euro AreaU.K.
GermanyFrance
Rest of APAC
South Korea
Japan
China
Rest of Americas
CanadaBrazil
2018 projected premium: USD757 billionProjected direct written premium by country
12 Insurance Risk Study
PropertyChina is by far the largest of the rapidly growing property
markets in the world with an 8 percent expected growth rate,
representing nearly USD20 .5 billion of direct written premium,
which will tie China with Japan for the fifth largest property
market in five years .
Many other countries with high expected premium growth
currently have a relatively small premium base . Thailand, for
instance, has nearly 9 percent executed annual growth but
only USD2 .3 billion of projected property premium . When
determining where and how to grow, companies must balance
the growth opportunity against the total market opportunity .
Catastrophe risk potential is another important consideration
for property lines . Economic growth and urbanization are
creating greater risk concentrations, often in catastrophe
exposed areas . Property premium growth is driven, in part,
by catastrophe losses—both actual and potential . Aon Benfield
can use its understanding of global catastrophe risk to produce
an optimal blending of target growth and acceptable risk .
Property 2018 premium projections
2013 2018 Projected Projected annual growth %
Country Rank DWP (USD B) Rank DWP (USD B)
United States 1 191 .3 1 223 .8 3 .2
Germany 2 26 .7 2 30 .3 2 .5
United Kingdom 3 25 .1 3 27 .9 2 .1
France 4 24 .8 4 26 .4 1 .2
Japan* 5 20 .5 5 23 .4 2 .6
*2013 DWP unavailable; 2012 used as proxy
0%
2%
4%
6%
8%
10%
2018 projected premium: USD529 billion Projected growth rate by country
2.3 0.9 2.13.31.920.5 1.5 2.9 0.74.7 796
U.S.
Middle East & Africa
Rest of Europe
Rest of Euro Area
U.K.
Germany
France
Rest of APACSouth Korea
JapanChina
Rest of AmericasCanada
Brazil
Nigeria
Mexico
India
Saudi A
rabia
Malaysi
a
New Zeal
and
Hong KongChina
Ecuad
or
Thail
and
2018 est. premiumfor country
Aon Benfield 13
LiabilityLiability insurance is the smallest of the global property
casualty segments, at approximately half the size of the global
motor insurance market . The U .S . will remain the largest
market by a wide margin, and with Japan, will grow faster than
other top five markets .
China is the fastest growing market for liability
with 16 percent projected annual growth—though
this will not yet make it a top five market .
Liability 2018 premium projections
2013 2018 Projected Projected annual growth %
Country Rank DWP (USD B) Rank DWP (USD B)
United States 1 133 .8 1 150 .0 2 .3
France 2 18 .1 2 19 .6 1 .6
Japan* 3 17 .0 3 19 .1 2 .3
United Kingdom 4 16 .6 4 18 .2 1 .9
Germany 5 16 .3 5 17 .7 1 .7
*2013 DWP unavailable; 2012 used as proxy
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
2018 projected premium: USD341 billionProjected growth rate by country
14.8 0.71.60.57.3 2.5 2.0 0.60.30.7 796
U.S.
Middle East& Africa
Rest of Europe Rest of Euro Area
U.K.
Germany
France
Rest of APAC
South Korea
Japan
China
Rest of AmericasCanada
Brazil
New Zeal
and
Saudi A
rabia
Malaysi
a
Hong KongIndia
Indonesia
Colombia
Ecuad
or
Argentin
aChina
2018 est. premiumfor country
14 Insurance Risk Study
ReinsuranceGlobal reinsurance premium by year (USD billions)
Our analysis so far has focused on primary insurance
direct written premium growth without considering the
impact of reinsurance . This approach is largely due to
data availability: reinsurance data is much more limited
and often distorted by the reporting of intercompany
reinsurance within global insurance conglomerates .
Aon Benfield has worked through the available data to
estimate the size of the property casualty global reinsurance
market and project it five years forward . As of year end
2013, we believe total global ceded written premium is
approximately USD170 billion, excluding intercompany
reinsurance and other mandatory pools . This amount
represents the total opportunity for reinsurers .
We do not expect the reinsurance market to grow as rapidly
as the primary market . Excess capital in the insurance market
will allow companies to retain much of their expected
growth, and excess capital in the reinsurance market will
pressure rates so ceded premium will not necessarily
reflect growth in exposures . The influx of alternative capital
could have a positive or a negative impact on reinsurance
premium growth depending on price elasticity . Hedge fund
reinsurers are bringing new capacity to reinsurance markets,
often pricing to a break-even underwriting profit while
expecting to make significant returns on assets . Whether such
changes will serve to stimulate new reinsurance demand,
or merely to further depress prices, remains to be seen .
Aon Benfield projects five year growth of approximately
2 .1 percent per year for the global reinsurance market .
By country, reinsurance growth estimates vary from 5 percent
annual growth in South Korea to negative growth in Japan .
On average, we expect the mature reinsurance markets to
grow about 2 percent per year while developing markets will
grow 5 to 7 percent per year . In China, the impact of the new
C-ROSS capital standards could have a negative impact on
ceded premium in the medium term . The new standards lower
the capital requirements for writing motor business, and at the
same time, decrease the capital efficiency of certain cessions .
The new standards are expected to run in parallel with the
current approach in 2015 and to be fully adopted in 2016 .
Given these dynamics, reinsurance companies are seeking
out new growth opportunities, as growth certainly will not
follow from continued rate reductions . The key to future
growth will be innovation coupled with hard data . While
capital remains plentiful, primary insurers’ growth will
not broadly translate into reinsurance growth . Reinsurers
must develop value propositions and seek partnership
opportunities to help primary insurers grow into new markets
and in new ways that they could not do by themselves .
0
45
90
135
180
20132012201120102009200820072006200520042003
147 142 142 142153
137 140 145155
165170
Aon Benfield 15
Last year Aon Benfield introduced to the Study a detailed screening process that we employ to identify potential markets worth exploring for realistic growth opportunities .
Our analysis entails an evaluation of basic insurance economics,
such as country market scale and insurance penetration, country
profitability and loss ratio volatility, and overall out or under
performance relative to other opportunities . We couple those
basics with an understanding of the larger macroeconomic
environment including population changes, GDP measures,
and an understanding of the legal and regulatory systems .
Finally by combining this fact based information with qualitative
feedback from Aon’s local teams we can identify attractive
opportunities in each country . Our process reveals specific
opportunities from which to form executable growth strategies .
Aon Benfield has expanded the analysis this year by introducing
five year projected insurance premiums in each country,
which we have added to the Country Opportunity Index
on the next page . We have selected and analyzed several
specific trends that we are seeing in the market, including
auto, health, crop, and cyber insurance . Based on our
experience with carriers, reinsurers, agents, brokers, and other
insurance service providers, we highlight some of the key
trends and emerging insurance opportunities in each area .
Profitability
Scale
Growth
Demographics
Political Stability
Regulation
Broker Surveys
Contender Geographies Deep dive on strategy, organic growth and M&A opportunities
Uncovering Growth Opportunities
16 Insurance Risk Study
Country opportunity indexTo summarize and sort between the various countries
outlined in this section, we have updated our
Country Opportunity Index . The index identifies
countries with a desirable mix of profitability,
growth potential and a relatively stable political
environment . For growth potential we used the
projections shown on pages 10 to 14 of the Study .
The table displays the top 50 P&C markets ranked
by the Index and divided into quartiles .
Ten of the thirteen countries in Quartile 1 were also
in the top quartile last year . Singapore fell from the
top spot last year to the number three position, as
we estimated its projected premium growth to be
less than its recent history . Saudi Arabia is now the
top country according to our Index, with a recent
combined ratio of 91 .5 percent, strong projected
growth of 8 .1 percent, and only modest political risk .
The new entrants to the top quartile are all in
Asia Pacific: Hong Kong, China, and Australia .
China showed the biggest overall increase on the
Index, driven by a combined ratio improvement
from 101 .7 percent down to 98 .7 percent .
Geography is one factor when considering a growth
strategy . Another is opportunities created by
advances in insurance products . The next section of
the Study will delve into several insurance markets
where we see significant changes at work—and
with them, significant opportunity for insurers to
differentiate and create value for their clients .
