Twelfth Edition Global Risk, Profitability, and Growth...

Global Risk, Profitability, and Growth MetricsCompanion volume to Global Insurance Market Opportunities

Insurance Risk Study | Twelfth Edition

Rating agencies, regulators, and investors today are demanding that insurers provide detailed assessments of their risk tolerance and quantify the adequacy of their economic capital. To complete such assessments requires a credible baseline for underwriting volatility. The Global Insurance Market Opportunities report provides our clients with an objective and data-driven set of underwriting volatility benchmarks by line of business and country as well as correlations by line and country. These benchmarks are a valuable resource to CROs, actuaries, and other economic capital modeling professionals who seek reliable parameters for their models.

Modern portfolio theory for assets teaches that increasing the number of stocks in a portfolio will diversify and reduce the portfolio’s risk, but will not eliminate risk completely; the systemic

market risk remains. This is illustrated in the left chart below. In the same way, insurers can reduce underwriting volatility by increasing account volume, but they cannot reduce their volatility to zero. A certain level of systemic insurance risk will always remain, due to factors such as the underwriting cycle, macroeconomic trends, legal changes and weather (right chart below). This study calculates this systemic risk by line of business and country. The Naïve Model on the right chart shows the relationship between risk and volume using a Poisson assumption for claim count—a textbook actuarial approach. The study clearly shows that this assumption does not fit with empirical data for any line of business in any country. It will underestimate underwriting risk if used in an ERM model.

Po

rtfo

lio r

isk

Insurance portfolio riskAsset portfolio risk

Number of stocks Premium Volume

Insu

ran

ce r

iskPortfolio Risk

SystemicMarket Risk

SystemicInsurance

Risk

Naïve Model

Introduction ...............................................................................................................................................1

Global Premium, Capital, Profitability, and Opportunity ............................................................................2

Geographic Opportunities..........................................................................................................................5

Global Risk Parameters ...............................................................................................................................8

US Risk Parameters .....................................................................................................................................10

Macroeconomic, Demographic, and Social Indicators .................................................................................11

Global Correlation Between Lines ...............................................................................................................12

Sources and Notes ......................................................................................................................................14

Contacts .....................................................................................................................................................15

Contents

About the Study

1

The 2017 edition of Global Insurance Market Opportunities reports on our current risk environment, in addition to

the ability and willingness of insurers and capital providers to address the risks we face today. As in previous editions,

we pay close attention to the critical role of data and analytics in bridging the gap between risk and capital to create

viable insurance market solutions. This year, however, we build on that story and pay greater attention to the role of

technology in providing growth opportunities through innovation.

The study, now in its 12th year, has evolved from its

beginnings as a quantification study for enterprise risk

management. We now take an expansive view of issues

related to global risk to encompass global growth,

emerging risks, operational challenges, and this year, how

our industry can best capture the opportunities from

innovation and defend against the threats of disruption.

Analytics remains at the core of everything we do. Aon’s

Global Insurance Market Opportunities study continues

to provide the insurance industry’s leading set of risk

parameters for modeling and benchmarking underwriting

risk and global profitability. While the main study focuses

on the opportunities, this companion volume explains the

numbers—including the critical metrics and parameters

that insurers can use to advance their decision making

in areas ranging from growth strategy to performance

benchmarking, risk tolerance, and capital management. All

parameters are produced using a consistent methodology

that we have employed since the first edition of the study.

This volume begins with a review of insurance industry

performance globally: premium and capital levels, areas of

growth, and profitability. Since the 8th edition, industry

combined ratios are calculated across the top 50 countries.

Beginning with the Country Opportunity Index on page 6,

we turn from numerical highlights to strategic considerations

that identify the countries showing an attractive mix

of growth and profitability with limited political risk.

Page 10 focuses on risk parameters that can be used to

model underwriting volatility and insurance capital.

Using the study

Beyond risk modeling, we can provide our clients with very

granular, customized market intelligence to create business

plans that are realistic, fact-based, and achievable. With a

global fact base and broad access to local market practitioners,

we are equipped to provide insight across a spectrum of lines,

products, and geographies. Inpoint, the consulting division of

Aon, helps insurers and reinsurers address these challenges,

from sizing market opportunities to identifying distribution

channel dynamics, assessing competitor behavior, and

understanding what it takes to compete and win. Our approach

leverages Aon’s USD 400 million annual investment in analytics,

data, and modeling to help our clients grow profitably. Please

enjoy the articles in the accompanying Global Insurance

Market Opportunities study, at http://aon.io/gimo-2017.

All of our work at Aon is motivated by client questions. We

continue to be grateful to clients who have invited us to

share in the task of helping them analyze their most complex

business problems. Dynamic and interactive working groups

always lead to innovative, and often unexpected, solutions.

If you have questions or suggestions for items we could

explore in future editions, please contact us through your local

Aon broker or one of the contacts listed on the last page.

Introduction

2 Global Risk, Profitability, and Growth Metrics

Globally, property casualty business again produced an underwriting profit in 2016 with a combined ratio of 97.6 percent, an improvement over last year’s 98.6 percent combined ratio. The Americas averaged a 97.3 percent combined ratio, while Europe averaged 97.0 percent and Asia Pacific was highest at 99.1 percent.

In 22 of the top 50 markets, combined ratios were below 95

percent, and 6 countries were below 90 percent, compared to

21 and 12 countries last year. Furthermore, 7 countries showed

five-year premium growth in excess of 10 percent, led by very

strong growth in China. The overall global combined ratio

result, and the variation in results by country, demonstrates

there are many desirable areas for profitable growth in the

market today.

At year-end 2016, global insurance premium stands at USD

5.1 trillion, down 1.0 percent compared to the prior year. The

overall decrease is driven by the Americas and EMEA, but

slightly offset by strong growth in APAC.

Global insurance premium and capital, USD trillions

Premium Capital

Property & Casualty 1.40 1.26

Life & Health 3.51 2.60

Reinsurance 0.17 0.60

Total 5.08 4.46

Global capital remained flat year on year to USD 4.5 trillion.

Property casualty, life, and health insurance capital were flat.

Reinsurance capital increased 5.3 percent, as we discuss at

greater length in Aon Benfield’s Reinsurance Market Outlook.

Property casualty penetration is 2.0 percent of GDP based

on 50 of the largest countries, flat compared to last year.

Motor insurance accounts for 47 percent of property-casualty

premium, while property accounts for 32 percent and liability

21 percent. This mix of business is nearly unchanged from

last year.

Motor insurance is also the fastest growing line of business,

with 6.7 percent annual growth over the last five years, driven

by strong growth in China, Brazil, Argentina, Venezuela, and

Saudi Arabia. Property is growing at an annual rate of 3.9

percent, and liability at 5.4 percent.

Global Premium, Capital, Profitability, and Opportunity

Motor: 6.7% annual growth

520

540

560

580

600

620

640

670

2013 2014 2015 20162012

USD

Bill

ion

s

Property: 3.9% annual growth

390

400

410

420

430

440

450

460

470

2013 2014 2015 20162012

USD

Bill

ions

Liability: 5.4% annual growth

240

250

260

270

280

290

300

310

2013 2014 2015 20162012

USD

Bill

ion

s

Global premium for motor

Global premium for property

Global premium for liability

http://aon.io/rmo-2017

3

Premium by product line

Notes: All statistics are the latest available. “Motor” includes all motor insurance coverages. “Property” includes construction, engineering, marine, aviation, and transit insurance as well as property. “Liability” includes general liability, workers’ compensation, surety, bonds, credit, and miscellaneous coverages.

