Insurance Risk Study - Reinsurance Thought Leadership - Aon Benfield

Aon Benfield

Insurance Risk Study Growth, profitability, and opportunity Ninth edition 2014

Risk. Reinsurance. Human Resources.

Table of Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Global Premium, Profitability, and Opportunity. . . . . . . . . . . . . . 5 Global 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 . . . . . . . . . . . . . . . . . . . . . . . 15 Country Opportunity Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Insurance Trends: Risks and Opportunities. . . . . . . . . . . . . . . . . 17 Auto 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

Introduction The 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

Catastrophe losses have been a driver of the growth in

USD4.9 trillion, an increase of 0.9 percent over the prior year.

property premiums in many parts of the world. Impact

Property casualty premium increased 3.5 percent, and

Forecasting, Aon Benfield’s catastrophe model development

life premiums shrunk by 2.0 percent while health premiums

center, estimates that during 2013 insured catastrophe losses

grew 4.5 percent.

totaled USD45 billion. In perspective, catastrophe losses

Global insurance premium and capital, USD trillions

translated into 3.2 percent of property casualty premium and a “global catastrophe load” of 9.9 percent of property premium.

Premium

Capital

Property & Casualty

1.4

1.3

Life & Health

3.3

2.1

Reinsurance

0.2

0.5

Total

4.9

4.0

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

Global capital increased 3.9 percent year on year to USD4.0

100.4 percent in Asia Pacific. The five year average combined

trillion. Property casualty insurance capital increased 7.1 percent.

ratio continued under 100 percent too, at 99.8 percent. The

And reinsurance capital is at an all-time high, as we discuss at

overall global combined ratio under 100 percent, and the

greater length in Aon Benfield’s Reinsurance Market Outlook.

variation in results by country, clearly show there are many

Property casualty penetration is 1.9 percent of GDP, marginally

desirable areas for profitable growth in the market today.

down from 2.0 percent last year based on the top 50 countries.

Where does the insurance market go from here? We

Auto insurance accounts for 46 percent of property casualty

project that global premium will grow by 18 percent over

premium, while property accounts for 33 percent and liability

the next five years, reaching a total of USD 1.6 trillion by

for 21 percent.

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.

Aon Benfield

3

Growth and Big Data

Using the study

The Insurance Risk Study is now in its ninth edition, and

Insurance risk remains core to the Study and pages 22

there have been many changes in the industry since we

to 30 contain our comprehensive view of risk by line

began research for the first edition in 2005. After seven

and geography using the techniques we have been

major (category 3+) U.S. hurricane landfalls from 2004

applying consistently since 2005. The Insurance Risk

to 2005 there have been no major hurricane landfalls

Study continues to be the industry’s leading set of risk

in the last eight years. Adverse loss development has

parameters for modeling and benchmarking underwriting

turned into a long stream of favorable development.

risk and global profitability. Beyond risk modeling, we can

Premium growth and the locus of catastrophe losses

also provide our clients with very granular, customized

has shifted to the East. Today the emphasis is on

market intelligence to create business plans that are

making efficient use of cheaper alternative capital and

realistic, fact-based, and achievable. With a global fact

on growth in the face of an often sluggish economic

base and broad access to local market practitioners, we

environment—challenging themes that recent editions

are equipped to provide insight across a spectrum of

of the Study have addressed with increasing detail.

lines, products, and geographies. Inpoint, the consulting

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

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.

broadly, are spearheading several initiatives to help

All of our work at Aon Benfield is motivated by client

bring more predictive variables and analytic insight to

questions. We continue to be grateful to clients who have

clients, in areas including health and crop insurance.

invited us to share in the task of helping them analyze

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.

4

division of Aon Benfield, helps insurers and reinsurers

Insurance Risk Study

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.

Global Premium, Profitability, and Opportunity 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

California is the largest U.S. state in terms of premium,

markets and high-potential customer segments to formulate

and if it were independent it would follow the U.K. as

growth programs tailored to an insurer’s capabilities and risk

the seventh largest insurance market in the world. Texas,

appetite. Working with our broker network and our investment

Florida, and New York would also sit among the top

bank, Aon Benfield Securities, we can develop and help

10 as independent countries, having roughly the same

execute growth plans through organic growth, acquisition,

premium as Canada, Italy, and Australia respectively.

reinsurance, and joint ventures, singly or in combination.

This section presents our unique, detailed analysis of

Part of our job is to make connections and draw comparisons

global capital, premium, and profitability, as well as

that others do not see. In that spirit, we begin this section

snapshots of trends and emerging risks that we expect to

with the map below, which overlays country names on

create both risk and opportunity in the coming years.

states with approximately equivalent premium volumes.

Global P&C premium compared to U.S. state premium Morocco

Mountain Region = Australia Austria

West North Central Region = Canada Greece

Romania

Singapore

Luxembourg

Pacific Region = China

Russia

Thailand Brazil

Ireland

India

Thailand

Austria

Switzerland

Denmark

Colombia

Greece

Mexico

Morocco

Argentina Netherlands Sweden

Poland Canada

West South Central Region = U.K.

Russia

South Africa Malaysia

Romania

Turkey

South Africa Venezuela

Poland

Romania Israel

Australia Brazil

Austria

U.K. Saudi Arabia

Morocco

New England Region = Spain

India

Portugal Taiwan

Romania

Mid-Atlantic Region = China

South Africa

Singapore

Thailand

Nigeria

East North Central Region = Germany

Switzerland

South Atlantic Region = Japan

Italy

East South Central Region = Spain

Aon Benfield

5

Global P&C gross written premium and growth rates by product line Premium by product line

Five-year average annual growth rate

Motor: USD 633 billion Brazil

Motor: 3.6% annual growth Canada Rest of Americas China, 10%

U.S., 34%

30 25 20 15

Japan South Korea Rest of APAC France, 4% Germany U.K.

0 -5 a

e

ric

st

&

Af

ur op

fE M

id

dl

e

Re s

Re s

Ea

ur o fE to

Re s

to

. U. K

Ar ea

an y

ce

m

G

er

PA C

Fr an

to

h ut So

fA

n

Ko re a

a

pa

in

to Re s

Property: USD 453 billion

Ja

as ic

Ch

fA

m

er

il

.

Br az

U. S

da

-10

Rest of Euro Area

na

Rest of Europe

5

Ca

Middle East & Africa

10

France, 6% Germany

30 25 20 15 10 5

U.K.

0 -5

Rest of Europe Rest of Euro Area

U.S., 45%

Rest of APAC France, 6% Germany

ur op Ea e st & Af ric a

id dl

e

Re s

fE to Re s

to fE

Ar ea

. U. K

ur o

Fr an ce er m an y G

PA C fA

to Re s

n

Ko re a

20 15 10 5

-5 ur op Ea e st & Af ric a

to

e

M id dl

Re s

to fE

Re s

fE

Ar ea

. U. K

ur o

Ko Re re a st of AP AC Fr an ce G er m an y

So

ut

h

Ja

pa n

-10

Re s


h

25

0 Rest of Euro Area

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.

6

pa

30

Br az

Rest of Europe

U.K.

Liability: 1.6% annual growth

.


