Insurance Risk Study. Sixth Edition 2011

Transcription

1 Insurance Risk Study Sixth Edition 2011

2 Contents 3 Foreword 4 Global Risk Parameters 6 U.S. Risk Parameters 7 U.S. Reserves 10 Innovations in Crop Insurance Modeling 12 An Optimal Insurer in a Solvency II World 14 Innovations in Hedging 16 Correlation and the Pricing Cycle 18 Macroeconomic Correlation 19 Global Market Review 20 Global Statistics: Motor 21 Global Statistics: Property 22 Global Statistics: Liability 23 Afterword: The Good Risk About the Study Rating agencies, regulators and investors today are demanding that insurers provide detailed assessments of their risk tolerance and quantify the adequacy of their economic capital. To complete such assessments requires a credible baseline for underwriting volatility. The Insurance Risk Study provides our clients with an objective and data-driven set of underwriting volatility benchmarks by line of business and country as well as correlations by line and country. These benchmarks are a valuable resource to CROs, actuaries, and other economic capital modeling professionals who seek reliable parameters for their models. Modern portfolio theory for assets teaches that increasing the number of stocks in a portfolio will diversify and reduce the portfolio s risk, but will not eliminate risk completely; the systemic market risk remains. This is illustrated in the left chart below. In the same way, insurers can reduce underwriting volatility by increasing account volume, but they cannot reduce their volatility to zero. A certain level of systemic insurance risk will always remain, due to factors such as the underwriting cycle, macroeconomic trends, legal changes and weather (right chart below). The Study calculates this systemic risk by line of business and country. The Naïve Model on the right chart shows the relationship between risk and volume using a Poisson assumption for claim count a textbook actuarial approach. The Study clearly shows that this assumption does not fit with empirical data for any line of business in any country. It will underestimate underwriting risk if used in an ERM model. Asset Portfolio Risk Insurance Portfolio Risk Portfolio Risk Portfolio Risk Insurance Risk Systemic Market Risk Systemic Insurance Risk Naïve Model Number of Stocks Volume

3 Foreword Even before the start of the U.S. hurricane season in June, we had already seen a series of significant natural catastrophe events which made 2011 a challenging insurance year: severe flooding in Australia, a 9.0-magnitude earthquake and subsequent tsunami in Japan, two earthquakes in New Zealand, as well as severe weather losses in the U.S., which rivaled the insured losses of moderate-sized hurricanes. As a result of the frequency of severity of catastrophe losses, modeling technology for natural catastrophes continues to advance very quickly. In contrast, modeling non-cat losses has not received nearly as much attention, despite being a greater net exposure to the industry. Therefore, we present this sixth edition of Aon Benfield s Insurance Risk Study, which continues to highlight this important but somewhat neglected risk. This Study marks a cornerstone of Aon Benfield Analytics integrated and comprehensive risk modeling and risk assessment capabilities. > Our reinsurance optimization framework, linking reinsurance to capital, volatility and valuation, relies on the Study for a credible assessment of baseline frequency and severity volatility; > Our global risk and capital strategy practice, providing ERM and economic capital services, uses the Study to benchmark risk, quantify capital adequacy and allocate capital to risk drivers; > Our ReMetrica risk evaluation and capital modeling software provides easy access to the Study parameters and risk insights. The massive database underlying the Study is supported by more than 400 local professionals within Aon Benfield s global analytics team. Our team is available to work with you to customize the basic parameters reported in the Study to answer your specific, pressing business questions. As part of our continued efforts in innovation and improvement, we have included in this edition two recent examples of Aon Benfield Analytics new technologies and modeling techniques to advance our understanding of risk. The first example is our ACReS model an application of detailed weather forecasts to project crop yields for use in global agricultural insurance products. The second example is PathWise, a family of products for hedging variable annuity business that incorporates cutting-edge graphical processor technology. The Aon Benfield Insurance Risk Study continues to be the industry s leading set of risk parameters for modeling and benchmarking underwriting risk. It is part of a suite of capabilities that help position Aon Benfield as the leading advisor on growth and risk management in the global insurance business. For convenient reference, you can find earlier editions of the Study at aonbenfield.com. I welcome your thoughts and suggestions, which you can share with an to stephen.mildenhall@aonbenfield.com. Stephen Mildenhall CEO, Aon Benfield Analytics 3