Aon Benfield country opportunity index
5yr Cumulative Net Combined
Ratio
5yr Annualized Projected
Growth RatePolitical Risk Assessment
Quartile 1
Saudi Arabia* 91.5% 8.1% Medium Low
Ecuador* 86.9% 7.8% High
Singapore* 88.9% 4.6% Low
Hong Kong 98.6% 7.0% Low
Malaysia* 90.7% 6.8% Medium
Indonesia* 88.2% 5.7% Medium
Nigeria* 80.2% 4.4% Medium High
China 98.7% 11.2% Medium
Chile* 95.6% 5.5% Medium Low
South Africa* 87.6% 4.4% Medium
Norway* 89.6% 2.9% Low
Brazil* 85.7% 3.7% Medium
Australia 99.7% 4.4% Low
Quartile 2
Switzerland* 97.1% 3.2% Low
United Arab Emirates* 88.1% 2.5% Medium Low
Thailand 101.0% 7.3% Medium
Sweden* 91.1% 2.4% Low
Taiwan 97.2% 3.3% Medium Low
Mexico 96.9% 4.2% Medium
New Zealand 120.5% 6.7% Low
Morocco 97.8% 4.4% Medium High
Canada 99.0% 2.9% Low
India 118.5% 7.0% Medium
Denmark 93.5% 1.6% Low
Argentina 104.8% 6.4% High
Quartile 3
Poland 95.8% 2.2% Medium Low
South Korea 101.1% 3.7% Medium Low
Russia 88.7% 1.7% Medium
Finland 100.6% 2.8% Low
Colombia 108.9% 6.1% Medium
Israel 108.7% 3.9% Medium Low
Luxembourg 101.9% 2.8% Low
Germany 99.0% 2.1% Low
Austria 99.0% 1.8% Low
United States 101.9% 2.7% Low
Bulgaria 90.3% 0.9% Medium
Japan 103.5% 2.8% Medium Low
Quartile 4
Czech Republic 91.0% 0.2% Medium Low
France 98.9% 1.5% Medium Low
Turkey 105.1% 3.1% Medium
United Kingdom 101.2% 2.3% Medium Low
Belgium 99.0% 1.6% Medium Low
Portugal 92.7% 0.6% Medium
Venezuela* 96.9% 1.3% High
Spain 92.5% -0.1% Medium
Netherlands 101.0% 0.8% Low
Italy 98.8% 0.4% Medium
Greece 100.7% 0.9% High
Ireland 101.0% 0.5% Medium
Romania 111.8% 1.1% Medium High
*Indicates top quartile performer in 2013. Index defined in Sources and Notes.
Aon Benfield 17
The insurance industry is evolving rapidly . We are witnessing long term shifts that are changing the risks that property casualty companies insure . Cars are becoming safer . Employers face rapidly rising health care costs . Hardly a week goes without news of a new cyber attack . Advances in modeling are facilitating the growth of the international crop insurance market . And technology is posing new opportunities and risks for individuals and businesses . As the world is becoming more connected, it is also becoming riskier . These shifts present challenges, but also opportunities for insurers .
Auto trendsPersonal auto insurance, which for many years has been the
stable cash flow product of the property casualty universe,
is currently undergoing a revolution due to advances in
technology .
Cars today are significantly safer than those that our parents
drove . The Economist reports that 90 percent of car crashes
are caused by human error . As a result, recent innovations
in vehicle safety have focused on mitigating the effects of
human error or negligence . The results speak for themselves:
the U .S . has seen a 15 percent reduction in crashes for cars
with an automatic braking system for example . Between
2000 and 2011, driver deaths due to rollover crashes have
fallen more than 50 percent for passenger cars . And for
SUVs, the death rate has fallen roughly 90 percent .
People in large metropolitan areas are changing the way
they get around, from drive share programs to semi-
private car services such as Uber and Lyft . This is forcing
the auto insurance industry to think about how to create
and better price policies for uberX and Lyft drivers, who
need a commercial policy when they have passengers and
personal policies when they do not . Recent incidents have
posed questions about how these policies overlap .
Telematics and usage-based insurance (UBI) are becoming
widespread across the industry, with many of the largest U .S .
and U .K . auto insurers now having some form of UBI . Insurers
believe UBI will allow them to better segment price and risks
accordingly . Good drivers should benefit, as in theory drivers
who opt for UBI will pay less while other drivers’ rates will
increase . While the potential discount varies by carrier and
driver, the average quoted is 30 percent . Smaller insurers are
struggling to enter the UBI market, as they lack the scale to
offset the up-front investment in telematics infrastructure .
Companies are seeking ways to better leverage the data they
have accumulated from UBI . Two commonly cited applications
are teen driving and commercial trucking monitoring . And the
data accumulated from UBI may not only help to sell additional
insurance products, but may also be monetized by companies
outside the insurance industry . While vast amounts of data exist,
companies are only beginning to understand its full value .
The insurers that have been successful in growing are doing
so with data . Through market segmentation and targeted
advertising, auto specialist insurers in the U .S . have expanded
their market shares—growing at an average annual rate of 7
percent—while traditional personal lines insurers’ premium
has on average been static over the past five years .
Looking further ahead, driverless cars have the potential to
radically change the business model for auto insurers . Personal
auto insurance is 45 percent of global premium, and it has
long provided ballast and stability for multiline insurers . An
insurance world without this ballast would have very different
risk dynamics . For example, we estimate that without personal
auto, loss ratio volatility for the U .S . market would have been
nearly 40 percent higher for the period 1995 to 2013 .
Such changes, while not on the immediate horizon, could
increase industry capital intensity and lower premium to
surplus ratios by more than 30 percentage points, from
0 .84x to 0 .50x . We estimate that surplus needed in the
U .S . to support personal auto is USD100 to 125 billion .
The changing dynamics of the auto industry do not
foreshadow the death of the auto insurance industry but
do represent a clear emerging risk . Insurers need to keep
pace with the changes and innovate accordingly .
Insurance Trends: Risks and Opportunities
18 Insurance Risk Study
U.S. health insurance marketIn 2014, the individual mandate of the Affordable Care Act—
aka “Obamacare”—came into effect . With the ACA, state-run
public health care exchanges have become operational, and
as of May 2014 approximately 20 million Americans have
purchased insurance through these public exchanges . At
the same time, and with much less controversy, a revolution
has been taking place in the private health care insurance
market—the advent of corporate health care exchanges .
Aon Hewitt has been a pioneer in this market, with 330,000
employees enrolled in its Corporate Health Exchange for 2014 .
Currently, about 60 percent of U .S . workers who receive
health insurance through their employers are covered under
self-insured plans . For companies with over 5,000 employees
this number is even higher—by some estimates as many as
94 percent of larger employers run self-insured health plans .
In these cases, the role of the health insurer is simply to
process payments and bill claims back to the employer—hence
these plans are called Administrative Services Only plans .
But over the past several years, several significant
developments in the industry have begun to change how
people buy health insurance and increase the flow of
insurance premiums into the market . These changes are a
real and material opportunity for the insurance industry .
Health care costs have risen at a 7 percent annual rate during
the 10 years to 2012, with long term trends estimated at
8 to 9 percent per year . At most companies, revenue growth
has not kept pace with this expansion in costs . Given these
trends, companies are seeking ways to manage costs while
continuing to provide essential benefits to their employees .
One such way is to rethink the traditional model of a self-
insured health plan . This trend has led to the creation of private
health care exchanges . Under this model, companies enroll
in a private exchange, which allows insurance companies
to compete for their employees’ health care insurance
business . Insurers bear the risk from these policies .
The private exchange market is still small; analysts at JP Morgan
estimate that less than 1 percent of active employees will be
enrolled in private exchanges in 2014 . Yet interest is high,
with an average of 40 percent of employers in recent surveys
saying they are considering a switch to a private exchange .
The implications for growth in the private health care insurance
market are significant . We estimate that if 20 percent of U .S .
employers move to private exchanges, then an additional
USD350 billion in premium will flow into the private health
insurance market . Twenty percent is the minimum level
of interest quoted in recent surveys . The median is 33
percent—if one in three U .S . employers move to a private
exchange, this will generate an additional USD500 billion
of premium flow into the market . As a reference point, this
number is roughly the size of the entire U .S . property casualty
insurance market, as shown in the statistics on page seven .
While the potential premium growth can seem staggering,
insurers must also consider the capital required to support
this growth . We estimate that the U .S . health insurance
industry’s capital adequacy, as defined by A .M . Best’s BCAR
model, is currently 225 percent—roughly in line with the
U .S . property casualty industry’s 230 percent . Depending
on how much premium flows into private exchanges,
we estimate that health insurers’ capital adequacy could
fall between 107 and 128 percent if capital levels remain
constant . To maintain a 225 percent capital adequacy level,
insurers will need to raise a significant new level of capital:
USD105 billion at the minimum, and USD150 billion at the
median . The table below summarizes these estimates .