M

Canada Rest of Americas China, 2% Japan South Korea

U. S

Brazil

So ut

Liability: USD 296 billion

Ja

U. S

.

-10

Ch in a


Property: 4.0% annual growth

il Ca na to da fA m er ic as

U.S., 44%

Canada Rest of Americas China, 3% Japan South Korea Rest of APAC

Br az il Ca Re na st d of Am a er ic as Ch in a

Brazil

Premium/ GDP Ratio

P&C GWP (USD M)

Top 50 P&C markets ranked by gross written premium by region Annualized Premium Growth 1yr

3yr

5yr

Cumulative Net Loss Ratio 1yr

3yr

5yr

Cumulative Net Expense Ratio 1yr

3yr

5yr

Cumulative Net Combined Ratio 1yr

3yr

5yr

Americas 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%

Canada

U.S.

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%

101.9% 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% 104.8%

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%

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%

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% 108.9%

Venezuela 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%

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%

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%

Subtotal

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% 101.0%

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%

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% 91.0%

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%

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% 100.7%

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%

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% 101.9%

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%

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%

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% 103.5%

Subtotal 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%

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%

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% 101.0%

India 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%

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% 120.5%

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%

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 Subtotal Top 50

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%

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%

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%

Aon Benfield

7

Growth markets and over or under performers Aon Benfield examined premium growth and loss ratio

For all quadrant plots, growth is determined based on five

performance by country across motor, property, and liability

year annualized premium growth. Countries with values

lines of business as well as premium growth and combined ratio

greater than 7.5 percent are classified as high growth.

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

Loss ratio and combined ratio performance is determined based on five year average loss ratio and five year average

combined ratio (total) out performers or under performers.

combined ratio, respectively. Each country’s loss ratio

To measure performance, the first three quadrant plots use loss

using a USD30,000 GDP per capita split between high income

ratio for each line of business while the right-most plot shows

and low income companies. Combined ratio performance

combined ratio for all lines of business. Each plot also provides

is compared against the global combined ratio. Countries

the gross written premium size, in USD millions, of each country.

with five year loss ratios lower than the average of their

performance is compared against its income level peers,

income peers, or combined ratios below the global combined ratio, are classified as out performers. Motor

Property

Loss ratio performance

Loss ratio performance Out performers Out performers

Out performers Out performers

Low growth

Austria Bulgaria Canada Czech Republic France Greece* Japan Malaysia* Mexico Netherlands Nigeria Poland Romania Spain Switzerland* Taiwan Thailand U.A.E.*

2,313 33 6,607 1,182 9,945 356 17,034 Austria 480 1,591 Bulgaria 2,941 Czech Republic 294 Denmark 1,209 Hong Kong 192 Japan 6,322 New Zealand 3,330 Norway*397 Switzerland 294 U.S. 1,137

Belgium Denmark Finland Germany Ireland Israel Italy Luxembourg Portugal S. Korea U.K. U.S.

Belgium 3,151 Brazil*1,026 Canada1,375 Finland 16,331 France 812 Germany622 Greece5,392 Ireland 193 Israel 1,047 Italy 1,797 16,617 Luxembourg 133,766 Mexico Netherlands Poland Portugal Romania Russia S. Korea Spain Sweden Taiwan Turkey U.A.E.* U.K.

Low growth

Australia Brazil* Chile* China* Colombia* Ecuador* 3,999Kong* Hong 673 India* 1,829 Indonesia* 2,903 New Zealand* 481 Russia* 57,290 Saudi Arabia 1,179 Singapore* 3,369 Africa South 6,449 Turkey* 206,817 Venezuela* 4,422 Argentina 14,209 Morocco 19,768 Norway 2,051 Sweden 24,785 30,671 2,006 1,520 2,401 24,799 481 5,193 5,483 4,361 1,739 1,281 11,127 8,896 12,717 3,287 2,254 4,475 1,227 23,789

11,644 1,692 742 7,007 1,078 337 1,425 China*1,764 Colombia* 487 Ecuador* 421 Indonesia* 2,231 Nigeria*251 Singapore 784 South1,144 Africa Thailand* 595 Venezuela* 1,166 Argentina 4,830 Australia408 Chile 1,594 India 203 Malaysia Morocco Saudi Arabia

63,522 1,982 593 1,196 294 974 4,535 3,854 4,506

High growth

5,160 12,854 1,181 5,213 2,388 931 1,695

High growth

Under performers Under performers * Indicates country was a high growth out performer in 2013 Insurance Risk Study Bold indicates country outperforms in all four quadrant plots. 8


Low growth

Brazil* Bulgaria Greece Hong Kong India Luxembourg Malaysia Romania Singapore Spain Switzerland* Taiwan Turkey* U.A.E.* U.K. U.S. Venezuela*

Austria Belgium Brazil* 7,745 Bulgaria Canada 210 730 Czech Republic Denmark745 France2,223 Germany277 Italy 1,573 Mexico 456 Norway 744 10,216 Poland 5,165 Portugal Russia*1,266 Spain 2,700 Sweden1,106 25,132 Switzerland 191,255 Taiwan U.A.E.*1,059

Austria Belgium Canada Czech Republic Denmark Finland France Germany Ireland Israel Italy Japan Netherlands Norway Poland Portugal S. Korea Sweden

Finland3,455 Greece3,307 Ireland 15,803 Israel 1,141 Luxembourg 4,544 Netherlands 1,682 Romania 25,672 S. Korea 23,030 Turkey1,216 U.K. 1,181 U.S. 7,206 20,501 4,943 4,916 1,868 978 2,605 4,179

Low growth

9,767 10,880 23,647 917 42,179 3,935 8,473 66,918 71,432 37,397 China* 10,415 Colombia* 9,454 Ecuador* 7,439 Indonesia* 4,165 Mexico 19,199 Morocco* 28,826 Nigeria* 7,669 Russia* 14,682 Saudi 3,917 Arabia* South Africa 3,424 5,107 Argentina* 2,840 Australia 3,548 Chile 4,326Zealand New 951 Thailand 13,366 1,929 13,298 7,770 65,538 531,838

Australia Chile* China 13,902 Ecuador* Hong 1,365 Kong 616 Indonesia* 1,721 Malaysia* 3,631 Morocco Nigeria*307 548 Saudi Arabia* 5,860 Singapore* South1,121 Africa 4,289 Venezuela* Argentina 1,844 Colombia 11,598 India 1,784 Japan 2,287 New Zealand 1,503 Thailand*

Under performers Under performers

34,097 3,708 84,431 1,547 3,187 3,404 4,442 1,638 1,136 3,067 2,501 9,968 672

High growth

11,835 4,425 9,200 94,825 3,886 5,651

High growth

Twenty countries are high growth, loss ratio outperformers

outperform the global averages for both growth

in at least one line of business. Of these twenty countries,

and profitability. Singapore, as an example, outperforms for

five appear in each of the lines of business analyzed

both motor and liability insurance, and with an all lines five year

as high growth out performers: China, Colombia,

combined ratio of 88.9 percent, it has been a significantly

Ecuador, Indonesia, and South Africa. All but China and

more profitable market than its overall Asia Pacific peer group. (See the Top 50 P&C Markets table, page 7 for more details.)