4 Insurance Risk Study Global Risk Parameters The 2011 Insurance Risk Study quantifies the systemic risk by line for 47 countries worldwide, representing more than 90 percent of global property casualty premium. Systemic risk in the Study is the coefficient of variation of loss ratio for a large book of business. Coefficient of variation (CV) is a commonly used normalized measure of risk defined as the standard deviation divided by the mean. Systemic risk typically comes from nondiversifiable risk sources such as changing market rate adequacy, unknown prospective frequency and severity trends, weather-related losses, legal reforms and court decisions, the level of economic activity and other macroeconomic factors. It also includes the risk to smaller and specialty lines of business caused by a lack of credible data. For many lines of business systemic risk is the major component of underwriting volatility. The systemic risk factors for major lines by region appear on the next page. Detailed charts comparing motor and property risk by country appear below. The factors measure the volatility of gross loss ratios. If gross loss ratios are not available the net loss ratio is used. Coefficient of Variation of Gross Loss Ratio by Country Motor Property Hungary 4% Israel 8% 5% Denmark 6% South Africa 7% Australia 7% Italy 7% Netherlands 8% Austria 8% Spain 8% Switzerland 8% Germany 8% U.K. 9% Canada 9% Turkey 9% Chile 9% Malaysia 10% Japan 10% Panama 11% China 11% India 12% France 12% Venezuela 12% Bolivia 13% El Salvador 13% Slovakia 14% Uruguay 14% Hungary 15% South Korea 15% 15% Poland 15% Ecuador 15% Vietnam 16% U.S. 18% Argentina 18% Pakistan 18% Romania 19% Honduras 20% Colombia 21% Dominican Republic 23% Nicaragua 24% Indonesia 25% Hong Kong 28% Singapore 30% Greece 32% Brazil 43% Mexico 46% Peru 58% Taiwan Japan Taiwan South Korea Israel Austria Turkey Australia France Czech Republic Switzerland Uruguay Mexico Bolivia Spain India Germany Italy Chile Brazil Netherlands Venezuela South Africa Pakistan China Dominican Republic Malaysia Argentina Vietnam Poland Colombia U.S. U.K. El Salvador Canada Peru Honduras Ecuador Denmark Singapore Romania Indonesia Slovakia Panama Hong Kong Greece Nicaragua 13% 14% 16% 16% 17% 18% 18% 18% 19% 20% 23% 25% 26% 27% 28% 29% 30% 31% 32% 35% 38% 39% 40% 40% 40% 42% 42% 42% 42% 43% 45% 51% 55% 55% 56% 57% 58% 64% 65% 69% 69% 70% 92% 96% 96% Americas Asia Pacific Europe, Middle East & Africa 4