Impact of private exchanges on health insurers
% of Employers Moving to Corporate Exchanges
New health insurance premium
(USD billions)
Additional required capital to maintain BCAR
(USD billions)
20% (minimum) 350 105
33% (median) 500 150
A capital demand of USD105 billion to USD150 billion is a
significant opportunity not only for investors but also for
property casualty insurers that are currently sitting on record
levels of capital and actively seeking new opportunities in
which to deploy it . For traditional property casualty insurers,
it is an opportunity to diversify into new lines of (potentially
uncorrelated) business . For reinsurers it is an opportunity as
well . Reinsurance can provide a substitute to traditional capital
and help health insurers lower their capital requirements by
sharing risk with the reinsurers . The U .S . group health insurance
market has only three insurers who are truly national in scope,
so a significant amount of the “new” commercial premium could
fall to regional carriers who are bigger users of reinsurance .
Aon Benfield 19
Earlier, we mentioned the potential market changes that could
take place if driverless cars cause the personal auto insurance
market to shrink . Perhaps health insurance will become the
new “ballast” to property casualty commercial lines volatility
in the future .
U.S. cyber insurance marketIn the past year, cyber risk has come into the mainstream as a
significant threat to businesses of all sizes . The data breach at
Target affected as many as one-third of all U .S . consumers, and the
Heartbleed bug exposed weaknesses in 17 percent (500,000) of
the internet’s secure web servers . Both the frequency and severity
of cyber attacks are on the rise . Attitudes are changing; businesses
now see a data breach as inevitable: not if but when .
Different sources count data breaches differently but all agree
there is an increasing trend . Symantec released a study counting
253 breaches, a 62 percent increase over 2012 . The Identity
Theft Resource Center counted 614 data breaches last year,
rising at an annual trend rate of 11 percent, as shown below .
Number of U.S. data breaches by year
All studies suggest that 2013 was a banner year, of sorts, for data
breaches . Notably, 2013 saw eight mega breaches, each more
than 10 million records; the previous high was the five breaches
in 2011, according to Symantec . In total, 552 million identities
were exposed—roughly 7 .8 percent of the world population .
And while breaches are increasing, the cost of a breach is
increasing as well . Data from the Ponemon Institute suggest
that the cost of the average breach is now USD5 .9 million—and
this number excludes breaches of more than 100,000 records .
The Ponemon study also indicates that customers are fleeing
from breached companies more than in the past: lost business
following a breach rose 15 percent last year .
Finally, the Ponemon study included a shocking statistic: that
roughly 19 percent of businesses are expected to have a data
breach in the next 24 months . These percentages vary by
industry, but every company in today’s economy is vulnerable to
the risks of a cyber attack .
Average total cost of a data breach (USD millions)
Excludes breaches with more than 100,000 records
From its beginnings 15 years ago, cyber insurance has
now become a standard product offered by many large
commercial insurers . Common coverage includes third-
party liability protection as well as first-party indemnity
protection for breach response expenses, business
interruption, forensics, and cyber extortion . Although
statistics on the business are difficult to come by, cyber
insurance has generally been seen as profitable . That said,
a growing number of entrants are offering the coverage,
and prices are beginning to fall as competition expands .
Takeup of cyber insurance is increasing, and the U .S . cyber
market is now estimated at roughly USD1 .5 billion in gross
written premium . Aon Risk Solutions has seen cyber premium
rise at a compound annual growth rate of 38 percent over the
last five years, according to data from the Aon GRIP platform .
Nearly one-third of companies buy some kind of cyber policy .
Main Street businesses have been slower to adapt than large
corporations . This presents a significant market opportunity for
enterprising insurers, given that small and medium enterprises
are often the most vulnerable to a cyber attack . A study by
Verizon found that 71 percent of cyber attacks are targeted
at companies with fewer than 100 employees . Moreover,
attacks against small businesses shot up by 91 percent in
2013 . Small businesses often lack the time and resources to
develop sophisticated cyber risk management strategies .
0
200
400
600
800
201320122011201020092008200720062005
0
2
4
6
8
201420132012201120102009200820072006
+11% trend
+3% trend
20 Insurance Risk Study
Many smaller businesses are responding to such limitations by
outsourcing their network security to managed security service
providers (MSSPs) . While MSSPs can provide valuable services
to help companies protect themselves, they are not insurers .
Insurers have a vital role to play, by providing indemnity
protection as well as sharing their security expertise in this area .
Current cyber insurance policies only provide basic protection .
Cyber insurance for large companies has focused primarily
on first party indemnity protection . This is not surprising,
given that since 2004 companies have been required to
notify customers in the event their personal information is
compromised—and the costs of doing so can be considerable .
Yet the potential for other kinds of expense is significant .
Increasingly, lawyers are pursuing directors and officers in the
event that a company fails to protect its data . Target’s data
breach has generated at least 40 lawsuits against the retailer .
Moreover, the current cyber insurance policies focus solely on
the direct costs and ramifications of a data breach . They do not
contemplate the risk a cyber attack can cause other kinds of
damage . Most property and general liability policies exclude
cyber risk . The first cyber difference-in-conditions policy to fill
such coverage gaps was just made available earlier this year .
For Main Street, the coverage options can be confusing,
and risk leaving the buyer exposed should an actual event
occur . Many insurers will include data breach coverage in
their business owners policies but subject to a USD10,000 or
USD25,000 limit . Given the size of the potential costs discussed
previously, such coverage limits are very low, and may create
a false sense of security that businesses are “covered .”
Cyber coverage must evolve in order to meet the needs of
buyers, and underwriting practices will need to evolve with it .
Cyber underwriting is currently focused more on compliance
with industry standard practices than on actual risk assessment .
And cyber risk still has an image challenge to overcome: often, it
is seen by companies merely as an IT problem, not tied into the
larger ERM framework . This suggests a failure by corporate risk
managers to translate cyber exposure into a potential bottom-
line impact that executives can understand and manage .
China crop insuranceGlobal population growth and emerging middle classes are
driving a rising demand for agricultural products including
those used for animal feed .
China is the second largest crop insurance market globally,
with USD3 billion premium of a global USD22 billion market .
The U .S . market is much larger, and fairly mature . The China
market, in contrast, is primed for growth . China’s population
is expected to grow by 4 percent by 2017 totaling nearly 1 .4
billion people . Even more impressive, GDP is expected to grow
by 50 percent by 2018 . These significant expectations, coupled
with the Chinese government’s focus on providing government
support to the agricultural industry and rural population,
warrant attention when considering growth opportunities .
Global crop insurance premium (USD billions)
0
2
4
6
8
10
12
14
Latin AmericaIndiaCanadaEuropeChinaUSA
Aon Benfield 21
Since 2004, China crop premium has quadrupled, from
RMB5 billion in 2007 to RMB20 billion in 2012 . The business
has also been relatively profitable, with an average 63 percent
loss ratio over these years . Considering China’s trajectory, we
expect the crop insurance market to continue growing quickly .
The size of the crop insurance opportunity and the number
of players in the market are simultaneously increasing . In the
last ten years, four new specialized agricultural insurance
companies were established in China that now collectively
write more than a quarter of the market . This growth has
not come without resistance: the largest crop insurers in
the market have been active for almost 30 years, and have
worked to limit competition and protect their market share .
For carriers seeking to enter this market, Aon Benfield can
provide detailed and data-driven support to help navigate the
vast and dynamic China agricultural landscape . The Aon Crop
Reinsurance System (ACReS) is built on 30+ years of county-level
yield data at the crop level, 60 years of city and provincial level
data and weather data from 160 weather stations . It is the only
model in the market built at this level of granularity . The ACReS
model provides PMLs per province for an insurer’s major crop
portfolio . It also incorporates correlation coefficients between
provinces so that we can model multi-province portfolios .
China premium and claims performance
Additionally, ACReS is a tool to help insurers grow
strategically in China, because it identifies the varying risk
by region and allows insurers to select those provinces
with an acceptable risk level for future growth . The model
demonstrates the effect of changes in policy terms or exposure
levels on underwriting results as well as the efficiency
and adequacy of various reinsurance arrangements .
Prem
ium
/cla
ims
(RM
B b
illio
ns)
Loss ratio
ClaimsLoss ratio Premium
0
5
10
15
20
201120102009200820072006200540%
50%
60%
70%
80%
22 Insurance Risk Study
The first part of the Study focused on insurance premium, profitability, and growth opportunities . Once insurers have set a strategy and identified opportunities for growth, they must address the tactical matters of operations: underwriting, claims and risk management . Insurance is a tradeoff between risk and potential return, and we now turn to the “risk” side of the equation . Measuring the volatility and correlation of risk has always been the core of the Study .
The 2014 edition of the Study quantifies the systemic risk by
line for 49 countries worldwide . By systemic risk, or volatility,
we mean the coefficient of variation of loss ratio for a large
book of business . Coefficient of variation (CV) is a commonly
used normalized measure of risk defined as the standard
deviation divided by the mean . Systemic risk typically comes
from non-diversifiable risk sources such as changing market rate
adequacy, unknown prospective frequency and severity 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 facing 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 .