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

Using combined ratio in addition to loss history allows us to further analyze and target high growth opportunities.

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

Liability

All Lines

Loss ratio performance

Combined ratio performance Out performers Out performers

Out performers Out performers

Low growth

Austria Bulgaria Canada Czech Republic France Greece* Japan Malaysia* Austria Mexico3,999 Bulgaria 673 Netherlands Czech Republic Nigeria1,829 Denmark 2,903 Poland Hong Kong Romania481 Japan Spain57,290 New Zealand Switzerland* 1,179 Norway* Taiwan3,369 Switzerland 6,449 Thailand U.S. 206,817 U.A.E.*

Low growth

Belgium Brazil* Canada Finland France Germany Greece Ireland Israel Italy Luxembourg Mexico Netherlands Poland Portugal Romania Russia S. Korea Spain Sweden Taiwan Turkey U.A.E.* U.K.

4,422 Belgium Denmark 14,209 Finland 19,768 Germany 2,051 Ireland 24,785 Israel30,671 Italy 2,006 Luxembourg 1,520 Portugal 2,401 S. Korea 24,799 U.K. 481 U.S. 5,193 5,483 4,361 1,739 1,281 11,127 8,896 12,717 3,287 2,254 4,475 1,227 23,789

2,313 33 6,607 1,182 9,945 356 17,034 480 1,591 China* 2,941 Colombia* 294 Ecuador* 1,209 Indonesia* 192 Nigeria* 6,322 Singapore 3,330 South 397Africa Thailand* 294 Venezuela* 1,137 Argentina 3,151 1,026 Australia 1,375 Chile 16,331 India 812 Malaysia 622 Morocco 5,392Arabia Saudi 193 1,047 1,797 16,617 133,766

Australia Brazil* Chile* China* Colombia* Ecuador* Hong Kong* 63,522 India* 1,982 Indonesia* 593 New Zealand* 1,196 Russia* 294 Saudi Arabia 974 Singapore* South4,535 Africa 3,854 Turkey* 4,506 Venezuela* 5,160 Argentina 12,854 Morocco 1,181 Norway 5,213 Sweden 2,388 931 1,695


11,644 1,692 742 7,007 1,078 337 1,425 1,764 487 421 2,231 251 784 1,144 595 1,166

High growth

4,830 408 1,594 203

High growth

Low growth

Austria Belgium Brazil* Bulgaria Canada Czech Republic Denmark France Germany Italy Mexico Norway Poland Portugal Russia* Spain Sweden Switzerland Taiwan U.A.E.*

9,767 10,880 23,647 Brazil* 917 Bulgaria 42,179 Greece3,935 Hong 8,473 Kong India66,918 Luxembourg 71,432 Malaysia 37,397 Romania 10,415 Singapore 9,454 Spain 7,439 Switzerland* 4,165 Taiwan 19,199 Turkey* 28,826 U.A.E.* 7,669 U.K. 14,682 U.S. 3,917 Venezuela* 3,424

Finland Greece Ireland Israel Luxembourg Netherlands Romania S. Korea Turkey U.K. U.S.

Austria5,107 2,840 Belgium 3,548 Canada Czech4,326 Republic 951 Denmark 13,366 Finland France1,929 13,298 Germany Ireland7,770 Israel65,538 Italy531,838 Japan Netherlands Norway Poland Portugal S. Korea Sweden

Low growth

7,745 210 730 745 2,223 Australia 277 Chile* 1,573 China 456 Ecuador* 744Kong Hong 10,216 Indonesia* 5,165 Malaysia* 1,266 Morocco 2,700 Nigeria* 1,106Arabia* Saudi 25,132 Singapore* 191,255 South Africa 1,059 Venezuela* Argentina 3,455 Colombia 3,307 India 15,803 Japan 1,141 New Zealand 4,544 Thailand* 1,682 25,672 23,030 1,216 1,181 7,206 20,501 4,943 4,916 1,868 978 2,605 4,179

34,097 3,708 84,431 China* 1,547 Colombia* 3,187 Ecuador* 3,404 Indonesia* 4,442 Mexico1,638 Morocco* 1,136 Nigeria* 3,067 Russia*2,501 Saudi 9,968 Arabia* South Africa 672 11,835 Argentina* 4,425 Australia Chile 9,200 New 94,825 Zealand 3,886 Thailand 5,651

13,902 1,365 616 1,721 3,631 307 548 5,860 1,121 4,289

High growth

1,844 11,598 1,784 2,287 1,503

High growth


Aon Benfield

9

Looking Ahead: Growth Projections 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

The United States will remain the largest property casualty

growth for the next five years, for the overall insurance market,

insurance market, representing an estimated 37 percent

and for motor, property, and liability. These projections

of global premium. China will surpass Japan to become

are based on a weighting of historic premium growth rates

the second largest market, with an expected 9 percent of

with projected country GDP and population estimates.

premium. But note that the U.S. projected annual growth rate is 2.7 percent while China’s is over 11 percent.

By 2018, we expect the global insurance market to grow by 18 percent to a total direct written premium of USD1.6 trillion.

Digging deeper into each line reveals similar trends. In each

Motor insurance will remain the largest property casualty

line the U.S. will remain the largest property casualty insurance

segment, accounting for 47 percent of total direct written

market, but with relatively limited growth prospects.

premium, followed by property (33 percent) and liability (21 percent).

Global 2018 premium projections Projected direct written premium by line 20%

800

Total DWP: $1,627

700 600

529

Motor 47%

15%

453

341

296

200 2013

0

2018

2013

Property

2018

2013

Liability

Motor 2018 Projected

Projected annual growth %

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

10

Liability 21%

2018

2013 Country

Property 33%

757 633

300

100

Total Growth: 18%

17%

500 400

2018 projected premium mix


Motor Motor, which accounts for USD633 billion of global premium

We project no change in rank amongst the top five global motor

today, will experience continued rapid growth with a

markets. Despite more limited population growth, wealth

20 percent five year rate increasing to USD757 billion of direct

generation continues in these countries at a rapid pace, with

written premium. Such projections are easy to understand,

more families owning multiple cars, supporting continued

given that we expect continued strong population growth,

steady growth; developing countries have a long way to go to

particularly in developing markets—and an early sign of middle

catch up with motor penetration in these top markets.

class life is owning a car, usually with auto insurance as a compulsory addition.

Later in the Study we discuss the changing dynamics of the

China is already the second largest auto market, and will almost

globally, but further off in the future; the technologies gaining

certainly retain this position given its projected 11.3 percent

momentum in the U.S. will be slower to make their way into the

annual growth. Yet we must also express a note of caution: the

developing markets. As such, despite slow projected growth in

widely expected partial de-tariffing of China motor business

the U.S., global growth will remain strong.

U.S. motor insurance market. We do anticipate similar changes

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.

Motor 2018 premium projections Projected direct written premium by country

2018 projected premium: USD757 billion

12% Brazil

2018 est. premium for country

10%

Canada Rest of Americas China

U.S. 8% 6%

Japan South Korea

4% Middle East & Africa

or

an d

ia

le

a

a

U.K.