5 Aon Benfield Underwriting Volatility for Major Lines by Country, Coefficient of Variation of Loss Ratio for Each Line Motor Motor Personal Motor Commercial Property Property Personal Property Commercial General Liability Accident & Health Marine, Aviation & Transit Workers Compensation Credit Fidelity & Surety Americas Argentina 15% 45% 53% 40% 10% 176% Bolivia 9% 38% 15% Brazil 12% 70% 48% 70% 50% 42% 50% 62% Canada 18% 23% 20% 35% 39% 41% 47% 90% 117% Chile 11% 26% 41% 44% 23% 66% Colombia 15% 56% 31% 15% 76% Dominican Republic 14% 57% 92% 64% Ecuador 21% 42% 52% 184% El Salvador 18% 39% 17% 96% Honduras 20% 55% 5% 200% Mexico 9% 92% 65% 43% Nicaragua 58% 58% 80% 149% Panama 32% 29% 21% 118% Peru 19% 96% 64% 8% 21% 82% 75% Uruguay 9% 40% U.S. 16% 14% 24% 43% 48% 34% 37% 53% 40% 27% 69% Venezuela 12% 35% 21% 159% Asia Pacific Australia 8% 16% 23% 32% 54% 10% 30% China 14% 14% 30% 73% 23% 28% 20% 16% 116% Hong Kong 43% 43% 65% 85% 23% 60% 81% India 10% 12% 31% 14% 31% Indonesia 28% 28% 64% 126% 55% 68% 73% 94% Japan 5% 28% 11% 9% 17% 6% Malaysia 15% 27% 119% 30% 36% 89% Pakistan 13% 51% 36% Singapore 24% 69% 47% 57% 42% South Korea 7% 7% 42% 33% 55% Taiwan 6% 6% 96% 47% 26% 68% 64% Vietnam 15% 42% 38% 11% 30% Europe, Middle East & Africa Austria 7% 18% 12% 51% 20% 12% 20% 50% Czech Republic 8% Denmark 23% 13% 15% 17% 15% 16% 33% 24% France 8% 32% 35% 26% 22% 25% 57% Germany 10% 19% 20% 31% 29% 22% 22% 44% Greece 46% 69% 82% 84% Hungary 4% 40% Israel 7% 8% 53% Italy 11% 16% 24% 13% 46% 38% 69% Netherlands 12% 17% 25% 58% 44% 32% Poland 15% 42% Romania 25% 55% Slovakia 30% 40% South Africa 13% 14% 63% 33% 47% Spain 9% 9% 18% 10% 23% 31% 14% 32% 47% 134% Switzerland 8% 18% 21% 11% 50% 75% Turkey 8% 8% 25% 24% 32% 21% 81% 80% U.K. 18% 18% 19% 20% 20% 24% 28% 5% Reported CVs are of gross loss ratios except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Uruguay and Venezuela which are of net loss ratios. Accident & Health is defined differently in each country; it may include pure accident A&H coverage, credit A&H, and individual or group A&H. In the U.S., A&H comprises about 80 percent of the Other line of business with the balance of the line being primarily credit insurance. 5

6 Insurance Risk Study U.S. Risk Parameters The U.S. portion of the Insurance Risk Study uses data from ten years of NAIC annual statements for 2,308 individual groups and companies. The database covers all 22 Schedule P lines of business and contains 1.5 million records of individual company observations from accident years The charts below show the loss ratio volatility for each Schedule P line, with and without the effect of the underwriting cycle. The effect of the underwriting cycle is removed by normalizing loss ratios by accident year prior to computing volatility. This adjustment decomposes loss ratio volatility into its loss and premium components. Coefficient of Variation of Gross Loss Ratio All Risk No Underwriting Cycle Risk Private Passenger Auto Auto Physical Damage Commercial Auto Workers Compensation Warranty Medical PL - Occurrence Commercial Multi Peril Other Liability - Occurrence Special Liability Other Liability - Claims-Made Medical PL - Claims-Made Products Liability - Occurrence Homeowners Other Reinsurance - Liability Fidelity and Surety International Reinsurance - Property Reinsurance - Financial Products Liability - Claims-Made Special Property Financial Guaranty 14% 16% 24% 27% 29% 33% 34% 37% 40% 41% 42% 47% 48% 53% 67% 69% 70% 85% 94% 101% 102% 154% Private Passenger Auto Auto Physical Damage Commercial Auto Workers Compensation Warranty Medical PL - Occurrence Commercial Multi Peril Other Liability - Occurrence Special Liability Other Liability - Claims-Made Medical PL - Claims-Made Products Liability - Occurrence Homeowners Other Reinsurance - Liability Fidelity and Surety International Reinsurance - Property Reinsurance - Financial Products Liability - Claims-Made Special Property Financial Guaranty 13% 15% 18% 18% 31% 32% 27% 25% 30% 27% 30% 33% 41% 50% 45% 53% 55% 55% 59% 48% 60% 103% The U.S. Underwriting Cycle The underwriting cycle acts simultaneously across many lines of business, driving dependencies between the results of different lines and amplifying the effect of underwriting risk to primary insurers and reinsurers. Our analysis demonstrates that the cycle increases volatility substantially for all major commercial lines, as shown in the table. For example, the underwriting volatility of other liability claims-made increases by 54 percent and commercial auto liability by 39 percent. Personal lines are more formula rated and thus show a much lower cycle effect, with private passenger auto volatility only increasing by seven percent because of the cycle. Impact of Pricing Cycle Line Impact of Pricing Cycle Other Liability Claims-Made 54% Reinsurance Liability 50% Workers Compensation 47% Other Liability Occurrence 46% Medical PL Claims-Made 39% Commercial Auto 39% Special Liability 31% Commercial Multi Peril 23% Homeowners 18% Private Passenger Auto 7% 6