Global Risk Parameters
Coefficient of variation of gross loss ratio by country
ThailandSingapore
MexicoPeru
PhilippinesGreece
Hong KongBrazil
IndonesiaTaiwan
Dominican RepublicPakistanVietnamRomania
NicaraguaPanamaSlovakia
ArgentinaU.S.
South KoreaHondurasColombia
UruguayEl Salvador
TurkeyPoland
HungaryEcuador
IndiaChile
MalaysiaJapanChina
FranceSwitzerland
SpainCanada
U.K.IsraelItaly
AustriaBolivia
AustraliaGermany
South AfricaNetherlands
DenmarkVenezuela
PhilippinesGreece
Hong KongRomania
NicaraguaPanama
South AfricaSlovakia
IndonesiaDenmarkEcuador
VenezuelaSingapore
TurkeyColombia
ChinaU.S.
CanadaHonduras
PakistanPoland
ArgentinaDominican Republic
U.K.BrazilPeru
VietnamIndiaItaly
UruguayMexico
ChileMalaysia
NetherlandsEl Salvador
Czech RepublicGermany
AustriaBolivia
AustraliaSpain
HungarySwitzerland
FranceJapanIsrael
South KoreaTaiwan
Thailand12%
10%
12%14%15%16%
18%18%18%
21%22%22%23%25%
27%33%33%33%33%33%34%34%35%35%
40%40%
36%39%
42%42%
52%53%54%55%57%57%58%
61%66%
68%68%69%
77%83%85%
99%
124%
3%5%5%5%6%6%7%8%8%8%8%8%9%9%9%9%10%10%11%11%12%12%13%13%13%13%13%14%14%14%15%15%16%16%16%17%18%18%
21%
19%19%
22%22%
43%
22%34%
37%
50%70%
Americas
Asia Pacific
Europe, Middle East & Africa
110%
Motor Property
Underwriting Volatility for Major Lines by Country, Coefficient of Variation of Loss Ratio for Each LineReported CVs are of gross loss ratios, except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Thailand, 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 makes up about 80 percent of the “Other” line of business with the balance of the line being primarily credit insurance.
Aon Benfield 23
Coefficient of variation of loss ratio for major lines by country
Mot
or
Mot
or—
Pe
rson
al
Mot
or—
C
omm
erci
al
Prop
erty
Prop
erty
—
Pers
onal
Prop
erty
—
Com
mer
cial
Gen
eral
Li
abili
ty
Acc
iden
t &
Hea
lth
Mar
ine,
A
viat
ion
&
Tra
nsit
Wor
kers
’ C
omp
ensa
tion
Cre
dit
Fid
elit
y
& S
uret
y
Americas
Argentina 14% 52% 57% 29% 8% 240%Bolivia 8% 18% 16%Brazil 13% 68% 46% 65% 83% 51% 40% 82%Canada 15% 22% 17% 34% 35% 38% 64% 102% 97%Chile 10% 33% 44% 61% 34% 46%Colombia 16% 40% 71% 29% 28% 68%Dominican Republic 13% 61% 88% 72%Ecuador 19% 34% 39% 161%El Salvador 9% 36% 18% 137%Honduras 15% 40% 225%Mexico 11% 99% 71% 45%Nicaragua 34% 55% 67% 200%Panama 22% 54% 18% 85%Peru 13% 85% 56% 23% 28% 114%Uruguay 11% 39%U .S . 16% 14% 24% 43% 47% 36% 38% 54% 39% 27% 71%Venezuela 18% 10% 20% 314%
Asia Pacific
Australia 8% 16% 23% 32% 54% 10% 30%China 16% 16% 33% 57% 29% 21% 19% 21% 62%Hong Kong 43% 69% 88% 21% 66% 75%India 12% 33% 6% 28%Indonesia 21% 68% 117% 46% 139% 123% 124%Japan 6% 33% 11% 10% 18% 10%Malaysia 10% 33% 97% 31% 54% 97%Pakistan 14% 58% 41%Philippines 70% 83% 85% 88% 124% 166%Singapore 18% 111% 48% 49% 31%South Korea 5% 6% 42% 33% 52%Taiwan 5% 5% 66% 43% 20% 69% 49%Thailand 3% 124% 124% 21% 33%Vietnam 13% 57% 54% 22% 45%
Europe, Middle East & Africa
Austria 8% 18% 13% 51% 22% 11% 23% 45%Czech Republic 9%Denmark 19% 12% 11% 10% 25% 15% 30% 31%France 6% 27% 30% 28% 36% 23% 60%Germany 9% 15% 16% 30% 26% 20% 19% 48%Greece 50% 77% 83% 84%Hungary 8% 34%Israel 5% 21% 53%Italy 12% 18% 25% 19% 41% 43% 63%Netherlands 9% 12% 20% 49% 25% 44%Poland 14% 35%Romania 37% 57% 94%Slovakia 22% 53%South Africa 22% 14% 62% 37% 39%Spain 8% 8% 23% 12% 27% 41% 13% 33% 37% 139%Switzerland 7% 25% 18% 8% 47% 86%Turkey 17% 17% 35% 35% 43% 18% 58% 86%U .K . 13% 12% 18% 22% 23% 25% 32% 6%
24 Insurance Risk Study
For the U .S . risk parameters, we use data from 13 years of
NAIC annual statements for 2,108 individual groups and
companies . Our database covers all 22 Schedule P lines
of business and contains 1 .9 million records of individual
company observations from accident years 1992-2013 .
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 (1992-2013)
The underwriting cycle acts simultaneously across many lines
of business, driving correlation between the results of different
lines and amplifying the effect of underwriting risk to primary
insurers and reinsurers . Our analysis demonstrates that the cycle
increases volatility substantially for all major commercial lines,
as shown in the table . For example, the underwriting volatility
of reinsurance liability increases by 55 percent and commercial
auto liability by 36 percent . Personal lines are more formula-
rated and thus show a lower cycle effect, with private passenger
auto volatility only increasing by 6 percent because of the cycle .
U.S. underwriting cycle impact on volatility
U.S. Risk Parameters
Line of Business Impact
Reinsurance—Liability 55%
Other Liability—Claims-Made 51%
Other Liability—Occurrence 42%
Workers Compensation 40%
Commercial Auto 36%
Medical PL—Claims-Made 37%
Special Liability 30%
Homeowners 19%
Commercial Multi Peril 15%
Private Passenger Auto 6%
14%
17%
24%
26%
32%
36%
36%
38%
39%
39%
43%
46%
47%
52%
67%
68%
72%
86%
92%
93%
99%
139%
13%
15%
18%
19%
32%
31%
36%
26%
30%
26%
32%
37%
39%
48%
44%
49%
56%
57%
58%
63%
49%
101%Financial Guaranty
Reinsurance – Financial
Products Liability – Claims-Made
Special Property
Reinsurance – Property
International
Fidelity and Surety
Reinsurance – Liability
Other
Products Liability – Occurrence
Homeowners
Medical PL – Claims-Made
Other Liability – Claims-Made
Special Liability
Other Liability – Occurrence
Medical PL – Occurrence
Commercial Multi Peril
Warranty
Workers’ Compensation
Commercial Auto
Auto Physical Damage
Private Passenger Auto
Financial Guaranty
Reinsurance – Financial
Products Liability – Claims-Made
Special Property
Reinsurance – Property
International
Fidelity and Surety
Reinsurance – Liability
Other
Products Liability – Occurrence
Homeowners
Medical PL – Claims-Made
Other Liability – Claims-Made
Special Liability
Other Liability – Occurrence
Medical PL – Occurrence
Commercial Multi Peril
Warranty
Workers’ Compensation
Commercial Auto
Auto Physical Damage
Private Passenger Auto
All Risk No Underwriting Cycle Risk
Aon Benfield 25
In many areas of the world, profitability data is both scarce
and coarse . In the U .S ., however, the NAIC statutory
financial statements provide a wealth of detailed information
summarizing profitability of property casualty insurance
by company, line of business, and geography .
The table below summarizes current net premium volume
and net accident year loss ratio, expense ratio, and
combined ratio over the last 10 years . The last five columns
summarize individual company 10 year combined ratios
at the 10th, 25th, 50th, 75th, and 90th percentiles . The
percentiles are computed on a premium weighted basis .
Over the past 10 accident years the property casualty
industry achieved a 99 percent combined ratio . However,
insurance companies between the 25th and 75th percentiles
had combined ratios ranging from 92 percent to
103 percent, illustrating that there is ample opportunity
for individual companies to outperform average results .
The magnitude of the opportunity to outperform varies
by line of business . For example, top quartile private
passenger auto liability writers achieved 98 percent
combined ratio or better, which is four points better than
the industry average . However, in commercial multiple peril
top quartile writers outperformed the industry by at least
12 points . Clearly, when considering new underwriting
opportunities, average profitability is only part of the story .