Rest of Euro Area

nt Ar ge

lom Co

do n In

Ec u

Rest of Europe

in

a

ala ys ia

ud iA

bi

6.7 796

Ch i

2.6

es

1.6

Th ail

1.7

ad

5.4

di

0.8

M

3.4

ra b

a

7.5

Rest of APAC France Germany

Sa

Ch in

2.3

In

108.4 0%

ia

2%

2013 Country

2018 Projected


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

Aon Benfield

11

Property Catastrophe risk potential is another important consideration

China is by far the largest of the rapidly growing property markets in the world with an 8 percent expected growth rate,

for property lines. Economic growth and urbanization are

representing nearly USD20.5 billion of direct written premium,

creating greater risk concentrations, often in catastrophe

which will tie China with Japan for the fifth largest property

exposed areas. Property premium growth is driven, in part,

market in five years.

by catastrophe losses—both actual and potential. Aon Benfield can use its understanding of global catastrophe risk to produce

Many other countries with high expected premium growth

an optimal blending of target growth and acceptable risk.

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.

Property 2018 premium projections

Projected growth rate by country


10%

Brazil


8%

U.S.

6%

Canada Rest of Americas China Japan South Korea Rest of APAC France

4%

Germany 2% 2.3

0.9

20.5

1.9

3.3

2.1

1.5

2.9

4.7

0.7 796 ria ge Ni

a

ico ex M

di In

ia ra b

Sa

Ne w

2013 Country

2018 Projected


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


12

Rest of Euro Area

ud

iA

ala ys ia

nd ala Ze

M

g Ko n

a ng

in

Ho

Ch

r do ua Ec

Th

ail

an

d

0%

U.K.

Middle East & Africa Rest of Europe


Liability Liability insurance is the smallest of the global property

China is the fastest growing market for liability

casualty segments, at approximately half the size of the global

with 16 percent projected annual growth—though

motor insurance market. The U.S. will remain the largest

this will not yet make it a top five market.

market by a wide margin, and with Japan, will grow faster than other top five markets.

Liability 2018 premium projections

Projected growth rate by country


18%

14%

Canada Rest of Americas

Brazil


16%

China Japan

12%

South Korea

U.S.

10%

Rest of APAC

8%

France

6%

Germany

4% 2%

14.8

7.3

0.5

1.6

0.7

2.5

2.0

0.7

0.3

0.6 796

Rest of Euro Area

Rest of Europe

nd

ia

Ze

ala

ra b iA

2013 Country

U.K.


Ne w

Sa

ud

ala ys ia

g

Ho

M

ng

Ko n

a di In

sia ne

bi

a

do In

lom

do

r Co

ua Ec

nt Ar ge

Ch

in

in

a

a

0%

2018 Projected

Rank

DWP (USD B)

United States

1

France

2

Japan*


Rank

DWP (USD B)

133.8

1

150.0

2.3

18.1

2

19.6

1.6

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


Aon Benfield

13

Reinsurance Global reinsurance premium by year (USD billions)

We do not expect the reinsurance market to grow as rapidly as the primary market. Excess capital in the insurance market

180 147

142 142 142

135

153

145 137 140

155

165

170

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

90

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

45

expecting to make significant returns on assets. Whether such 0

changes will serve to stimulate new reinsurance demand, 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Our analysis so far has focused on primary insurance direct written premium growth without considering the

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.

impact of reinsurance. This approach is largely due to

By country, reinsurance growth estimates vary from 5 percent

data availability: reinsurance data is much more limited

annual growth in South Korea to negative growth in Japan.

and often distorted by the reporting of intercompany

On average, we expect the mature reinsurance markets to

reinsurance within global insurance conglomerates.

grow about 2 percent per year while developing markets will

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.

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.

14


Uncovering Growth Opportunities 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.

Profitability

Demographics

Contender Geographies

Deep dive on strategy, organic growth and M&A opportunities

Political Stability

Scale Regulation

Growth

Broker Surveys

Our analysis entails an evaluation of basic insurance economics,

Aon Benfield has expanded the analysis this year by introducing

such as country market scale and insurance penetration, country

five year projected insurance premiums in each country,

profitability and loss ratio volatility, and overall out or under

which we have added to the Country Opportunity Index

performance relative to other opportunities. We couple those

on the next page. We have selected and analyzed several

basics with an understanding of the larger macroeconomic

specific trends that we are seeing in the market, including

environment including population changes, GDP measures,

auto, health, crop, and cyber insurance. Based on our

and an understanding of the legal and regulatory systems.

experience with carriers, reinsurers, agents, brokers, and other

Finally by combining this fact based information with qualitative

insurance service providers, we highlight some of the key

feedback from Aon’s local teams we can identify attractive

trends and emerging insurance opportunities in each area.

opportunities in each country. Our process reveals specific opportunities from which to form executable growth strategies.

Aon Benfield

15

Country opportunity index

Aon Benfield country opportunity index

To summarize and sort between the various countries

5yr Cumulative Net Combined Ratio

outlined in this section, we have updated our Country Opportunity Index. The index identifies countries with a desirable mix of profitability,

5yr Annualized Projected Growth Rate

Political Risk Assessment

Quartile 1 Saudi Arabia*

91.5%

8.1%

Medium Low

growth potential and a relatively stable political

Ecuador*

86.9%

7.8%

High

environment. For growth potential we used the

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 Medium

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

87.6%

4.4%

Norway*

89.6%

2.9%

Low

Brazil*

85.7%

3.7%

Medium

Australia

99.7%

4.4%

Low

Switzerland*

Quartile 2

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.

South Africa*

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 Medium

Mexico

96.9%

4.2%

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

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.

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

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

Quartile 4

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.

16


Insurance Trends: Risks and Opportunities 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 trends Personal auto insurance, which for many years has been the

Companies are seeking ways to better leverage the data they

stable cash flow product of the property casualty universe,

have accumulated from UBI. Two commonly cited applications

is currently undergoing a revolution due to advances in

are teen driving and commercial trucking monitoring. And the

technology.

data accumulated from UBI may not only help to sell additional

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

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.

in vehicle safety have focused on mitigating the effects of

The insurers that have been successful in growing are doing

human error or negligence. The results speak for themselves:

so with data. Through market segmentation and targeted

the U.S. has seen a 15 percent reduction in crashes for cars

advertising, auto specialist insurers in the U.S. have expanded

with an automatic braking system for example. Between

their market shares—growing at an average annual rate of 7

2000 and 2011, driver deaths due to rollover crashes have

percent—while traditional personal lines insurers’ premium

fallen more than 50 percent for passenger cars. And for

has on average been static over the past five years.

SUVs, the death rate has fallen roughly 90 percent.

Looking further ahead, driverless cars have the potential to

People in large metropolitan areas are changing the way

radically change the business model for auto insurers. Personal

they get around, from drive share programs to semi-

auto insurance is 45 percent of global premium, and it has

private car services such as Uber and Lyft. This is forcing

long provided ballast and stability for multiline insurers. An

the auto insurance industry to think about how to create

insurance world without this ballast would have very different

and better price policies for uberX and Lyft drivers, who

risk dynamics. For example, we estimate that without personal

need a commercial policy when they have passengers and

auto, loss ratio volatility for the U.S. market would have been

personal policies when they do not. Recent incidents have

nearly 40 percent higher for the period 1995 to 2013.

posed questions about how these policies overlap.