U.S.46%

U.S.37%

Middle East & Africa

Rest of Europe

Rest of Euro Area U.K.Germany

France3%

Rest of APAC

South Korea

Japan

China15%

Rest of Americas

Canada

Motor: USD 661 billion

Property: USD 437 billion

Brazil

Middle East & Africa

Rest of Europe

Rest of Euro Area

U.K.

Germany

France5%

Rest of APAC

South KoreaJapan

China4%

Rest of AmericasCanada

Brazil

Liability: USD 293 billion

U.S.51%

Middle East& Africa

Rest of Europe Rest of Euro Area

U.K.

Germany

France3%

Rest of APAC

South KoreaJapan

China4%

Rest of AmericasCanada

Brazil

Five-year average annual growth rate

Property: 3.9% annual growth globally

Liability: 5.4% annual growth globally

Motor: 6.7% annual growth globally

-5%

0%

5%

10%

15%

20%

25%

30%

35%

Middle East & Afri

ca

Rest of E

urope

Rest of E

uro Area

U.K.

Germany

France

Rest of A

PAC

South Korea

Japan

China

Rest of A

mericas

CanadaBrazilU.S.

-5%

0%

5%

10%

15%

20%

25%

30%

35%

Middle East & Afri

ca

Rest of E

urope

Rest of E

uro Area

U.K.

Germany

France

Rest of A

PAC

South Korea

Japan

China

Rest of A

mericas

CanadaBrazilU.S.

-5%

0%

5%

10%

15%

20%

25%

30%

35%

Middle East & Afri

ca

Rest of E

urope

Rest of E

uro Area

U.K.

Germany

France

Rest of A

PAC

South Korea

Japan

China

Rest of A

mericas

CanadaBrazilU.S.

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



Top 50 P&C markets ranked by gross written premium by region

P&C

GW

P (U

SD M

)

Prem

ium

/ G

DP

Rati

o

Annualized Premium Growth Cumulative Net Loss Ratio Cumulative Net Expense Ratio Cumulative Net Combined Ratio

1yr 3yr 5yr 1yr 3yr 5yr 1yr 3yr 5yr 1yr 3yr 5yr

Americas

U.S. 597,924 3.2% 3.4% 4.0% 4.5% 71.1% 69.9% 72.3% 26.8% 27.0% 27.0% 97.9% 96.9% 99.4%

Canada 40,840 2.7% 3.8% 4.0% 5.0% 62.9% 65.6% 67.4% 26.3% 25.9% 26.8% 89.2% 91.4% 94.2%

Brazil 19,145 1.1% 1.3% 6.3% 9.5% 56.1% 56.4% 57.6% 33.9% 33.9% 34.0% 90.1% 90.3% 91.6%

Argentina 11,601 2.1% 37.2% 38.4% 36.6% 80.5% 76.1% 73.3% 33.0% 33.8% 35.4% 113.5% 109.9% 108.7%

Venezuela 1,075 3.6% 170.4% 95.0% 62.4% 57.5% 58.0% 58.7% 38.1% 37.7% 37.1% 95.6% 95.7% 95.8%

Mexico 10,321 0.9% 13.5% 7.7% 9.8% 61.1% 60.9% 62.2% 33.3% 32.1% 31.2% 94.5% 93.0% 93.4%

Colombia 3,611 1.3% 6.2% 9.6% 9.7% 56.8% 56.6% 56.7% 49.9% 48.4% 47.6% 106.7% 105.0% 104.3%

Chile 3,458 1.4% 4.8% 8.4% 6.9% 58.9% 56.7% 54.4% 41.0% 43.4% 43.3% 100.0% 100.1% 97.7%

Ecuador 1,541 1.6% -11.5% -0.1% 5.2% 54.2% 52.0% 52.1% 36.4% 32.7% 33.0% 90.5% 84.7% 85.1%

Subtotal 689,514 2.8% 4.3% 4.9% 5.4% 69.9% 69.0% 71.1% 27.4% 27.5% 27.7% 97.3% 96.5% 98.8%

Europe, Middle East & Africa

Germany 64,389 1.9% 3.8% 3.5% 3.4% 68.0% 68.8% 70.0% 27.8% 27.6% 27.2% 95.8% 96.4% 97.3%

U.K. 66,164 2.3% 6.7% 1.4% 1.3% 60.6% 61.2% 63.3% 38.6% 37.7% 36.6% 99.1% 99.0% 99.9%

France 54,020 2.2% 1.5% 1.1% 1.8% 73.4% 74.5% 74.7% 24.8% 24.5% 24.5% 98.1% 99.0% 99.2%

Italy 29,161 1.7% -3.1% -4.2% -2.9% 64.3% 65.5% 66.8% 27.8% 27.1% 26.8% 92.0% 92.6% 93.7%

Spain 24,769 2.1% 2.3% -0.7% -1.0% 68.3% 68.6% 68.3% 23.2% 23.1% 22.6% 91.5% 91.6% 91.0%

Netherlands 12,379 1.7% -0.5% -3.5% -3.1% 87.2% 86.4% 86.2% 12.0% 12.4% 12.6% 99.1% 98.7% 98.7%

Switzerland 14,551 2.2% -0.1% 0.6% 1.0% 71.6% 70.4% 72.0% 27.4% 26.3% 25.9% 99.0% 96.7% 97.9%

Russia 11,274 0.9% 0.6% 4.0% 8.6% 66.7% 64.7% 62.3% 29.9% 31.8% 32.1% 96.7% 96.6% 94.3%

Belgium 9,720 2.2% -0.3% 1.0% 2.3% 64.6% 64.9% 66.2% 32.7% 32.9% 31.1% 97.3% 97.9% 97.3%

Norway 7,321 1.8% 2.8% 4.3% 4.4% 66.6% 67.7% 70.2% 15.3% 15.0% 15.3% 81.9% 82.7% 85.4%

South Africa 8,120 2.5% 10.2% 8.2% 8.8% 56.9% 62.6% 62.7% 44.3% 42.3% 36.3% 101.1% 104.8% 98.9%

Austria 8,428 2.3% 2.1% 1.8% 2.4% 69.0% 69.5% 69.5% 27.8% 28.0% 28.0% 96.8% 97.5% 97.6%

Sweden 5,994 1.4% -9.5% -1.0% 0.0% 71.0% 71.5% 72.2% 19.5% 20.2% 20.4% 90.5% 91.7% 92.6%

Turkey 7,869 1.1% 20.2% 16.3% 17.7% 81.1% 74.6% 75.8% 24.5% 25.4% 26.1% 105.6% 99.9% 101.8%

Denmark 7,807 2.6% -1.4% 3.2% 3.1% 76.0% 74.2% 74.8% 16.9% 17.2% 17.2% 92.9% 91.4% 92.0%

Poland 6,132 1.3% -0.1% -0.4% 2.6% 66.8% 66.5% 66.2% 27.0% 26.0% 25.4% 93.7% 92.5% 91.6%

Finland 4,190 2.0% -1.4% 0.6% 3.8% 71.3% 70.6% 71.8% 18.9% 20.6% 21.2% 90.2% 91.2% 92.9%