U.S. profitability by line of business
Line of business 2013 NEP USD Millions
Net loss ratio 10yr
Net expense ratio 10yr
Net combined ratio 10yr
10yr combined ratio percentiles
10% 25% 50% 75% 90%
Private Passenger Auto Liability 106,183 77% 25% 102% 94% 98% 103% 105% 106%
Homeowner & Farmowners 72,689 73% 31% 103% 91% 94% 103% 110% 120%
Auto Physical Damage 71,613 69% 25% 95% 84% 86% 97% 103% 103%
Other Liability 41,680 65% 30% 95% 84% 90% 95% 96% 105%
Workers' Compensation 40,610 74% 25% 99% 90% 93% 99% 106% 109%
Special Property 36,199 63% 28% 91% 71% 80% 89% 106% 110%
Commercial Multiple Peril 32,381 65% 35% 99% 65% 87% 98% 102% 109%
Commercial Auto Liability 17,908 69% 30% 99% 90% 95% 97% 102% 105%
Reinsurance 14,029 65% 26% 91% 44% 85% 87% 87% 95%
Medical Professional Liability 8,617 69% 21% 89% 76% 79% 84% 93% 113%
Other (Inc Credit, Accident & Health) 8,178 72% 35% 107% 53% 72% 95% 105% 127%
Fidelity & Surety 5,937 37% 46% 83% 36% 48% 67% 73% 97%
Special Liability 5,881 57% 33% 90% 55% 78% 93% 97% 109%
Financial Guaranty 5,494 152% 25% 177% 94% 124% 130% 140% 259%
Product Liability 2,629 64% 27% 91% 69% 80% 90% 97% 110%
Warranty 1,524 74% 24% 98% 43% 64% 64% 81% 88%
International 111 76% 31% 107% 52% 52% 74% 115% 115%
All Lines 471,664 71% 28% 99% 90% 92% 99% 103% 104%
U.S. Profitability
26 Insurance Risk Study
Reserve releases in the U .S . are now in their eighth consecutive
year, heightening concerns that insurers are cutting reserves
too aggressively . We can form an independent opinion
about the adequacy of statutory reserves using the high
quality, uniform data at the legal entity available through
the NAIC Schedule P in statutory accounts . These accounts
provide U .S . regulators with a clear view into insurance
companies and are part of a very effective system of solvency
regulation based on consistent and transparent reporting .
Five years ago Aon Benfield started publicly tracking the
reported reserve adequacy of U .S . companies . Each year
we analyze the aggregated net loss development data
by Schedule P line of business . Working at an aggregate
level allows our actuaries to use different methods, and
to weight the results in different ways, than is possible for
company actuaries who are working with smaller and less
stable data sets . Unlike some other public studies, each of
our reports has indicated overall reserve redundancies .
The table below summarizes the analysis of year end 2013
data . The overall industry redundancy position decreased
to USD6 .5 billion at YE2013—equivalent to only 1 .1 percent
of total booked reserves . This compares to an USD9 .2 billion
total industry redundancy position at YE2012, while USD14 .8
billion was released by insurers during 2013 . The amount of
reserves released in 2013 was the highest since 2009 . However,
a closer inspection of the results shows a fundamental shift in
the view of reserve adequacy on the commercial lines sector .
The analysis reveals that commercial lines continued to
move further into an overall deficiency position of USD2 .8
billion at year end 2013 compared to an estimated USD0 .9
billion deficiency at year end 2012 . Reserve positions
deteriorated across the commercial lines sector, which
includes commercial property, commercial liability, and
workers’ compensation . Financial guaranty moved to
a slightly less deficient position, though it represents a
small portion of the overall commercial lines sector .
The drivers of year-over-year change in our reserve estimates
are illustrated in the waterfall chart on the next page . It shows
the year end 2012 estimate of the property casualty industry
reserve redundancy was USD9 .2 billion . During calendar
year 2013, the industry released USD14 .8 billion of reserves .
Offsetting the impact of reserve releases were two factors:
2013 calendar year favorable loss emergence and redundantly
booked reserves in the 2013 accident year . Favorable
development of case-incurred losses in calendar year 2013
contributed to a decrease in ultimate loss estimates of USD11 .9
billion, while the 2013 accident year contributed an additional
USD0 .2 billion of reserve redundancy . The sum of these pieces
drives our year end 2013 redundancy estimate of USD6 .5 billion .
When we separate the year-over-year waterfall into personal
and commercial lines, a different picture emerges . On the
personal lines side, a reduction in booked reserves during
the 2013 calendar year was somewhat offset by favorable
loss emergence on prior years and redundancy in the 2013
accident year . However, on the commercial lines side,
despite having favorable loss emergence offsetting the
calendar year 2013 reserve releases, the 2013 accident year
appears under-reserved . This results in a worsening negative
overall position for the industry’s commercial lines .
U.S. reserve estimated adequacy and loss development summary (USD Billions)
LineEstimated
reservesBooked
reserves
Remaining redundancy
at YE 2013
Favorable or (adverse) developmentYears at run rate2009 2010 2011 2012 2013 Average
Personal Lines 128 .5 137 .9 9 .3 5 .8 6 .7 7 .6 7 .1 6 .0 6 .6 1 .4
Commercial Lines 440 .8 438 .0 (2 .8) 12 .8 3 .9 5 .1 5 .1 8 .8 7 .1 N/A
Commercial Property 42 .8 43 .4 0 .5 2 .4 2 .7 1 .4 1 .1 1 .7 1 .9 0 .3
Commercial Liability 231 .3 233 .9 2 .6 3 .8 2 .4 4 .1 2 .5 2 .8 3 .1 0 .8
Workers’ Compensation 146 .1 141 .5 (4 .6) (0 .5) (1 .6) (0 .0) 0 .0 0 .6 (0 .3) N/A
Financial Guaranty 20 .5 19 .2 (1 .4) 7 .0 0 .4 (0 .4) 1 .4 3 .7 2 .4 N/A
Total 569 .3 575 .8 6 .5 18 .5 10 .5 12 .7 12 .2 14 .8 13 .7 0 .5
U.S. Reserve Adequacy
Aon Benfield 27
Drivers of 2013 reserve redundancy or deficiency (USD billions)
We estimate that companies will continue to release reserves
through year end 2014, possibly extinguishing overall
redundancy in the industry . Through the first quarter
of 2014 companies have already released an additional
USD5 .4 billion of reserves . USD4 .1 billion of this release
is from personal lines, while commercial lines released
an additional USD1 .4 billion . The release in the personal
lines may be attributable to conservatism in booked
results at year end 2012 related to Superstorm Sandy .
With reduced equity in reserves going forward, mistakes in
underwriting, rate monitoring, and primary pricing will no
longer be covered up by a reserve cushion . Compounding
this issue is a continued low interest rate environment .
As we have discussed in past editions of the Study,
understanding reserve risk is critical for effectively modeling
company solvency . It is also a notoriously difficult problem:
whereas all companies face broadly similar insurance risks, such
as weather, legal, social, and medical trends, each company’s
reserving practices are idiosyncratic . Moving forward, rate
adequacy and rate monitoring—not on an aggregate premium
basis but on a rate per exposure basis—will be critical to the
operating results of companies . Aon Benfield Analytics has
developed effective models of industry loss drivers for some
U .S . lines and continues to work to expand its understanding
of macro drivers across all classes of business . We can also
assist clients with exposure-adjusted rate monitoring in
this challenging reserve and investment environment .
-12
-8
-4
0
4
8
12
+6.5+0.2+11.9
(14.8)+9.2
All Lines
Personal Lines
Commercial Lines
-12
-8
-4
0
4
8
12+9.4+3.6
+1.7
(6.0)+10.1
-12
-8
-4
0
4
8
12
(2.9)(3.4)+10.2(8.8)(0.9)
2012 Year End Reserve Redundancy/Deficiency
2013 Calendar Year Change in Booked Reserves
2013 Calendar Year Favorable/Adverse
Loss Emergence
2013 Accident Year Redundancy/Deficiency
2013 Year End ReserveRedundancy/Deficiency
28 Insurance Risk Study
Correlation between lines of business is central to a realistic
assessment of aggregate portfolio risk, and in fact becomes
increasingly significant for larger companies where there is little
idiosyncratic risk to mask correlation . Most modeling exercises
are carried out at the product or business unit level and then
aggregated to the company level . In many applications, the
results are more sensitive to the correlation and dependency
assumptions made when aggregating results than to all the
detailed assumptions made at the business unit level .
The Study determines correlations between lines within
each country . Correlation between lines is computed
by examining the results from larger companies
that write pairs of lines in the same country .
Aon Benfield Analytics has correlation tables for most
countries readily available and can produce custom analyses of
correlation for many insurance markets globally upon request .