Such changes, while not on the immediate horizon, could

Telematics and usage-based insurance (UBI) are becoming

increase industry capital intensity and lower premium to

widespread across the industry, with many of the largest U.S.

surplus ratios by more than 30 percentage points, from

and U.K. auto insurers now having some form of UBI. Insurers

0.84x to 0.50x. We estimate that surplus needed in the

believe UBI will allow them to better segment price and risks

U.S. to support personal auto is USD100 to 125 billion.

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

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.

offset the up-front investment in telematics infrastructure.

Aon Benfield

17

U.S. health insurance market In 2014, the individual mandate of the Affordable Care Act—

The implications for growth in the private health care insurance

aka “Obamacare”—came into effect. With the ACA, state-run

market are significant. We estimate that if 20 percent of U.S.

public health care exchanges have become operational, and

employers move to private exchanges, then an additional

as of May 2014 approximately 20 million Americans have

USD350 billion in premium will flow into the private health

purchased insurance through these public exchanges. At

insurance market. Twenty percent is the minimum level

the same time, and with much less controversy, a revolution

of interest quoted in recent surveys. The median is 33

has been taking place in the private health care insurance

percent—if one in three U.S. employers move to a private

market—the advent of corporate health care exchanges.

exchange, this will generate an additional USD500 billion

Aon Hewitt has been a pioneer in this market, with 330,000

of premium flow into the market. As a reference point, this

employees enrolled in its Corporate Health Exchange for 2014.

number is roughly the size of the entire U.S. property casualty

Currently, about 60 percent of U.S. workers who receive

insurance market, as shown in the statistics on page seven.

health insurance through their employers are covered under

While the potential premium growth can seem staggering,

self-insured plans. For companies with over 5,000 employees

insurers must also consider the capital required to support

this number is even higher—by some estimates as many as

this growth. We estimate that the U.S. health insurance

94 percent of larger employers run self-insured health plans.

industry’s capital adequacy, as defined by A.M. Best’s BCAR

In these cases, the role of the health insurer is simply to

model, is currently 225 percent—roughly in line with the

process payments and bill claims back to the employer—hence

U.S. property casualty industry’s 230 percent. Depending

these plans are called Administrative Services Only plans.

on how much premium flows into private exchanges,

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

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)

trends, companies are seeking ways to manage costs while continuing to provide essential benefits to their employees.

20% (minimum)

350

105

One such way is to rethink the traditional model of a self-

33% (median)

500

150

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.

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,

The private exchange market is still small; analysts at JP Morgan

it is an opportunity to diversify into new lines of (potentially

estimate that less than 1 percent of active employees will be

uncorrelated) business. For reinsurers it is an opportunity as

enrolled in private exchanges in 2014. Yet interest is high,

well. Reinsurance can provide a substitute to traditional capital

with an average of 40 percent of employers in recent surveys

and help health insurers lower their capital requirements by

saying they are considering a switch to a private exchange.

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.

18


Earlier, we mentioned the potential market changes that could

Finally, the Ponemon study included a shocking statistic: that

take place if driverless cars cause the personal auto insurance

roughly 19 percent of businesses are expected to have a data

market to shrink. Perhaps health insurance will become the

breach in the next 24 months. These percentages vary by

new “ballast” to property casualty commercial lines volatility

industry, but every company in today’s economy is vulnerable to

in the future.

the risks of a cyber attack.

Average total cost of a data breach (USD millions)

U.S. cyber insurance market In the past year, cyber risk has come into the mainstream as a

+3% trend

8

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

6

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

4

now see a data breach as inevitable: not if but when. Different sources count data breaches differently but all agree

2

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.

2006 2007 2008 2009 2010 2011 2012 2013 2014

Excludes breaches with more than 100,000 records

From its beginnings 15 years ago, cyber insurance has

Number of U.S. data breaches by year

now become a standard product offered by many large +11% trend

800

0

commercial insurers. Common coverage includes thirdparty liability protection as well as first-party indemnity protection for breach response expenses, business

600

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,

400

a growing number of entrants are offering the coverage, and prices are beginning to fall as competition expands.

200

Takeup of cyber insurance is increasing, and the U.S. cyber 0

market is now estimated at roughly USD1.5 billion in gross 2005 2006 2007 2008 2009 2010 2011 2012 2013

written premium. Aon Risk Solutions has seen cyber premium

All studies suggest that 2013 was a banner year, of sorts, for data

rise at a compound annual growth rate of 38 percent over the

breaches. Notably, 2013 saw eight mega breaches, each more

last five years, according to data from the Aon GRIP platform.

than 10 million records; the previous high was the five breaches

Nearly one-third of companies buy some kind of cyber policy.

in 2011, according to Symantec. In total, 552 million identities were exposed—roughly 7.8 percent of the world population.

Main Street businesses have been slower to adapt than large corporations. This presents a significant market opportunity for

And while breaches are increasing, the cost of a breach is

enterprising insurers, given that small and medium enterprises

increasing as well. Data from the Ponemon Institute suggest

are often the most vulnerable to a cyber attack. A study by

that the cost of the average breach is now USD5.9 million—and

Verizon found that 71 percent of cyber attacks are targeted

this number excludes breaches of more than 100,000 records.

at companies with fewer than 100 employees. Moreover,

The Ponemon study also indicates that customers are fleeing

attacks against small businesses shot up by 91 percent in

from breached companies more than in the past: lost business

2013. Small businesses often lack the time and resources to

following a breach rose 15 percent last year.

develop sophisticated cyber risk management strategies.

Aon Benfield

19

Many smaller businesses are responding to such limitations by

Cyber coverage must evolve in order to meet the needs of

outsourcing their network security to managed security service

buyers, and underwriting practices will need to evolve with it.

providers (MSSPs). While MSSPs can provide valuable services

Cyber underwriting is currently focused more on compliance

to help companies protect themselves, they are not insurers.

with industry standard practices than on actual risk assessment.

Insurers have a vital role to play, by providing indemnity

And cyber risk still has an image challenge to overcome: often, it

protection as well as sharing their security expertise in this area.

is seen by companies merely as an IT problem, not tied into the

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

larger ERM framework. This suggests a failure by corporate risk managers to translate cyber exposure into a potential bottomline impact that executives can understand and manage.

notify customers in the event their personal information is

China crop insurance

compromised—and the costs of doing so can be considerable.

Global population growth and emerging middle classes are

Yet the potential for other kinds of expense is significant.

driving a rising demand for agricultural products including

Increasingly, lawyers are pursuing directors and officers in the

those used for animal feed.

event that a company fails to protect its data. Target’s data breach has generated at least 40 lawsuits against the retailer.

China is the second largest crop insurance market globally, with USD3 billion premium of a global USD22 billion market.