Israel 4,360 1.5% 10.3% 6.0% 4.3% 79.9% 75.2% 75.2% 31.5% 30.0% 30.9% 111.4% 105.2% 106.1%

Portugal 3,329 1.6% 7.5% 0.7% -0.4% 71.8% 72.8% 79.4% 23.6% 24.3% 24.4% 95.4% 97.1% 103.8%

U.A.E. 3,820 0.9% 4.4% 1.7% 0.3% 78.9% 75.0% 74.0% 23.6% 23.4% 23.6% 102.5% 98.4% 97.6%

Saudi Arabia 4,579 0.5% 10.7% 18.7% 19.7% 80.6% 84.4% 82.1% 13.5% 13.8% 14.9% 94.0% 98.2% 97.0%

Czech Republic 3,384 2.0% -2.2% 1.7% 0.9% 67.1% 65.6% 64.1% 29.6% 30.3% 30.1% 96.7% 95.9% 94.2%

Ireland 3,223 1.4% 8.5% 1.6% -1.1% 71.4% 69.9% 70.4% 33.6% 32.7% 31.4% 105.0% 102.6% 101.8%

Greece 2,076 1.1% -7.5% -8.2% -5.8% 39.0% 43.6% 46.7% 43.0% 41.3% 40.8% 82.0% 84.9% 87.5%

Morocco 1,861 1.7% 4.0% 5.5% 6.2% 65.3% 64.4% 63.8% 33.4% 33.5% 33.6% 98.7% 97.9% 97.4%

Nigeria 971 0.2% 6.0% 1.8% 2.9% 49.2% 49.6% 48.9% 46.9% 45.0% 42.7% 96.1% 94.6% 91.6%

Luxembourg 872 1.7% -2.0% 2.1% 2.6% 68.9% 70.3% 71.4% 25.9% 23.3% 22.6% 94.9% 93.6% 94.1%

Romania 1,684 0.5% 8.4% 3.3% -14.0% 59.1% 68.2% 69.2% 43.7% 49.3% 49.1% 102.8% 117.5% 118.2%

Bulgaria 838 1.6% 9.8% 4.4% 1.9% 53.4% 55.4% 54.7% 31.1% 30.1% 29.9% 84.5% 85.4% 84.6%

Subtotal 373,288 1.7% 3.0% 1.8% 2.1% 69.9% 70.1% 71.0% 27.1% 26.7% 26.4% 97.0% 96.9% 97.4%

Asia Pacific

China 128,351 1.1% 11.0% 14.5% 15.5% 60.5% 61.9% 61.6% 37.8% 35.6% 34.6% 98.3% 97.5% 96.2%

Japan 71,219 1.7% 3.0% 4.3% 3.8% 64.2% 67.7% 69.9% 32.6% 32.7% 33.1% 96.8% 100.4% 103.0%

Australia 28,449 2.4% 5.8% 2.6% 3.6% 68.8% 68.8% 69.8% 25.1% 25.3% 25.1% 93.9% 94.1% 95.0%

S. Korea 16,896 1.2% 7.5% 3.6% 3.3% 84.4% 84.2% 83.3% 19.7% 19.6% 19.7% 104.1% 103.8% 103.0%

India 10,677 0.5% 11.4% 10.0% 15.1% 85.1% 84.2% 85.9% 30.3% 29.3% 28.4% 115.4% 113.5% 114.3%

Thailand 5,280 1.3% 0.9% 4.5% 9.7% 56.6% 61.3% 68.2% 36.3% 35.6% 35.2% 92.9% 96.9% 103.4%

Malaysia 3,897 1.3% 2.9% 5.1% 6.2% 60.0% 58.4% 59.8% 30.2% 29.1% 28.7% 90.2% 87.5% 88.5%

Taiwan 4,275 0.8% 7.2% 5.8% 5.7% 55.0% 55.0% 55.7% 38.8% 38.1% 37.9% 93.8% 93.2% 93.5%

New Zealand 3,539 2.1% 0.1% 6.0% 7.9% 56.9% 53.8% 59.5% 38.6% 37.8% 37.7% 95.5% 91.6% 97.2%

Indonesia 3,741 0.4% 10.1% 16.2% 15.0% 53.1% 53.8% 54.6% 36.6% 34.6% 34.1% 89.8% 88.4% 88.7%

Hong Kong 2,826 0.9% 3.4% 4.7% 6.5% 56.9% 55.7% 56.4% 34.4% 34.6% 34.8% 91.3% 90.3% 91.2%

Singapore 2,092 0.8% -6.2% -1.0% 0.6% 50.2% 49.9% 51.9% 38.5% 36.6% 35.3% 88.7% 86.5% 87.2%

Subtotal 281,243 1.2% 7.5% 9.1% 9.9% 68.7% 69.9% 70.8% 30.4% 29.5% 29.0% 99.1% 99.4% 99.8%

Grand Total 1,344,046 2.0% 4.6% 4.9% 5.4% 69.6% 69.8% 71.0% 27.9% 27.6% 27.3% 97.6% 97.3% 98.3%


5

Aon Benfield created the Country Opportunity Index to identify countries with a desirable mix of profitability, growth potential, and a relatively stable political environment. The table below displays the 50 property casualty markets ranked by this Index and divided into quartiles.

Geographic Opportunities

Aon Benfield country opportunity index

Rank Country5yr Cumulative Net

Combined Ratio5yr Annualized

Premium GrowthReal GDP

5yr GrowthPopulation 5yr

Annualized GrowthPolitical Risk Assessment

Quartile 1

1 Malaysia** 88.5% 6.2% 6.7% 1.7% Medium Low2 Indonesia** 88.7% 15.0% 6.9% 1.3% Medium3 Hong Kong 91.2% 6.5% 3.9% 0.7% Low3 Norway** 85.4% 4.4% 3.2% 1.1% Low3 Luxembourg 94.1% 2.6% 4.8% 2.4% Low3 Singapore** 87.2% 0.6% 4.8% 1.6% Low7 Ecuador** 85.1% 5.2% 4.0% 1.6% Medium High7 Australia 95.0% 3.6% 4.2% 1.5% Low7 Nigeria** 91.6% 2.9% 5.0% 2.8% High10 Saudi Arabia** 97.0% 19.7% 5.0% 2.3% Medium10 China* 96.2% 15.5% 9.0% 0.5% Medium10 Mexico* 93.4% 9.8% 4.1% 1.1% Medium

Quartile 2

10 Brazil 91.6% 9.5% 1.1% 0.9% Medium10 New Zealand 97.2% 7.9% 4.5% 1.5% Low10 Canada 94.2% 5.0% 3.4% 1.1% Low10 Denmark 92.0% 3.1% 2.7% 0.5% Low17 Poland 91.6% 2.6% 4.2% -0.1% Medium Low17 Sweden 92.6% 0.0% 3.7% 1.1% Low19 Venezuela 95.8% 62.4% -3.1% 1.4% Very High19 India 114.3% 15.1% 8.4% 1.5% Medium19 South Africa 98.9% 8.8% 3.1% 1.6% Medium19 Chile 97.7% 6.9% 4.6% 1.1% Medium Low19 Israel 106.1% 4.3% 4.9% 1.9% Medium Low19 U.A.E. 97.6% 0.3% 5.9% 3.0% Medium Low