As examples, tables for Colombia and France appear below .
Colombia
Acc
iden
t &
Hea
lth
Cro
p &
Ani
mal
Fid
elit
y &
Su
rety
Gen
eral
L
iab
ility
Life
Mar
ine,
A
viat
ion
&
Tran
sit
Mot
or
Prop
erty
Spec
ial
Liab
ility
Spec
ial
Prop
erty
Sure
ty
Accident & Health 25% 12% 27% 42% 4% 34% 17% 13% 26% -1%
Crop & Animal 25% 29% 15% 24% 7% 46% 27% 19% 6% 4%
Fidelity & Surety 12% 29% 48% 8% 4% 29% 8% 17% 8% 5%
General Liability 27% 15% 48% 46% -10% 36% 17% 18% 14% 1%
Life 42% 24% 8% 46% 12% 62% 37% 27% 47% 13%
Marine, Aviation & Transit 4% 7% 4% -10% 12% 8% 8% 19% 12% -3%
Motor 34% 46% 29% 36% 62% 8% 35% 26% 46% 9%
Property 17% 27% 8% 17% 37% 8% 35% 13% 39% 4%
Special Liability 13% 19% 17% 18% 27% 19% 26% 13% 14% 1%
Special Property 26% 6% 8% 14% 47% 12% 46% 39% 14% 11%
Surety -1% 4% 5% 1% 13% -3% 9% 4% 1% 11%
France
Acc
iden
t &
Hea
lth
Gen
eral
Li
abili
ty
Mot
or
Prop
erty
Rein
sura
nce
Accident & Health 80% 31% -5% 46%
General Liability 80% 55% 65% 88%
Motor 31% 55% 27% 66%
Property -5% 65% 27% 51%
Reinsurance 46% 88% 66% 51%
Correlation is a measure of association between two random quantities. It varies between -1 and +1, with +1 indicating a perfect increasing linear relationship and -1 a perfect decreasing relationship. The closer the coefficient is to either +1 or -1 the stronger the linear association between the two variables. A value of 0 indicates no linear relationship whatsoever.
All correlations in the Study are estimated using the Pearson sample correlation coefficient.
In each table the correlations shown in bold are statistically different from zero at the 90 percent confidence level.
Global Correlation Between Lines
Aon Benfield 29
Macroeconomic, demographic, and social indicators
Country GD
P—PP
P,
USD
bill
ions
GD
P 5y
r re
al g
row
th
Popu
latio
n— m
illio
ns
Popu
latio
n 5y
r an
nual
ized
gro
wth
GD
P Pe
r Cap
ita—
PP
P, U
SD
Gov
ernm
ent
cons
umpt
ion
as
% o
f GD
P
Actu
al in
divi
dual
co
nsum
ptio
n as
%
of G
DP
Gen
eral
gov
ernm
ent
debt
as
% o
f GD
P
Net
fore
ign
dire
ct
inve
stm
ent —
U
SD b
illio
ns
Infla
tion
rate
Une
mpl
oym
ent r
ate
Cor
pora
te ta
x ra
te
Polit
ical
risk
as
sess
men
t
Aon
terr
orism
risk
as
sess
men
t
Aon
Hew
itt p
eopl
e ris
k as
sess
men
t
Wor
ld B
ank
rela
tive
ease
of d
oing
bu
sines
s
Argentina 793.8 6.5% 42.0 1.1% 18,917 23.4% 69.1% n/a 12.1 n/a 7.6% 35.0% High Medium High More difficult
Australia 1041.5 4.3% 23.5 1.4% 44,346 31.3% 58.9% 16.1% 56.6 2.3% 6.2% 30.0% Low Negligible Low Easiest
Austria 373.1 3.1% 8.5 0.4% 43,796 25.8% 59.7% 58.3% 4.1 1.8% 5.0% 25.0% Low Negligible Low Easiest
Belgium 434.5 2.7% 11.2 0.8% 38,826 29.8% 58.4% 82.5% -1.9 1.0% 9.1% 34.0% Medium Low Negligible Low Easiest
Brazil 2505.2 4.7% 200.0 0.9% 12,526 21.0% 67.2% 33.3% 76.1 5.9% 5.6% 25.0% Medium High Medium More difficult
Bulgaria 108.3 2.7% 7.2 -1.0% 15,031 19.3% 55.8% -7.0% 2.1 -0.4% 12.5% 10.0% Medium Low Very high Easier
Canada 1585.0 4.0% 35.5 1.1% 44,656 25.4% 60.9% 39.5% 43.1 1.5% 7.0% 26.5% Low Negligible Very low Easiest
Chile 352.2 6.6% 17.7 0.9% 19,887 29.0% 64.2% -5.9% 30.3 3.5% 6.1% 20.0% Medium Low Medium Low Easiest
China 14625.2 10.2% 1367.5 0.5% 10,695 51.2% 27.9% n/a 253.5 3.0% 4.1% 25.0% Medium Medium Medium More difficult
Colombia 559.7 6.4% 47.7 1.2% 11,730 25.5% 68.6% 25.2% 15.6 1.9% 9.3% 25.0% Medium High High Easiest
Czech Republic 295.9 2.5% 10.5 0.2% 28,086 23.7% 53.4% n/a 10.6 1.0% 6.7% 19.0% Medium Low Low Low Easier
Denmark 218.3 2.4% 5.6 0.4% 38,917 23.3% 49.3% 8.9% 1.3 1.5% 6.8% 24.5% Low Negligible Very low Easiest
Ecuador 168.2 6.6% 16.0 1.7% 10,492 26.1% 65.1% n/a 0.6 2.8% 5.0% 22.0% High Medium n/a More difficult
Finland 197.8 2.4% 5.5 0.5% 36,122 24.8% 55.6% -48.6% 4.3 1.7% 8.1% 20.0% Low Negligible Low Easiest
France 2336.6 2.6% 64.0 0.5% 36,537 23.2% 61.9% 89.5% 28.1 1.0% 11.0% 33.3% Medium Low Low Low Easiest
Germany 3338.0 3.7% 80.9 -0.2% 41,248 18.7% 58.7% 52.9% 27.2 1.4% 5.2% 29.6% Low Negligible Low Easiest
Greece 271.3 -3.0% 11.0 -0.3% 24,574 16.5% 76.9% 169.3% 1.7 -0.4% 26.3% 26.0% High Medium Very high Easier
Hong Kong 402.3 5.6% 7.3 0.9% 55,026 28.4% 62.6% n/a 74.6 4.0% 3.1% 16.5% Low Low Very low Easiest
India 5425.4 8.0% 1259.7 1.3% 4,307 28.7% 51.8% n/a 24.0 8.0% 0.0% 34.0% Medium High High More difficult
Indonesia 1382.9 7.7% 251.5 1.4% 5,499 22.9% 58.2% n/a 19.6 6.3% 6.1% 25.0% Medium High High More difficult
Ireland 195.0 2.1% 4.8 1.2% 40,586 13.2% 41.2% 103.5% 41.0 0.6% 11.2% 12.5% Medium Negligible Low Easiest
Israel 286.8 5.7% 8.0 2.2% 35,659 23.2% 60.5% 65.1% 9.5 1.6% 6.7% 26.5% Medium Low High Low Easiest
Italy 1846.9 1.3% 60.0 0.3% 30,803 22.6% 63.3% 112.4% 6.7 0.7% 12.4% 31.4% Medium Low Medium Easier
Japan 4835.0 3.3% 127.1 -0.2% 38,053 20.7% 59.5% 137.1% 2.5 2.8% 3.9% 35.6% Medium Low Low Low Easiest
Luxembourg 44.2 3.4% 0.6 2.1% 79,977 32.1% 44.6% n/a 27.9 1.6% 7.1% 29.2% Low Negligible n/a Easier
Malaysia 561.5 7.3% 30.1 1.4% 18,639 30.3% 52.2% n/a 9.7 3.3% 3.0% 25.0% Medium Low Low Easiest
Mexico 1926.6 5.0% 119.6 1.2% 16,111 19.9% 66.9% 42.2% 15.5 4.0% 4.5% 30.0% Medium Medium Medium Easier
Morocco 189.1 5.6% 33.2 1.0% 5,699 38.2% 66.0% 62.2% 2.8 2.5% 9.1% 30.0% Medium High Medium High Easier
Netherlands 717.1 1.8% 16.8 0.4% 42,586 19.8% 46.2% 37.8% 6.7 0.8% 7.3% 25.0% Low Low Very low Easiest
New Zealand 143.2 4.1% 4.5 0.9% 31,692 18.2% 64.7% 25.3% 2.2 2.2% 5.2% 28.0% Low Negligible Low Easiest
Nigeria 521.4 8.8% 173.9 2.8% 2,997 9.6% 58.9% 20.0% 7.1 7.3% n/a 30.0% Medium High Severe Very high Most difficult
Norway 289.4 3.0% 5.1 1.2% 56,223 22.4% 37.0% -205.2% 23.0 2.0% 3.5% 27.0% Low Negligible Very low Easiest
Poland 855.6 4.6% 38.5 0.2% 22,201 18.4% 62.4% 21.8% 6.7 1.5% 10.2% 19.0% Medium Low Low Medium Easiest
Portugal 251.5 1.0% 10.6 0.0% 23,671 22.0% 66.2% 119.9% 13.4 0.7% 15.7% 23.0% Medium Low Medium Easiest
Romania 296.0 3.1% 21.2 -0.2% 13,932 24.8% 59.3% n/a 2.0 2.2% 7.2% 16.0% Medium High Low High Easier
Russia 2629.7 4.6% 142.9 0.0% 18,408 16.4% 55.5% n/a 50.7 5.8% 6.2% 20.0% Medium Medium High Easier
Saudi Arabia 990.4 7.6% 30.6 2.8% 32,340 33.0% 31.1% -62.3% 12.2 3.0% n/a 20.0% Medium Low Medium Low Easiest
Singapore 366.9 7.7% 5.5 1.9% 67,035 31.5% 33.1% n/a 56.7 2.3% 2.0% 17.0% Low Negligible Very low Easiest
South Africa 619.8 4.3% 53.7 1.3% 11,543 22.2% 71.6% 41.5% 4.6 6.0% 24.7% 28.0% Medium Low Medium Easiest
South Korea 1755.0 5.3% 50.4 0.5% 34,795 13.3% 50.8% 37.4% 5.0 1.8% 3.1% 24.2% Medium Low Low Low Easiest
Spain 1424.9 1.2% 46.5 0.1% 30,637 25.5% 57.9% 65.7% 36.2 0.3% 25.5% 30.0% Medium Low Low Easier
Sweden 414.1 4.6% 9.7 0.8% 42,624 21.7% 52.2% -17.8% 4.0 0.4% 8.0% 22.0% Low Negligible Very low Easiest
Switzerland 385.3 3.6% 8.1 0.9% 47,863 23.6% 58.1% 27.4% 2.7 0.2% 3.2% 17.9% Low Negligible Very low Easiest
Taiwan 973.3 5.9% 23.4 0.3% 41,540 18.2% 55.8% n/a n/a 1.4% 4.2% 17.0% Medium Low Low Low Easiest
Thailand 701.1 5.6% 68.6 0.5% 10,227 25.2% 52.8% n/a 10.7 2.3% 0.7% 20.0% Medium High Medium Easiest
Turkey 1219.2 7.0% 77.3 1.4% 15,767 20.9% 65.0% 27.2% 12.5 7.8% 10.2% 20.0% Medium High High Easier