Moreover, the current cyber insurance policies focus solely on

The U.S. market is much larger, and fairly mature. The China

the direct costs and ramifications of a data breach. They do not

market, in contrast, is primed for growth. China’s population

contemplate the risk a cyber attack can cause other kinds of

is expected to grow by 4 percent by 2017 totaling nearly 1.4

damage. Most property and general liability policies exclude

billion people. Even more impressive, GDP is expected to grow

cyber risk. The first cyber difference-in-conditions policy to fill

by 50 percent by 2018. These significant expectations, coupled

such coverage gaps was just made available earlier this year.

with the Chinese government’s focus on providing government support to the agricultural industry and rural population,

For Main Street, the coverage options can be confusing,

warrant attention when considering growth opportunities.

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

Global crop insurance premium (USD billions)

USD25,000 limit. Given the size of the potential costs discussed

14

previously, such coverage limits are very low, and may create

12

a false sense of security that businesses are “covered.”

10 8 6 4 2 0

20


USA

China

Europe

Canada

India Latin America

Since 2004, China crop premium has quadrupled, from

China premium and claims performance

RMB5 billion in 2007 to RMB20 billion in 2012. The business Loss ratio

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 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

Claims

Premium

20

80%

15

70%

10

60%

5

50%

0

2005 2006 2007 2008 2009 2010 2011

Loss ratio

of players in the market are simultaneously increasing. In the

Premium/claims (RMB billions)

has also been relatively profitable, with an average 63 percent

40%

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.

portfolio. It also incorporates correlation coefficients between provinces so that we can model multi-province portfolios.

Aon Benfield

21

Global Risk Parameters 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

level of economic activity, and other macroeconomic factors. It

line for 49 countries worldwide. By systemic risk, or volatility,

also includes the risk to smaller and specialty lines of business

we mean the coefficient of variation of loss ratio for a large

caused by a lack of credible data. For many lines of business

book of business. Coefficient of variation (CV) is a commonly

systemic risk is the major component of underwriting volatility.

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

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.

Coefficient of variation of gross loss ratio by country Property

Motor Thailand Taiwan South Korea Israel Japan France Switzerland Hungary Spain Australia Bolivia Austria Germany Czech Republic El Salvador Netherlands Malaysia Chile Mexico Uruguay Italy India Vietnam Peru Brazil U.K. Dominican Republic Argentina Poland Pakistan Honduras Canada U.S. China Colombia Turkey Singapore Venezuela Ecuador Denmark Indonesia Slovakia South Africa Panama Nicaragua Romania Hong Kong Greece Philippines

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% 19% 19% 21% 22% 22% 22%

34% 37% 43%

50%

Venezuela Denmark Netherlands South Africa Germany Australia Bolivia Austria Italy Israel U.K. Canada Spain Switzerland France China Japan Malaysia Chile India Ecuador Hungary Poland Turkey El Salvador Uruguay Colombia Honduras South Korea U.S. Argentina Slovakia Panama Nicaragua Romania Vietnam Pakistan Dominican Republic Taiwan Indonesia Brazil Hong Kong Greece Philippines Peru Mexico Singapore 70% Thailand

10% 12% 12% 14% 15% 16% 18% 18% 18% 21% 22% 22% 23% 25% 27% 33% 33% 33% 33% 33% 34% 34% 35% 35% 36% 39% 40% 40% 42% 42%

Europe, Middle East & Africa Asia Pacific Americas

52% 53% 54% 55% 57% 57% 58% 61% 66% 68% 68% 69% 77% 83% 85%

99% 110%

124%

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

22


Fidelity & Surety

Credit

Workers’ Compensation

Marine, Aviation & Transit

Accident & Health

General Liability

Property— Commercial

Property— Personal

Property

Motor— Commercial

Motor— Personal

Motor

Coefficient of variation of loss ratio for major lines by country

Americas Argentina Bolivia

14%

52%

57%

8%

18%

16%

29%

8%

240%

Brazil

13%

68%

46%

65%

83%

51%

40%

82%

Canada

15%

22%

17%

34%

35%

38%

64%

102%

Chile

10%

33%

44%

61%

34%

Colombia

16%

40%

29%

28%

68%

Dominican Republic

13%

61%

88%

72%

Ecuador

19%

34%

39%

161%

El Salvador

9%

36%

18%

137%

Honduras

15%

40%

Mexico

11%

99%

Nicaragua

34%

55%

67%

Panama

22%

54%

18%

Peru

13%

85%

Uruguay

11%

U.S.

16%

Venezuela

18%

71%

97% 46%

225% 71%

45% 200% 85%

56%

23%

28%

38%

54%

39%

114%

39% 14%

24%

43%

47%

36%

10%

27%

71%

20%

314%

Asia Pacific Australia

8%

16%

23%

32%

54%

33%

57%

29%

21%

19%

21%

88%

21%

66%

6%

28%

68%

117%

46%

139%

33%

11%

10%

18%

10%

97%

31%

54%

97%

China

16%

Hong Kong

43%

69%

India

12%

33%

Indonesia

21% 6%

Japan

16%

Malaysia

10%

33%

Pakistan

14%

58%

Philippines

70%

83%

Singapore

18%

111%

10%

30% 62% 75% 123%

124%

41% 85%

88%

124%

48%

49%

20%

69%

21%

33%

54%

22%

45%

22%

11%

23%

South Korea

5%

6%

42%

33%

Taiwan

5%

5%

66%

43%

Thailand

3%

124%

Vietnam

13%

57%

Austria

8%

18%

Czech Republic

9%

124%

166% 31%

52% 49%

Europe, Middle East & Africa

Denmark France Germany

51%

19%

12%

11%

10%

25%

15%

30%

6%

27%

30%

28%

36%

23%

60%

16%

30%

26%

20%

19%

83%

84%

45% 31%

9%

15%

50%

77%

Hungary

8%

34%

Israel

5%

21%

Italy

12%

18%

25%

19%

41%

43%

9%

12%

20%

49%

25%

44%

Poland

14%

35%

Romania

37%

57%

Slovakia

22%

53%

South Africa

22%

Greece

Netherlands

13%

Spain

8%

Switzerland

7%

Turkey

17%

U.K.

13%

53%

23%

12%

62%

37%

39%

41%

13%

33%

37%

18%

8%

47%

86%

35%

43%

18%

58%

86%

25%

32%

6%

27%

25% 12%

17%

35%

18%

22%

63%

94%

14% 8%

48%

23%

139%

Aon Benfield

23

U.S. Risk Parameters For the U.S. risk parameters, we use data from 13 years of

The charts below show the loss ratio volatility for each

NAIC annual statements for 2,108 individual groups and

Schedule P line, with and without the effect of the

companies. Our database covers all 22 Schedule P lines

underwriting cycle. The effect of the underwriting cycle is

of business and contains 1.9 million records of individual

removed by normalizing loss ratios by accident year prior to computing volatility. This adjustment decomposes loss

company observations from accident years 1992-2013.

ratio volatility into its loss and premium components.