Quartile 3

25 Turkey 101.8% 17.7% 7.1% 1.3% Medium25 Colombia 104.3% 9.7% 5.2% 1.1% Medium25 Morocco 97.4% 6.2% 4.8% 1.0% Medium25 Taiwan 93.5% 5.7% 3.6% 0.3% Medium Low25 Finland 92.9% 3.8% 1.3% 0.4% Low25 Bulgaria 84.6% 1.9% 3.4% -0.6% Medium31 Germany 97.3% 3.4% 2.8% 0.6% Low31 Switzerland 97.9% 1.0% 2.9% 1.1% Low33 Russia 94.3% 8.6% 2.0% 0.0% Medium High33 U.S. 99.4% 4.5% 3.7% 0.7% Low33 Austria 97.6% 2.4% 2.3% 0.7% Low33 Czech Republic 94.2% 0.9% 3.2% 0.1% Medium Low33 Spain 91.0% -1.0% 2.1% -0.2% Medium

Quartile 4

38 Thailand 103.4% 9.7% 5.0% 0.4% Medium High38 South Korea 103.0% 3.3% 4.4% 0.5% Medium Low38 Belgium 97.3% 2.3% 2.4% 0.6% Medium Low38 France 99.2% 1.8% 2.3% 0.5% Medium Low38 Italy 93.7% -2.9% 1.0% 0.4% Medium38 Greece 87.5% -5.8% -0.6% -0.5% High44 Argentina 108.7% 36.6% 1.3% 1.1% Medium High44 Ireland 101.8% -1.1% 9.2% 0.5% Medium44 Netherlands 98.7% -3.1% 2.4% 0.4% Low47 Japan 103.0% 3.8% 2.8% -0.1% Medium Low47 U.K. 99.9% 1.3% 3.6% 0.7% Medium Low49 Romania 118.2% -14.0% 4.8% -0.4% Medium High50 Portugal 103.8% -0.4% 1.3% -0.4% Medium

*Indicates top quartile performer in 2016. **Indicates top quartile performer in each year since 2013.Index methodology explained in Sources and Notes.

Nine of the 12 countries

in Quartile 1 were also in

the top quartile last year,

and seven have been

in Quartile 1 for all five

years of this Index. Asian

countries dominate the top

positions, but Quartile 1

also includes countries in

Latin America, Scandinavia,

Africa, and the Middle East.

For the second year in a

row, Malaysia, Indonesia,

and Singapore are ranked

1, 2, and 3 respectively. All

three of these countries

have shown low combined

ratios, healthy premium and

GDP growth, and a stable

political environment.

Hong Kong, Luxembourg,

and Australia entered the

top quartile this year due

to improvements in their

combined ratio. Chile, South

Africa, and Sweden fell out

of the top quartile this year.

Note that the US, Japan, and

most of Western Europe

are in Quartiles 3 and 4.

This Index suggests that to

achieve strong insurance

growth, it is best for

insurers to look beyond the

developed economies.


Growth markets and out/underperformers

To determine expansion opportunities we examined

premium growth and loss ratio performance by country

across motor, property, and liability lines of business as

well as premium growth and combined ratio performance

by country for all lines. The quadrant plots below

identify countries as either low growth or high growth,

and as either outperformers or underperformers.

To measure performance, the first three quadrant plots use loss

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

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

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

For all quadrant plots, growth is determined based on five-

year annualized premium growth. Countries with values

greater than 7.5 percent are classified as high growth.

Loss ratio and combined ratio performance is determined

based on five-year cumulative loss ratio and five-year net

cumulative combined ratio, respectively. Each country’s

loss ratio performance is compared against its income level

peers, using a USD 30,000 GDP per capita split between

high income and low income countries; whereas, combined

ratio performance is compared against the global combined

ratio. Countries with five-year loss ratios lower than the

average of their income peers, or combined ratios below

the global combined ratio, are classified as outperformers.

Property

Loss ratio performance

Motor


Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Brazil 1,424 China 11,320 Colombia 835 Hong Kong 1,445 India 1,903 Indonesia 647 Mexico 2,575 New Zealand 412 Russia 1,442 Saudi Arabia 280 South Africa 1,126 Turkey 457 Venezuela 96

Bulgaria 35 Canada 6,342 Ecuador 249 Greece 205 Japan 12,680 Malaysia 419 Nigeria 215 Poland 1,009 Romania 131 S. Korea 1,856 Switzerland 3,331 Taiwan 478 U.A.E. 1,164 U.S. 149,286

Australia 7,891 Austria 1,657 Belgium 2,774 Chile 718 Czech Republic 830 Denmark 873 Finland 1,090 France 8,635 Germany 14,185 Ireland 762 Israel 779 Italy 4,627 Luxembourg 135 Morocco 463 Netherlands 3,578 Norway 1,335 Portugal 987 Singapore 686 Spain 5,436 Sweden 192 Thailand 285 U.K. 19,600

Argentina 4,751

Brazil 6,024 China 17,536 Colombia 1,099 Ecuador 791 India 2,263 Indonesia 1,858 Mexico 2,968 Saudi Arabia 1,058 South Africa 3,447 Turkey 2,892 Venezuela 148

Belgium 3,003 Bulgaria 195 Chile 1,647 Czech Republic 990 Greece 566 Hong Kong 856 Luxembourg 270 Malaysia 1,404 Morocco 314 Nigeria 547 Poland 1,511 Portugal 884 Romania 311 Russia 3,691 S. Korea 2,362 Singapore 610 Spain 8,750 Switzerland 4,847 Taiwan 1,154 U.A.E. 1,123 U.K. 22,946 U.S. 201,966

Australia 9,346 Austria 3,298 Canada 16,150 Denmark 4,088 Finland 1,284 France 23,058 Germany 22,019 Ireland 954 Italy 6,042 Japan 16,517 Netherlands 4,493 Norway 3,503 Sweden 3,361

Argentina 1,633 Israel 1,181 New Zealand 2,025 Thailand 1,472

Argentina 5,218 Brazil 11,697 Chile 1,092 China 99,495 Colombia 1,677 Indonesia 1,236 Mexico 4,778 Russia 6,142 South Africa 3,547 Taiwan 2,644 Thailand 3,522 Venezuela 831

Austria 3,473 Bulgaria 609 Czech Republic 1,564 Denmark 2,846 Ecuador 501 Greece 1,306 Hong Kong 526 Japan 42,022 Malaysia 2,073 Morocco 1,084 Nigeria 209 Norway 2,484 Singapore 796 Switzerland 6,374 U.S. 246,672

Australia 11,212 Belgium 3,943 Canada 18,348 Finland 1,817 France 22,327 Germany 28,185 Ireland 1,507 Israel 2,401 Italy 18,492 Luxembourg 466 Netherlands 4,309 New Zealand 1,103 Poland 3,612 Portugal 1,459 Romania 1,242 S. Korea 12,678 Spain 10,582 Sweden 2,440 U.A.E. 1,532 U.K. 23,619

India 6,511 Saudi Arabia 3,242 Turkey 4,519

Brazil 19,145China 128,351Indonesia 3,741Mexico 10,321New Zealand 3,539Russia 11,274Saudi Arabia 4,579Venezuela 1,075

Australia 28,449 Austria 8,428 Belgium 9,720 Bulgaria 838 Canada 40,840 Chile 3,458 Czech Republic 3,384 Denmark 7,807 Ecuador 1,541 Finland 4,190 Germany 64,389 Greece 2,076 Hong Kong 2,827 Italy 29,161 Luxembourg 872 Malaysia 3,897 Morocco 1,861 Nigeria 971 Norway 7,322 Poland 6,132 Singapore 2,092 Spain 24,769 Sweden 5,994 Switzerland 14,551 Taiwan 4,275 U.A.E. 3,820

France 54,020 Ireland 3,223 Israel 4,360 Japan 71,219 Netherlands 12,379 Portugal 3,329 Romania 1,684 S. Korea 16,896 U.K. 66,164 U.S. 597,924

Argentina 11,601 Colombia 3,611 India 10,677 South Africa 8,120 Thailand 5,280 Turkey 7,869

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers




Argentina 4,751














7


Eighteen countries are high growth, loss ratio outperformers in

at least one line of business. Of these eighteen countries, seven

appear in each of the lines of business analyzed as high growth

outperformers: Brazil, China, Colombia, Indonesia, Mexico,

South Africa, and Venezuela. Venezuela’s growth is likely due

to inflation, but their loss ratios still stand as outperformers.