U.A.E. 288.2 5.5% 9.3 2.6% 30,985 n/a n/a -96.0% 9.6 2.2% 0.0% 55.0% Medium Low Low Very low Easiest
U.K. 2497.3 3.1% 64.5 0.9% 38,711 19.8% 65.7% 84.4% 56.1 1.9% 6.9% 21.0% Medium Low Low Very low Easiest
U.S. 17528.4 4.0% 318.8 0.7% 54,980 11.6% 75.9% 82.3% 203.8 1.4% 6.4% 40.0% Low Low Very low Easiest
Venezuela 412.1 3.3% 30.5 1.6% 13,531 22.7% 57.6% n/a 2.2 50.7% 11.2% 34.0% High High Very high Most difficult
30 Insurance Risk Study
Big Data is ever present in the media, and we hear of new innovations and predictions derived from massive on-line data sources almost daily . What is Big Data and what tools are used to analyze it? How are these concepts being applied in the insurance industry and what are the potential implications? Now we explore Big Data in the insurance space .
What is “Big Data”?There are a number of different views of what comprises
Big Data, but two themes appear again and again .
The first theme is the three-Vs: big data has volume, variety,
and velocity . Volume is obvious: the data would not be big
without volume . Variety expresses the broad range of data
types, from free-form text to photographs, and from video to
manufacturing and scientific instrument output . Velocity refers
to the real-time and streaming nature of many types of Big
Data . Financial market data, voice feeds, social media, security
camera feeds, embedded devices, and the whole “internet of
things” cast off a massive volume of data on a continuous basis .
The second theme relates to computational capacity . High-
velocity streaming data must be processed in real time to
be useful, which stresses traditional computing devices .
This definition is evolving with computer technology, but
today it means data in the petabyte range—a petabyte
being 1,000 terabytes . When working with this mass of data,
one key criterion is an algorithm’s tolerance of hardware
failures, since the data will be stored on so many individual
hard drives that failures become relatively common .
Traditional Big Data, such as social media feeds, delivers
a massive amount of cheap user-contributed content
characterized by the three Vs . Insurance data, in comparison,
is sparse, occurs at discrete intervals (a renewal, a claim) and is
often expensive to collect and maintain (on-site inspection) . It is
typically structured but inhomogeneous within type: different
databases have the same general columns (construction,
occupancy, protection) but the values are coded differently
between different systems . This inhomogeneity makes data
aggregation difficult .
Whereas traditional Big Data is high frequency and low
value, insurance data is lower frequency but potentially
much higher value—in part explaining company concerns
over data security . Companies in personal lines and small
commercial lines use data as a key competitive advantage,
and they are able to glean important pricing insights from it .
From data to actionBig Data is like iron ore: bulky and useless in its raw state .
It has to be refined through several stages before it yields
up actionable and consumable information . Transforming
raw data into usable information is typically an IT problem .
In the insurance industry it often involves combining
database and data sources, remapping, recoding, cleansing,
and normalizing data . Insights are then the result of
applying analysis to the resulting information . Insights
draw out connections and linkages . They suggest patterns,
causation, and ultimately actions for new products,
coverages, pricing variables, and risk management .
Big Data and Insurance
How does traditional insurance data compare to Big Data?
Insurance data Internet big data
Sparse, expensive data Massive, free data
Policy, claim, client Internet of Things
Expensive to collect & maintain Free: user contributed, digital exhaust
Static Streaming
Structured Unstructured—variety
Inhomogeneous within type Homogeneous within type
Potentially high value density Low value density
Low to medium volume Very high volume
Aon Benfield 31
ActionInsightInformationData
New data sources The data available to insurers today far exceeds that available
to underwriters twenty five years ago . As catastrophe
modeling was introduced to the industry, an early complaint
was that the “data is not available .” But, driven by huge
losses from Hurricane Andrew in 1992 and the Northridge
earthquake in 1994, the industry quickly adapted and
implemented vastly improved data standards . Today our
understanding of property risks far exceeds anything
available then . We see the implications for the industry in
the market every day . And yet we have barely scratched
the surface of the new data sources potentially available in
today’s connected world . We touch on three emerging data
sources: near real-time satellite imagery; embedded medical
and personal fitness devices; and streaming location data .
Today’s technology has enabled a new generation of micro-
satellites that can be deployed relatively cheaply and in
sufficient numbers to provide a view of anywhere on earth
multiple times each day . The possibilities are endless . In
underwriting we can document the property on the day the
policy is written . We can look at tree proximity and brush
clearance as it exists today, without the need for a costly
on-site survey . We can also see the property just before
and immediately after a claim . Still under development, but
theoretically possible, are tracking systems monitoring for
signs of construction or the progress of a large commercial
construction project, changes in roofs, and, in the infrared
spectrum, an assessment of the energy efficiency of each home .
Beyond property, there are exciting potential applications in
agricultural insurance, logistics, and exposure analysis . For
example, is reported revenue consistent with parking lot usage?
Big Data for insurance is often synonymous with Behavioral
Data . Behavioral Data starts with credit based variables, which
have been used successfully for many years . But credit is a
window into behavior that can be detected in other ways .
Shopping habits are a good example . Personal fitness devices
are increasingly popular, linking activity wirelessly to on-line
databases and there are insurance behavioral implications
in the data they collect . Related data comes from smart
phone records of location . Not as detailed as auto usage
based insurance feeds, time-stamped location data has a
myriad of insurance applications, particularly for auto .
We all leave a trail of “digital exhaust” that can be
exploited not just to assess our personal risk footprint
more accurately, but also to design and offer risk
protections more tailored to our personal situation .
32 Insurance Risk Study
The future We end with some ideas about how the future could unfold .
• Big Data will create new insurance markets, more aligned with
how customers want to buy protection . It is already possible
to source many of the required rating variables for personal
lines protections from public and quasi-public data sources .