Coefficient of variation of gross loss ratio (1992-2013) All Risk Private Passenger Auto Auto Physical Damage Commercial Auto Workers’ Compensation Warranty

No Underwriting Cycle Risk 14%

Private Passenger Auto

17%

Auto Physical Damage

24% 26%

18%


19%

32%

Warranty

36%

Medical PL – Occurrence

Commercial Multi Peril

36%


Other Liability – Occurrence

38%

Other Liability – Occurrence

Special Liability

39%

Special Liability

Other Liability – Claims-Made

39%

Other Liability – Claims-Made

43%

15%

Commercial Auto

Medical PL – Occurrence

Medical PL – Claims-Made

13%

Medical PL – Claims-Made

32% 36%

31% 26%

30% 26% 32%

Homeowners

46%

Homeowners

39%

Products Liability – Occurrence

47%

Products Liability – Occurrence

37%

Other

Other

52%

Reinsurance – Liability

67%

Reinsurance – Liability

Fidelity and Surety

68%

Fidelity and Surety

International Reinsurance – Property

56%

Reinsurance – Property

57% 58%

Special Property

92%

Special Property

Reinsurance – Financial

93%

Reinsurance – Financial

Products Liability – Claims-Made

Products Liability – Claims-Made

99%

Financial Guaranty

The underwriting cycle acts simultaneously across many lines

49%

International

72% 86%

48% 44%

139%

63% 49%

Financial Guaranty

101%

U.S. underwriting cycle impact on volatility

of business, driving correlation between the results of different lines and amplifying the effect of underwriting risk to primary

Line of Business

Impact

insurers and reinsurers. Our analysis demonstrates that the cycle

Reinsurance—Liability

55%

increases volatility substantially for all major commercial lines,

Other Liability—Claims-Made

51%

Other Liability—Occurrence

42%

Workers Compensation

40%

rated and thus show a lower cycle effect, with private passenger

Commercial Auto

36%

auto volatility only increasing by 6 percent because of the cycle.

Medical PL—Claims-Made

37%

Special Liability

30%

Homeowners

19%


15%

Private Passenger Auto

6%

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-

24


U.S. Profitability In many areas of the world, profitability data is both scarce

Over the past 10 accident years the property casualty

and coarse. In the U.S., however, the NAIC statutory

industry achieved a 99 percent combined ratio. However,

financial statements provide a wealth of detailed information

insurance companies between the 25th and 75th percentiles

summarizing profitability of property casualty insurance

had combined ratios ranging from 92 percent to

by company, line of business, and geography.

103 percent, illustrating that there is ample opportunity

The table below summarizes current net premium volume

for individual companies to outperform average results.

and net accident year loss ratio, expense ratio, and

The magnitude of the opportunity to outperform varies

combined ratio over the last 10 years. The last five columns

by line of business. For example, top quartile private

summarize individual company 10 year combined ratios

passenger auto liability writers achieved 98 percent

at the 10th, 25th, 50th, 75th, and 90th percentiles. The

combined ratio or better, which is four points better than

percentiles are computed on a premium weighted basis.

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

Private Passenger Auto Liability

Net loss ratio 10yr

Net expense ratio 10yr

Net combined ratio 10yr

10yr combined ratio percentiles 10%

25%

50%

75%

90%

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%

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%

111

76%

31%

107%

52%

52%

74%

115%

115%

471,664

71%

28%

99%

90%

92%

99%

103%

104%

Medical Professional Liability

International All Lines

2013 NEP USD Millions

Aon Benfield

25

U.S. Reserve Adequacy The analysis reveals that commercial lines continued to

Reserve releases in the U.S. are now in their eighth consecutive year, heightening concerns that insurers are cutting reserves

move further into an overall deficiency position of USD2.8

too aggressively. We can form an independent opinion

billion at year end 2013 compared to an estimated USD0.9

about the adequacy of statutory reserves using the high

billion deficiency at year end 2012. Reserve positions

quality, uniform data at the legal entity available through

deteriorated across the commercial lines sector, which

the NAIC Schedule P in statutory accounts. These accounts

includes commercial property, commercial liability, and

provide U.S. regulators with a clear view into insurance

workers’ compensation. Financial guaranty moved to

companies and are part of a very effective system of solvency

a slightly less deficient position, though it represents a

regulation based on consistent and transparent reporting.

small portion of the overall commercial lines sector.

Five years ago Aon Benfield started publicly tracking the

The drivers of year-over-year change in our reserve estimates

reported reserve adequacy of U.S. companies. Each year

are illustrated in the waterfall chart on the next page. It shows

we analyze the aggregated net loss development data

the year end 2012 estimate of the property casualty industry

by Schedule P line of business. Working at an aggregate

reserve redundancy was USD9.2 billion. During calendar

level allows our actuaries to use different methods, and

year 2013, the industry released USD14.8 billion of reserves.

to weight the results in different ways, than is possible for

Offsetting the impact of reserve releases were two factors:

company actuaries who are working with smaller and less

2013 calendar year favorable loss emergence and redundantly

stable data sets. Unlike some other public studies, each of

booked reserves in the 2013 accident year. Favorable

our reports has indicated overall reserve redundancies.

development of case-incurred losses in calendar year 2013 contributed to a decrease in ultimate loss estimates of USD11.9

The table below summarizes the analysis of year end 2013

billion, while the 2013 accident year contributed an additional

data. The overall industry redundancy position decreased

USD0.2 billion of reserve redundancy. The sum of these pieces

to USD6.5 billion at YE2013—equivalent to only 1.1 percent

drives our year end 2013 redundancy estimate of USD6.5 billion.

of total booked reserves. This compares to an USD9.2 billion total industry redundancy position at YE2012, while USD14.8

When we separate the year-over-year waterfall into personal

billion was released by insurers during 2013. The amount of

and commercial lines, a different picture emerges. On the

reserves released in 2013 was the highest since 2009. However,

personal lines side, a reduction in booked reserves during

a closer inspection of the results shows a fundamental shift in

the 2013 calendar year was somewhat offset by favorable

the view of reserve adequacy on the commercial lines sector.

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)

Booked reserves

Remaining redundancy at YE 2013

2009

2010

2011

2012

2013

Average

Years at run rate

Personal Lines

128.5

137.9

9.3

5.8

6.7

7.6

7.1

6.0

6.6

1.4

Commercial Lines

Line

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


146.1

141.5

(4.6)

(0.5)

(1.6)

(0.0)

0.0

0.6

(0.3)

N/A

20.5

19.2

(1.4)

7.0

0.4

(0.4)

1.4

3.7

2.4

N/A

569.3

575.8

6.5

18.5

10.5

12.7

12.2

14.8

13.7

0.5

Financial Guaranty Total

26

Favorable or (adverse) development

Estimated reserves


Drivers of 2013 reserve redundancy or deficiency (USD billions) All Lines 12

+9.2

(14.8)

8

+11.9

+0.2

+6.5

4 0 -4 -8 -12

Personal Lines 12

+10.1

(6.0)

8

+1.7

+3.6

+9.4

(3.4)

(2.9)

4 0 -4 -8 -12

Commercial Lines 12 8 4 0

(0.9)

(8.8)

+10.2

2012 Year End Reserve Redundancy/Deficiency

2013 Calendar Year Change in Booked Reserves

2013 Calendar Year Favorable/Adverse Loss Emergence

-4 -8 -12 2013 Accident Year Redundancy/Deficiency

2013 Year End Reserve Redundancy/Deficiency

We estimate that companies will continue to release reserves

As we have discussed in past editions of the Study,

through year end 2014, possibly extinguishing overall

understanding reserve risk is critical for effectively modeling

redundancy in the industry. Through the first quarter

company solvency. It is also a notoriously difficult problem:

of 2014 companies have already released an additional

whereas all companies face broadly similar insurance risks, such

USD5.4 billion of reserves. USD4.1 billion of this release

as weather, legal, social, and medical trends, each company’s

is from personal lines, while commercial lines released

reserving practices are idiosyncratic. Moving forward, rate

an additional USD1.4 billion. The release in the personal

adequacy and rate monitoring—not on an aggregate premium

lines may be attributable to conservatism in booked

basis but on a rate per exposure basis—will be critical to the

results at year end 2012 related to Superstorm Sandy.

operating results of companies. Aon Benfield Analytics has

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.