If we compare these countries on the basis of overall

combined ratio, five of the seven are outperformers globally.

The exceptions are Colombia and South Africa, which

underperform their peers with a five-year net combined

ratio of 104.3 and 98.9 percent respectively, driven by high

expense ratios of 40 to 50 percent. In addition to the seven

outperforming countries mentioned above, eight additional

countries outperform the global averages for both growth

and profitability. Saudi Arabia, for instance, outperforms

for both property and liability insurance, and its five-year

combined ratio of 97.0 percent is better than both the

global average and the average among its EMEA peers. See

the Top 50 P&C Markets table for more details on page 4.

Using combined ratio in addition to loss history allows us

to further analyze and target high growth opportunities.

Liability


Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers




Argentina 4,751













All Lines

Combined ratio performance

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers

Lowgrowth

Highgrowth

Outperformers

Underperformers




Argentina 4,751














Global Risk Parameters

Coefficient of variation of gross loss ratio by country

ThailandMexico

EcuadorUkraine

ArgentinaPhilippines

TaiwanChile

SingaporeBrazil

VenezuelaPeru

Hong KongIndonesiaNicaragua

BulgariaColombiaHondurasRomania

TurkeyDominican Republic

VietnamPakistanPanama

U.S.Bolivia

South KoreaRussia

El SalvadorPoland

HungaryCzech Republic

MalaysiaJapanChinaIndia

SwitzerlandUruguayCanada

U.K.France

ItalyGermany

AustriaSouth AfricaNetherlands

IsraelDenmark

PhilippinesHong Kong

RomaniaRussia

BulgariaUkraine

IndonesiaNicaraguaVenezuelaSingapore

TurkeyPanama

U.S.South Africa

PakistanVietnam

ColombiaIndia

HondurasDenmark

PolandU.K.

Dominican RepublicItaly

CanadaUruguayEcuadorMexico

ChinaIsrael

NetherlandsPeru

MalaysiaBolivia

ArgentinaCzech Republic

AustriaGermany

South KoreaHungary

ChileFrance

SwitzerlandBrazilJapan

El SalvadorTaiwan

Thailand

Americas

Asia Pacific

Europe, Middle East & Africa

42%37%

36%35%

30%27%

26%25%

24%22%

20%19%

18%16%16%

14%14%14%14% 14%14%13%13%13%13%13%12%

11%11%10%10%10%

9%9%9%9%9%8%8%8%

7%7%7%7%

6%4%

3%2%

126%112%

102%95%

93%91%

87%87%

84%78%

75%71%70%

66%55%55%54%

50%49%48%

46%45%

43%42%

41%40%40%40%40%

35%34%

32%31%30%

27%25%

24%22%22%21%

20%18%

18%17%

16%15%

12%10%

Motor Property

The insurance business is always a tradeoff of assuming risk in exchange for potential—presumed—return. We now turn to the “risk” side of the risk and return equation. Measuring the volatility and correlation of risk has long been the hallmark of the study.

The 2017 edition of the study quantifies the systemic risk

by line for 48 countries worldwide. By systemic risk, or

volatility, we mean the coefficient of variation of loss ratio

for a large book of business. Coefficient of variation (CV) is

a commonly used normalized measure of risk defined as the

standard deviation divided by the mean. Systemic risk typically

comes from non-diversifiable risk sources such as changing

market rate adequacy, unknown prospective frequency and

severity trends, weather-related losses, legal reforms and

court decisions, the level of economic activity and other

macroeconomic factors. It also includes the risk to smaller and

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

many lines of business systemic risk is the major component of

underwriting volatility.

The systemic risk factors for major lines by region appear

on the facing page. Detailed charts comparing motor and

property risk by country appear below. The factors measure

the volatility of gross loss ratios. If gross loss ratios are not

available the net loss ratio is used.

9

Global Risk Parameters

Coefficient of variation of loss ratio for major lines by country

Mot

or

Mot

or—

Pe

rson

al

Mot

or—

C

omm

erci

al

Prop

erty

Prop

erty

—

Pers

onal

Prop

erty

—

Com

mer

cial

Gen

eral

Li

abili

ty

Acc

iden

t &

Hea

lth

Mar

ine,

A

viat

ion

&

Tra

nsit

Wor

kers

C

omp

ensa

tion

Cre

dit

Fid

elit

y

& S

uret

y

Am

eric

as Argentina 9% 93% 131% 74% 86% 269%

Bolivia 9% 40% 10% 44% 80%

Brazil 7% 78% 86% 91% 33% 96%

Canada 13% 22% 18% 32% 36% 37% 63% 72% 101%

Chile 7% 87% 84% 41% 68% 27%

Colombia 14% 54% 70% 38% 42% 60% 51%

Dominican Republic 13% 46% 43% 208%

Ecuador 12% 102% 110% 57% 73% 87%

El Salvador 4% 40% 16% 161%

Honduras 14% 50% 12% 186%

Mexico 11% 112% 60% 49%

Nicaragua 25% 55% 64% 84%

Panama 19% 42% 27% 143%

Peru 10% 71% 59% 21% 28% 135% 75% 94%

Uruguay 13% 22% 11%

U.S. 18% 14% 23% 41% 44% 35% 37% 47% 37% 26% 63%

Venezuela 24% 75% 14% 156%

Asi

a Pa

cific China 11% 27% 45% 21% 14% 23% 22% 48%

Hong Kong 37% 70% 77% 25% 62% 66%

India 14% 25% 7% 27%

Indonesia 26% 66% 119% 42% 85% 68% 109%

Japan 6% 30% 12% 8% 16% 17%

Malaysia 9% 31% 102% 34% 34% 102%

Pakistan 16% 43% 49%

Philippines 42% 91% 88% 100% 106%

Singapore 22% 84% 36% 54% 27%

South Korea 8% 40% 19% 60% 181%

Taiwan 2% 2% 87% 27% 13% 64%

Thailand 2% 126% 121% 16% 27%

Vietnam 14% 45% 152% 52% 46% 45%

Euro

pe, M

iddl

e Ea

st &

Afr

ica Austria 9% 17% 13% 48% 19% 11% 28% 41%

Bulgaria 30% 55% 45% 35%

Czech Republic 9% 32% 22%

Denmark 14% 10% 14% 20% 18% 13% 23% 38%

France 7% 20% 22% 23% 29% 17% 38% 46%

Germany 8% 18% 19% 31% 25% 21% 17% 46%

Hungary 8% 34% 1% 10%

Israel 10% 12% 19%

Italy 13% 18% 27% 19% 41% 44% 70%

Netherlands 10% 15% 26% 41% 30% 31%

Poland 14% 35%

Romania 36% 49% 100%

Russia 35% 40% 21%

South Africa 16% 16% 55% 57% 41%

Switzerland 7% 24% 16% 9% 46% 85%

Turkey 20% 8% 48% 24% 81% 16% 69% 66% 106%

Ukraine 27% 95%

U.K. 13% 13% 16% 21% 22% 25% 30% 15% 31%


For the US risk parameters of the study, we use data from 20 years of NAIC annual statements for over 2,800 individual groups and companies. Our database covers all 22 Schedule P lines of business and contains over 5 million records of individual company observations from accident years 1987-2016.