The next step could be a lifestyle policy covering all first and
third party liabilities for an individual, replacing separate
homeowners, auto and umbrella policies . Such a policy
could be priced almost entirely based on Big Data feeds:
credit and financial information delivering both behavioral
and risk insights as well as an inventory of possessions for
first party coverages, potentially enhanced via location and
activity feeds . Given the relative size of property casualty
and health insurance premiums, such a policy could evolve
into an add-on to a health policy, potentially bought through
a corporate or private exchange shopping environment .
Provision through an exchange would greatly improve
the customer shopping experience, clarifying pricing and
coverage options, and facilitating easy comparative shopping .
• Big Data and technology will stress or kill some existing
markets . Driverless cars have the potential to transform
auto insurance, and given the importance of auto to
overall industry results and capital needs, the whole
property casualty industry . The industry of the future
will be even more capital intensive than today, but will
offer more material limits to commercial buyers—often
syndicated to the global reinsurance market—helping
to offset the decline in attritional loss volume .
• New data sources will continue to be tapped for
insurance applications . This will include both new
applications of existing feeds and the creation of
wholly new data sources, such as micro-satellites .
• Greater understanding of risk will lead to better
risk management and behavioral “nudges,”
continuing the trend of greater safety and fewer
accidents in mainstream insurance products .
• Larger insurance companies will continue to prosper at the
expense of smaller companies in many lines, driving explicit
consolidation or concentration through natural selection .
Smaller companies will be forced to form data unions to pool
data and to leverage their customer and local geographic
knowledge advantages more effectively . Concentration in the
U .S . market will increase to mirror that seen in other countries .
Data and analytics will increasingly create and drive value
creation in the future . The Aon Benfield team invests
heavily in order to stay at the forefront of emerging data
technologies to be able to offer meaningful support
and collaboration to our clients as we all work to stay
relevant and competitive in the risk marketplace .
Aon Benfield 33
Sources and Notes
Foreword
Sources:
A .M . Best, Axco Insurance Information Services, Impact Forecasting 2013 Annual Global Climate and Catastrophe Report, SNL Financial
Global Premium, Profitability & Opportunities
Sources:
A .M . Best, Axco Insurance Information Services, IMF World Economic Outlook Database April 2014 Edition, SNL Financial, Standard & Poor’s, World Bank
Notes:
Pages 5-15—P&C GWP from Axco converted to USD by Axco .
Page 5 Map—Country premiums from Axco matched against U .S . state premiums from SNL .
Page 7 Table—GDP (PPP) is nominal GDP in local currency adjusted using purchasing power parity (PPP) exchange rate into U .S . dollars . The PPP exchange rate is the rate at which the currency of one country would need to be converted in order to purchase the same amount of goods and services in another country .
Pages 7-9 Table and Quadrant Plots—Premium and growth calculated using Axco data . Loss ratios for motor, property and liability lines also calculated using Axco . “All lines” loss, expense, and combined ratios are calculated using A .M . Best’s Statement File—Global and are based on the net results of the largest 25 writers for a given country (where available) .
Looking Ahead: Growth Projections
Sources:
Axco Insurance Information Services, IMF World Economic Outlook Database April 2014 Edition, and annual financial statements
Notes:
Pages 10-13 Tables—Compound annual growth rate is a measure of growth over multiple time periods, in this case years . It can be understood as the growth rate from an initial period to a final period if we assume annual compounding at a consistent rate . GWP projections were made using an equal weighting of projected GDP growth, projected population growth, and past 5-year GWP growth for each country, for motor, property and liability business .
Strategies for Growth
Sources:
Axco Insurance Information Services, Euromoney, IMF World Economic Outlook Database April 2014 Edition, World Bank, and annual financial statements
Notes:
Opportunity Index Calculation: For each combined ratio, growth and political risk statistic, countries were ranked from 1 to 50 . A combined score was then calculated from these metrics . Opportunity Index Score = 1/3 * combined ratio score + 1/2 * projected GWP growth rate + 1/6 * political risk score
Insurance Trends: Risks and Opportunities
Sources:
A .M . Best, Aon Global Risk Insight Platform (GRIP), Aon Hewitt, The Economist, Identity Theft Resource Center, JP Morgan, Kaiser Family Foundation: Employer Health Benefits 2012 Annual Survey, Mondaq .com, Onlineautoinsurance .com, Ponemon Institute, SNL Financial, Symantec, Verizon, Yearbooks Of China’s Insurance
Global Risk Parameters and U.S. Risk Parameters
Sources:
ANIA (Italy), Association of Vietnam Insurers, BaFin (Germany), Banco Central del Uruguay, Bank Negara Malaysia, CADOAR (Dominican Republic), Cámara de Aseguradores de Venezuela, Comisión Nacional de Bancos y Seguros de Honduras, Comisión Nacional de Seguros y Fianzas (Mexico), Danish FSA (Denmark), Dirección General de Seguros (Spain), DNB (Netherlands), Ernst & Young Annual Statements (Israel), Finma (Switzerland), FMA (Austria), FSA (U .K .), HKOCI (Hong Kong), http://www .bapepam .go .id/perasuransian/index .htm (Indonesia), ICA (Australia), Insurance Commission (Philippines), IRDA Handbook on Indian Insurance Statistics, Korea Financial Supervisory Service, Monetary Authority of Singapore, MSA Research Inc . (Canada), Quest Data Report (South Africa), Romanian Insurance Association, Slovak Insurance Association, SNL Financial (U .S .), 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, The Insurance Association of Pakistan, The Statistics of Japanese Non-Life Insurance Business, Turkish Insurance and Reinsurance Companies Association, Yearbooks Of China’s Insurance, and annual financial statements
34 Insurance Risk Study
Notes:
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 behavior is illustrated in the left hand 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, see right chart . The Study calculates this systemic risk by line of business and country . The Naïve Model on the right hand plot shows the relationship between risk and volume using a Poisson assumption for claim count—a textbook actuarial approach . The Study clearly shows that this assumption does not fit with empirical data for any line of business in any country . It will underestimate underwriting risk if used in an ERM model .
U.S. Reserve Adequacy and U.S. Profitability
Sources:
SNL Financial
Notes:
See the Aon Benfield Analytics U .S . P&C Industry Statutory Reserve Study for additional analysis at: http://thoughtleadership.aonbenfield.com/Documents/20140604_ab_analytics_industry_reserves_study_2013.pdf
Page 25 table—Results based on universe of U .S . statutory top level companies and single companies . Combined ratio calculated by combining accident year loss ratios from Schedule P with IEE expense ratios on a net basis . Combined ratio percentiles are weighted by 10-year average premium volume . Individual company combined ratios below 30 percent or greater than 600 percent were excluded from the calculation .
Global Correlation between Lines
Sources:
Superintendencia Financiera de Colombia, and annual financial statements (France)
Macroeconomic Demographic and Social Indicators
Sources:
Aon Political Risk Map 2014, Aon Terrorism & Political Violence Map 2014, Axco Insurance Information Services, Bloomberg, Ernst & Young, Euromoney, Fitch Ratings, IMF World Economic Outlook Database April 2014 Edition, KPMG, Moody’s, Penn World Table Version 8 .0, Standard & Poor’s, World Bank
Port
folio
ris
k
Insurance portfolio riskAsset portfolio risk
Number of stocks Volume
Insu
ranc
e ri
sk
Portfolio Risk
SystemicMarket Risk
SystemicInsurance
Risk
Naïve Model
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About Aon Benfield
© Aon Benfield Inc . 2014 .All rights reserved . This document is intended for general information purposes only and should not be construed as advice or opinions on any specific facts or circumstances . This analysis is based upon information from sources we consider to be reliable, however Aon Benfield Inc . does not warrant the accuracy of the data or calculations herein . The content of this document is made available on an “as is” basis, without warranty of any kind . Aon Benfield Inc . disclaims any legal liability to any person or organization for loss or damage caused by or resulting from any reliance placed on that content . Members of Aon Benfield Analytics will be pleased to consult on any specific situations and to provide further information regarding the matters .
ContactsFor more information on the Insurance Risk Study or our analytic capabilities, please contact your local Aon Benfield broker or:
Stephen MildenhallGlobal Chief Executive Officer of AnalyticsAon Center for Innovation and Analytics, Singapore+65 6231 6481stephen .mildenhall@aon .com
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About Aon Aon plc (NYSE:AON) is the leading global provider of risk management, insurance and reinsurance brokerage, and human resources solutions and outsourcing services. Through its more than 66,000 colleagues worldwide, Aon unites to empower results for clients in over 120 countries via innovative and effective risk and people solutions and through industry-leading global resources and technical expertise. Aon has been named repeatedly as the world’s best broker, best insurance intermediary, best reinsurance intermediary, best captives manager, and best employee benefits consulting firm by multiple industry sources. Visit aon.com for more information on Aon and aon.com/manchesterunited to learn about Aon’s global partnership with Manchester United.
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