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. Aon Benfield

27

Global Correlation Between Lines Correlation between lines of business is central to a realistic

The Study determines correlations between lines within

assessment of aggregate portfolio risk, and in fact becomes

each country. Correlation between lines is computed

increasingly significant for larger companies where there is little

by examining the results from larger companies

idiosyncratic risk to mask correlation. Most modeling exercises

that write pairs of lines in the same country.

are carried out at the product or business unit level and then

Aon Benfield Analytics has correlation tables for most

aggregated to the company level. In many applications, the

countries readily available and can produce custom analyses of

results are more sensitive to the correlation and dependency

correlation for many insurance markets globally upon request.

assumptions made when aggregating results than to all the

As examples, tables for Colombia and France appear below.

detailed assumptions made at the business unit level.

Fidelity & Surety

General Liability

Life


Motor

Property

Special Liability

Special Property

Surety

Accident & Health

Crop & Animal

Accident & Health

Colombia

25%

12%

27%

42%

4%

34%

17%

13%

26%

-1%

24% 8% 46%

46%

27%

29%

8%

-10%

36%

17%

12%

62% 8%

12%

29%

General Liability

27%

15%

48%

Life

42%

24%

8%

46%

4%

7%

4%

-10%

12%

Motor

34%

46%

29%

36%

62%

8%

Property

17%

27%

8%

17%

37%

8%

35%

Special Liability

13%

19%

17%

18%

27%

19%

26%

13%

Special Property

26%

6%

8%

14%

47%

12%

46%

39%

14%

-1%

4%

5%

1%

13%

-3%

9%

4%

1%

Surety

15% 48%

4%

25%


29%

7%

Crop & Animal Fidelity & Surety

19%

6%

4%

17%

8%

5%

18%

14%

1%

37%

27%

47%

13%

8%

19%

12%

-3%

35%

26%

46%

9%

13%

39%

4%

14%

1% 11%

11%

Motor

Property

Reinsurance

Accident & Health

General Liability

Accident & Health

France

80%

31%

-5%

46%

55%

65%

88%

27%

66%

General Liability

80%

Motor

31%

55%

Property

-5%

65%

27%

Reinsurance

46%

88%

66%

51% 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.

28


World Bank relative ease of doing business

Aon Hewitt people risk assessment

Aon terrorism risk assessment

Political risk assessment

Corporate tax rate

Unemployment rate

Inflation rate

Net foreign direct investment— USD billions

General government debt as % of GDP

Actual individual consumption as % of GDP

Government consumption as % of GDP

GDP Per Capita— PPP, USD

Population 5yr annualized growth

Population— millions

GDP 5yr real growth

Country

GDP—PPP, USD billions

Macroeconomic, demographic, and social indicators

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

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

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

Brazil

High More difficult

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

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

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

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

Easiest 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

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

n/a More difficult

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

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

Luxembourg

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

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 Russia

Easiest

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

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 Easiest

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

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 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

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

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

Venezuela

Very high Most difficult

Aon Benfield

29

Big Data and Insurance 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,

How does traditional insurance data compare to Big Data? Insurance data

Internet big data

Sparse, expensive data

Massive, free data

without volume. Variety expresses the broad range of data

Policy, claim, client

Internet of Things

types, from free-form text to photographs, and from video to

Expensive to collect & maintain

manufacturing and scientific instrument output. Velocity refers

Static

to the real-time and streaming nature of many types of Big

Structured

and velocity. Volume is obvious: the data would not be 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.

Inhomogeneous within type

Free: user contributed, digital exhaust Streaming Unstructured—variety Homogeneous within type

Potentially high value density

Low value density

Low to medium volume

Very high volume

The second theme relates to computational capacity. Highvelocity 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 action Big 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.

30


Data

Information

Insight

Action

New data sources The data available to insurers today far exceeds that available

construction project, changes in roofs, and, in the infrared

to underwriters twenty five years ago. As catastrophe

spectrum, an assessment of the energy efficiency of each home.

modeling was introduced to the industry, an early complaint

Beyond property, there are exciting potential applications in

was that the “data is not available.” But, driven by huge

agricultural insurance, logistics, and exposure analysis. For

losses from Hurricane Andrew in 1992 and the Northridge

example, is reported revenue consistent with parking lot usage?

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

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

and personal fitness devices; and streaming location data.

in the data they collect. Related data comes from smart

Today’s technology has enabled a new generation of micro-

based insurance feeds, time-stamped location data has a

satellites that can be deployed relatively cheaply and in

myriad of insurance applications, particularly for auto.

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

phone records of location. Not as detailed as auto usage

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.

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

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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.

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

The next step could be a lifestyle policy covering all first and

risk management and behavioral “nudges,”

third party liabilities for an individual, replacing separate

continuing the trend of greater safety and fewer

homeowners, auto and umbrella policies. Such a policy

accidents in mainstream insurance products.

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.

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• New data sources will continue to be tapped for


• 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.

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

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Asset portfolio risk

Insurance portfolio risk

Portfolio risk

Insurance risk

Portfolio Risk

Systemic Insurance Risk

Systemic Market Risk

Naïve Model

Number of stocks

Volume

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

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Contacts For more information on the Insurance Risk Study or our analytic capabilities, please contact your local Aon Benfield broker or: Stephen Mildenhall Global Chief Executive Officer of Analytics Aon Center for Innovation and Analytics, Singapore +65 6231 6481 [email protected]

Kelly Superczynski Partner, Inpoint Aon Benfield +1 312 381 5351 [email protected]

Greg Heerde Head of Analytics & Inpoint, Americas Aon Benfield +1 312 381 5364 [email protected]

Will Street Principal, Inpoint Aon Benfield +44 (0) 20 7086 4497 [email protected]

John Moore Head of Analytics, International Aon Benfield +44 (0)20 7522 3973 [email protected]

Andrew Hare Principal, Inpoint Aon Benfield +65 6512 0263 [email protected]

George Attard Head of Analytics, Asia Pacific Aon Benfield +65 6239 8739 [email protected]

About Aon Benfield Aon Benfield, a division of Aon plc (NYSE: AON), is the world‘s

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unparalleled investment in innovative analytics, including catastrophe management, actuarial and rating agency © 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.

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. © Aon plc 2014. All rights reserved. The information contained herein and the statements expressed are of a general nature and are not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information and use sources we consider reliable, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act on such information without appropriate professional advice after a thorough examination of the particular situation.

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