The chart below shows the loss ratio volatility for each Schedule P line, with and without the effect of the underwriting cycle. The

underwriting cycle effect is removed by normalizing loss ratios by accident year prior to computing volatility. This adjustment

decomposes loss ratio volatility into its loss and premium components.

Coefficient of variation of gross loss ratio (1987-2015)

The underwriting cycle acts simultaneously across many

lines of business, driving correlation between the results of

different lines and amplifying the effect of underwriting risk

to primary insurers and reinsurers. Our analysis demonstrates

that the cycle increases volatility substantially for all major

commercial lines, as shown in the table. For example, the

underwriting volatility of reinsurance liability increases by

60 percent and commercial auto liability by 30 percent.

Personal lines are more formula-rated and thus show a

lower cycle effect, with private passenger auto volatility

only increasing by 11 percent because of the cycle.

See page 58 of the 2015 Insurance Risk Study for a detailed

description of how the underwriting cycle input is calculated.

US underwriting cycle impact on volatility

US Risk Parameters

Line of Business Impact

Reinsurance - Liability 60%

Other Liability - Claims-Made 45%

Other Liability - Occurrence 42%

Medical PL - Claims-Made 42%

Workers' Compensation 37%

Special Liability 33%

Commercial Auto 30%

Homeowners 23%

Commercial Multi Peril 22%

Private Passenger Auto 11%

Financial Guaranty

Products Liability - Claims-Made

Reinsurance - Property

Special Property

Reinsurance - Financial

Reinsurance - Liability

International

Fidelity and Surety

Products Liability - Occurrence

Other

Homeowners

Medical PL - Claims-Made

Other Liability - Claims-Made

Special Liability

Other Liability - Occurrence

Medical PL - Occurrence

Commercial Multi Peril

Warranty

Workers' Compensation

Commercial Auto

Auto Physical Damage

Private Passenger Auto 14%

17%

23%

26%

34%

35%

36%

37%

37%

39%

41%

44%

47%

48%

63%

68%

70%

80%

83%85%

89%162%

13%

16%

18%

19%

34%

28%

30%

26%

28%

27%

29%

36%43%

37%

45%

22%

44%

40%

52%54%

41%

92%

All RiskNo Underwriting Cycle Risk

http://thoughtleadership.aonbenfield.com/Documents/20150913-ab-analytics-insurance-risk-study.pdf

11

Macroeconomic, Demographic, and Social Indicators

Country GDP—

PPP,

US

D bi

llion

s

GDP

5yr

real

gro

wth

Popu

latio

n—

mill

ions

Popu

latio

n 5y

r an

nual

ized

gro

wth

GDP

Per C

apita

—

PPP,

USD

Gove

rnm

ent

cons

umpt

ion

as

% o

f GDP

Actu

al in

divi

dual

co

nsum

ptio

n as

%

of G

DP

Gene

ral

gove

rnm

ent

debt

as %

of G

DP

New

fore

ign

dire

ct

inve

stm

ent —

US

D bi

llion

s

Infla

tion

rate

Unem

ploy

men

t rat

e

Corp

orat

e ta

x ra

te

Polit

ical

risk

as

sess

men

t

Aon

terr

orism

risk

as

sess

men

t

Wor

ld B

ank

rela

tive

ease

of d

oing

bus

ines

s

Argentina 874.1 1.3% 43.6 1.1% 20,047 11.2% 74.4% n/a 11.7 n/a 8.5% 35.0% Medium High Medium More difficult

Australia 1,187.3 4.2% 24.3 1.5% 48,899 16.0% 57.5% 19.9% 36.9 1.3% 5.7% 30.0% Low Low Easiest

Austria 417.2 2.3% 8.7 0.7% 48,005 17.9% 56.5% 57.7% 5.7 1.0% 6.1% 25.0% Low Low Easiest

Belgium 509.5 2.4% 11.3 0.6% 45,047 22.7% 56.9% 62.1% -20.7 1.8% 8.0% 34.0% Medium Low Medium Easiest

Brazil 3,141.3 1.1% 206.1 0.9% 15,242 18.8% 59.8% 46.2% 75.1 8.7% 11.3% 34.0% Medium Medium More difficult

Bulgaria 144.6 3.4% 7.1 -0.6% 20,327 25.7% 60.9% -4.2% 1.8 -1.3% 7.7% 10.0% Medium Low Easiest

Canada 1,682.4 3.4% 36.2 1.1% 46,437 17.2% 55.7% 27.6% 63.2 1.4% 7.0% 26.5% Low Low Easiest

Chile 438.8 4.6% 18.2 1.1% 24,113 16.5% 62.0% -0.9% 20.5 3.8% 6.5% 25.5% Medium Low Medium Easier

China 21,291.8 9.0% 1382.7 0.5% 15,399 12.8% 37.4% n/a 249.9 2.0% 4.0% 25.0% Medium Medium Easier

Colombia 6,88.8 5.2% 48.7 1.1% 14,130 16.6% 64.9% 41.4% 12.1 7.5% 9.2% 34.0% Medium High Easier

Czech Republic 350.7 3.2% 10.6 0.1% 33,232 26.0% 50.5% n/a 2.5 0.7% 4.0% 19.0% Medium Low Negligible Easiest

Denmark 273.9 2.7% 5.7 0.5% 47,985 24.5% 47.2% 6.5% 1.7 0.2% 6.2% 22.0% Low Low Easiest

Ecuador 183.6 4.0% 16.5 1.6% 11,109 12.8% 61.4% n/a 1.1 1.7% 5.2% 22.0% Medium High Medium More difficult

Finland 231.4 1.3% 5.5 0.4% 42,165 24.4% 58.4% -51.4% 18.7 0.4% 8.8% 20.0% Low Negligible Easiest

France 2,733.7 2.3% 64.6 0.5% 42,314 22.8% 56.8% 88.3% 42.9 0.3% 10.0% 33.3% Medium Low Medium Easiest

Germany 3,980.3 2.8% 82.7 0.6% 48,111 17.8% 54.9% 45.0% 46.2 0.4% 4.2% 29.8% Low Medium Easiest

Greece 289.4 -0.6% 10.9 -0.5% 26,669 22.5% 71.3% 65.5% -0.3 n/a 23.8% 29.0% High Medium Easier

Hong Kong 429.7 3.9% 7.4 0.7% 58,322 25.2% 58.0% n/a 180.8 2.6% 3.3% 16.5% Low Low Most difficult

India 8,662.4 8.4% 1309.3 1.5% 6,616 10.7% 60.8% n/a 44.2 4.9% n/a 30.0% Medium High More difficult

Indonesia 3,032.1 6.9% 258.7 1.3% 11,720 10.8% 53.9% n/a 15.5 3.5% 5.6% 25.0% Medium High Easier

Ireland 324.9 9.2% 4.7 0.5% 69,231 13.5% 37.2% 69.9% 125.7 -0.2% 7.9% 12.5% Medium Low Easiest

Israel 300.6 4.9% 8.5 1.9% 35,179 23.3% 58.9% 59.2% 11.5 -0.5% 4.8% 24.0% Medium Low High Easier

Italy 2,234.5 1.0% 60.7 0.4% 36,833 17.8% 59.8% 113.3% 2.7 -0.1% 11.7% 24.0% Medium Low Easier

Japan 5,237.8 2.8% 126.9 -0.1% 41,275 20.9% 59.9% 119.8% -0.0 -0.1% 3.1% 30.9% Medium Low Low Easiest

Luxembourg 59.9 4.8% 0.6 2.4% 104,003 16.3% 44.0% n/a 24.6 0.1% 6.4% 27.1% Low Negligible Easier

Malaysia 863.3 6.7% 31.7 1.7% 27,267 15.8% 54.6% n/a 11.0 2.1% 3.5% 24.0% Medium Low Medium Easiest

Mexico 2,315.7 4.1% 122.3 1.1% 18,938 19.3% 63.6% 51.8% 30.3 2.8% 4.3% 30.0% Medium Medium Easiest

Morocco 281.8 4.8% 33.8 1.0% 8,330 19.3% 56.3% 64.2% 3.2 1.6% 9.4% 31.0% Medium Medium Easier

Netherlands 869.4 2.4% 17.0 0.4% 51,049 22.5% 44.1% 33.9% 68.7 0.1% 5.9% 25.0% Low Low Easiest

New Zealand 177.0 4.5% 4.7 1.5% 37,294 19.6% 59.9% 6.1% -0.7 0.6% 5.1% 28.0% Low Negligible Easiest

Nigeria 1,091.2 5.0% 183.6 2.8% 5,942 8.1% 71.3% 17.3% 3.1 15.7% 12.7% 30.0% High Severe Most difficult

Norway 364.4 3.2% 5.3 1.1% 69,249 14.1% 34.5% -284.5% -9.9 3.6% 4.8% 24.0% Low Negligible Easiest

Poland 1,054.1 4.2% 38.0 -0.1% 27,764 23.3% 61.8% 20.0% 6.3 -0.6% 6.1% 19.0% Medium Low Low Easiest

Portugal 298.7 1.3% 10.3 -0.4% 28,933 20.5% 65.6% 121.0% -1.3 0.6% 11.1% 21.0% Medium Low Easiest

Romania 441.6 4.8% 19.8 -0.4% 22,348 22.3% 59.4% n/a 3.9 -1.6% 6.0% 16.0% Medium High Low Easiest

Russia 3,799.7 2.0% 143.4 n/a 26,490 22.3% 59.4% n/a 4.8 7.0% 5.5% 20.0% Medium High Medium Most difficult

Saudi Arabia 1,750.9 5.0% 31.7 2.3% 55,158 20.1% 33.5% -18.9% 8.1 3.5% 5.7% 20.0% Medium High Easier

Singapore 492.6 4.8% 5.6 1.6% 87,855 10.0% 34.0% n/a 65.3 -0.5% 2.1% 17.0% Low Low Easiest

South Africa 739.4 3.1% 55.9 1.6% 13,225 19.1% 61.9% 45.2% 1.6 6.3% 26.7% 28.0% Medium Medium Easier

South Korea 1,934.0 4.4% 51.2 0.5% 37,740 9.6% 79.8% 36.5% 5.0 1.0% 3.7% 22.0% Medium Low Medium Most difficult

Spain 1,686.9 2.1% 46.3 -0.2% 36,416 18.9% 56.9% 80.4% 22.1 -0.2% 19.6% 25.0% Medium Low Easiest

Sweden 498.1 3.7% 10.0 1.1% 49,836 23.2% 50.3% -18.3% 15.9 1.1% 7.0% 22.0% Low Low Easiest

Switzerland 496.0 2.9% 8.3 1.1% 59,561 7.6% 49.1% 24.3% 119.7 -0.4% 3.3% 24.4% Low Low Easiest

Taiwan 1,132.1 3.6% 23.5 0.3% 48,095 14.4% 58.0% 33.5% n/a 1.4% 3.9% 17.0% Medium Low Low Most difficult

Thailand 1,164.9 5.0% 69.0 0.4% 16,888 18.5% 61.1% n/a 8.0 0.2% 0.8% 20.0% Medium High Medium Easiest

Turkey 1,988.3 7.1% 79.8 1.3% 24,912 21.0% 60.0% 22.2% 16.8 7.8% 10.8% 20.0% Medium Severe Easier

U.A.E. 668.9 5.9% 9.9 3.0% 67,871 27.0% 73.2% -247.7% 11.0 1.8% n/a 55.0% Medium Low Low Easiest

U.K. 2,785.6 3.6% 65.6 0.7% 42,481 19.4% 63.4% 80.7% 39.5 0.6% 4.9% 19.0% Medium Low Medium Easiest

U.S. 18,569.1 3.7% 323.3 0.7% 57,436 11.3% 71.7% 81.5% 409.9 1.3% 4.9% 40.0% Low Medium Easiest

Venezuela 427.0 -3.1% 31.0 1.4% 13,761 19.1% 94.8% n/a 3.8 254.9% 21.2% 34.0% Very High High Most difficult


Correlation between lines of business is central to a realistic assessment of aggregate portfolio risk, and in fact becomes increasingly significant for larger companies where there is little idiosyncratic risk to mask correlation. Most modeling exercises are carried out at the product or business unit level and then aggregated to the company level. In many applications, the results are more sensitive to the correlation and dependency assumptions made when aggregating results than to all the detailed assumptions made at the business unit level.

The study determines correlations between lines within each country. Correlation between lines is computed by examining the

results from larger companies that write pairs of lines in the same country.

Aon Benfield Analytics has correlation tables for most countries readily available and can produce custom analyses of correlation

for many insurance markets globally upon request. As examples, tables for the US, Canada, Colombia, and China appear below.

US

Hom

eow

ners

Priv

ate

Pass

eng

er A

uto

Com

mer

cial

M

ulti

Peri

l

Com

mer

cial

A

uto

Wor

kers

C

omp

ensa

tion

Oth

er L

iab

ility

- O

ccur

renc

e

Med

ical

PL

- C

laim

s M

ade

Oth

er L

iab

ility

- C

laim

s-M

ade

Prod

ucts

Li

abili

ty -

Occ

urre

nce

Homeowners 3% 30% 9% -6% 4% 7% 0% 18%

Private Passenger Auto 3% 12% 20% 41% 18% 26% 21% 20%

Commercial Multi Peril 30% 12% 50% 29% 51% 56% 45% 41%

Commercial Auto 9% 20% 50% 53% 59% 68% 42% 62%

Workers Compensation -6% 41% 29% 53% 48% 54% 53% 52%

Other Liability—Occurrence 4% 18% 51% 59% 48% 74% 55% 60%

Medical PL—Claims Made 7% 26% 56% 68% 54% 74% 68% 68%

Other Liability—Claims-Made 0% 21% 45% 42% 53% 55% 68% 30%

Products Liability—Occurrence 18% 20% 41% 62% 52% 60% 68% 30%

Canada

Acc

iden

t &

Hea

lth

Cre

dit

Fid

elit

y &

Su

rety

Gen

eral

Lia

bili

ty

Mar

ine,

A

viat

ion

&

Tran

sit

Mot

or

Prop

erty

Spec

ial L

iab

ility

Accident & Health 81% -23% 28% -6% -5% 27% -26%

Credit 81% -24% 50% 29% 1% 30% 16%

Fidelity & Surety -23% -24% 4% 44% -4% -3% 30%

General Liability 28% 50% 4% -1% 14% 1% 15%

Marine, Aviation & Transit -6% 29% 44% -1% 4% 8% 26%

Motor -5% 1% -4% 14% 4% 11% 24%

Property 27% 30% -3% 1% 8% 11% 2%

Special Liability -26% 16% 30% 15% 26% 24% 2%

Global Correlation Between Lines

13

Columbia

Acc

iden

t &

Hea

lth

Cro

p &

Ani

mal

Fid

elit

y &

Su

rety

Gen

eral

Lia

bili

ty

Mar

ine,

A

viat

ion

&

Tran

sit

Mot

or

Prop

erty

Spec

ial

Liab

ility

Spec

ial

Prop

erty

Accident & Health 22% 2% 21% 4% 27% 17% 13% 25%

Crop & Animal 22% 3% 14% 2% 42% 12% 11% 1%

Fidelity & Surety 2% 3% 40% -4% 28% 10% 21% 18%

General Liability 21% 14% 40% -8% 35% 17% 18% 16%

Marine, Aviation & Transit 4% 2% -4% -8% 6% 5% 16% 13%

Motor 27% 42% 28% 35% 6% 30% 23% 42%

Property 17% 12% 10% 17% 5% 30% 13% 38%

Special Liability 13% 11% 21% 18% 16% 23% 13% 14%

Special Property 25% 1% 18% 16% 13% 42% 38% 14%

China

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Mar

ine,

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sit

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erty

Spec

ial R

isks

Accident & Health 23% 10% 9% 20% 8% 17% 17% 17% 15% 3%

Agriculture 23% 42% 12% 11% 24% 13% 8% 15% 26% -25%

Credit 10% 42% 18% 2% 4% 3% 18% 8% 11% 3%

Engineering 9% 12% 18% 7% 21% 17% 37% 27% 11% -3%

Financial Guaranty 20% 11% 2% 7% 4% -3% 4% 18% -4% 9%

General Liability 8% 24% 4% 21% 4% 18% 27% 25% 7% -6%

Marine, Aviation & Transit 17% 13% 3% 17% -3% 18% 14% 19% -1% -5%

Motor 17% 8% 18% 37% 4% 27% 14% 37% 7% -14%

Other 17% 15% 8% 27% 18% 25% 19% 37% 35% -33%

Property 15% 26% 11% 11% -4% 7% -1% 7% 35% 15%

Special Risks 3% -25% 3% -3% 9% -6% -5% -14% -33% 15%

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 95 percent confidence interval.



Sources and Notes

Global Premium, Capital, Profitability & Opportunity Sources: A.M. Best, Axco Insurance Information Services, IMF World Economic Outlook Database April 2017 Edition, SNL Financial, Standard & Poor’s, World Bank

Notes: Premium amounts stated in USD are converted to USD by Axco. Growth rates are calculated in original currency and exclude currency exchange fluctuation.

Country Opportunity Index Calculation: For each combined ratio, growth and political risk statistic, countries were ranked and segmented into quartiles. A score of 1 to 4 was assigned to each metric based on quartile. Opportunity Index Score = one-third multiplied by combined ratio score plus two-thirds multiplied by average of premium, GDP and population growth and political scores. Ties were broken by premium growth.

Growth Markets and Out/Underperformers—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).

Global Risk Parameters and US Risk Parameters Sources: Superintendencia de Seguros de la Nación (Argentina), FMA (Austria), Superintendencia de Pensiones, Valores y Seguros (Bolivia), Superintendencia de Seguros Privados (Brazil), Financial Supervision Commission (Bulgaria), MSA Research Inc. (Canada), Superintendencia de Valores y Seguros de Chile, China Insurance Yearbooks, Superintendencia Financiera de Colombia, Czech National Bank, Danish FSA (Denmark), CADOAR (Dominican Republic), Superintendencia de Bancos y Seguros (Ecuador), Superintendencia de Pensiones de El Salvador, French Prudential Supervision and Resolution Authority, BaFin (Germany), Comisión Nacional de Bancos y Seguros de Honduras, HKOCI (Hong Kong), Magyar Nemzeti Bank (Hungary), IRDA (India), bapepam.go.id (Indonesia), Ernst & Young Annual Statements (Israel), ANIA (Italy), The Statistics of Japanese Non-Life Insurance Business, ISM Insurance Services Malaysia Berhad, Comisión Nacional de Seguros y Fianzas (Mexico), DNB (Netherlands), Superintendencia de Bancos y Otras Instituciones Financieras de Nicaragua, Superintendencia de Seguros y Reaseguros de Panama, Superintendencia de Banca y Seguros (Peru), Insurance Commission (Philippines), Polish Financial Supervision Authority, Autoritatea de Supraveghere Financiară (Romania), Bank of Russia, Monetary Authority of Singapore, Quest Data Report (South Africa), Korea Financial Supervisory Service, Finma (Switzerland), Taiwan Insurance Institution, Undersecretariat of Treasury (Turkey), National Financial Services Commission (Ukraine), SFCR (UK), SNL Financial (US), Banco Central del Uruguay, Cámara de Aseguradores de Venezuela, Association of Vietnam Insurers, and annual financial statements

Macroeconomic, Demographic, and Social Indicators

Sources:

Aon Political Risk Map 2017, Aon Terrorism & Political Violence Map 2017, Axco Insurance Information Services, Bloomberg, IMF World Economic Outlook Database April 2017 Edition, KPMG, Penn World Table Version 8.1, World Bank

Notes: Table—GDP (PPP) is GDP in local currency adjusted using purchasing power parity (PPP) exchange rate into US 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.


Sources:

MSA Research Inc. (Canada), SNL Financial (US), Superintendencia Financiera de Colombia, and China Insurance Yearbook

15

ContactsFor more information on the Global Insurance Market Opportunities study or our analytic capabilities, please contact your local Aon broker or:

Paul MangGlobal Chief Executive Officer of Analytics +65 6812 [email protected] Tracy HatlestadGlobal Chief Operating Officer of Analytics +65 6512 [email protected] Greg HeerdeHead of Analytics & Inpoint, Americas+1 312 381 [email protected]

George AttardHead of Analytics, International+65 6239 [email protected]

John MooreChairman of International Analytics+44 (0)20 7522 [email protected] Kelly SuperczynskiHead of Analytics, EMEA+1 312 381 [email protected]

Dan DickGlobal Head of Catastrophe Management+1 214 989 [email protected]

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About Aon Aon plc (NYSE:AON) is a leading global professional services firm providing a broad range of risk, retirement and health solutions. Our 50,000 colleagues in 120 countries empower results for clients by using proprietary data and analytics to deliver insights that reduce volatility and improve performance.

© Aon plc 2017. 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 profes-sional advice after a thorough examination of the particular situation.

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