7 Aon Benfield U.S. Reserves Industry Reserve Adequacy: The Party Continues In 2010 the U.S. P&C industry enjoyed its fifth consecutive year of favorable reserve development. Despite several instances of individual companies taking adverse development, the industry has released a total of USD46 billion of reserves since This begs the question: How much longer can this favorable development continue? Industry Reserve Development 25.0B 20.0B 15.0B 10.0B 5.0B 0.0B -0.0B -10.0B -15.0B -20.0B -25.0B 2001 Favorable / (Adverse) Reserve Development Last year we estimated industry reserve redundancies of USD21.9 billion. During the year, the industry released USD10.5 billion, so it is natural to expect deterioration in the overall redundancy On a comparative basis, this is exactly what we find: applying standard actuarial reserving methods to the 2010 data indicates a redundancy of USD13.9 billion. However, AIG, which represents 10 percent of total U.S. statutory reserves, published additional disclosures outlining adjustments to their statutory Schedule P triangles in their 2010 combined annual statement. These adjustments cover large portfolio transfers, reinsurance commutations and additional line of business splits, all of which can cause material distortions to mechanically generated indications. To incorporate the effect of these disclosures on the industry reserve position, we separately analyzed the industry excluding AIG and the disclosure-adjusted AIG triangles to derive a total industry view. After making these adjustments, the results indicate that there are approximately USD22 billion of excess reserves across all lines of business, leaving our estimate of the industry position essentially unchanged from last year. The casualty market is unlikely to harden again as long as the industry has more than adequate reserves. We estimate that reserve redundancies will be depleted in two to three years if reserve releases continue at the pace of 2007 to A summary of adequacy by major market segments appears below. U.S. Reserve Estimated Adequacy (USD Billions) Line Estimated Reserves Booked Reserves Remaining Redundancy Favorable / (Adverse) Development Average Years at Run Rate Personal Lines Commercial Property Commercial Liability Workers Compensation (0.5) (1.6) 0.0 n/a Total Excl Financial Guaranty Financial Guaranty (2.4) (1.2) (12.6) (1.6) n/a Total The 2010 release of USD10.5 billion was at the 36th percentile of the estimated range of one-year outcomes. The range is based on a Monte Carlo simulation for accident years 2009 and prior, calibrated to the December 31, 2009 statements. The 90th percentile range for 2010 emergence was from USD22 billion favorable to USD9 billion adverse emergence. See page 26 for a link to the Reserve Study and the Reserve Study Disclosure regarding compensation paid to Aon Benfield. 7

8 Insurance Risk Study Reserve Risk: The Silent Killer Despite the U.S. P&C industry s recent favorable development and a seemingly redundant reserve position, reserve risk remains one of the largest threats to insurer solvency. According to A.M. Best s 2011 Impairment Study, 54 percent of insurer impairments from can be attributed to loss reserve deficiencies and rapid growth or inadequate pricing. Individual accident year development accounts for four of the ten largest U.S. P&C industry events. Combined reserve development on the soft market accident years amounts to USD64 billion: 55 percent more than Katrina, the U.S. industry s largest natural catastrophe. The ultimate loss estimate associated with asbestos and environmental claims accounts for the largest U.S. P&C industry loss event at USD117 billion. These losses manifested themselves almost entirely in the form of unforeseen adverse reserve development suggesting the potential volatility on initial reserve estimates. Largest U.S. P&C Industry Events (USD Billions) Event Nominal Loss Asbestos & Environmental Hurricane Katrina 41.1 AY 2000 Development* 22.0 AY 1999 Development* 19.3 September 11th 18.8 Hurricane Andrew 15.5 Northridge Earthquake 12.5 Hurricane Ike 12.5 AY 1998 Development* 12.1 AY 2001 Development* 10.5 * 10 years of development from Schedule P. Companies looking to benchmark their internal reserve volatility estimates against industry based volatility estimates should be cognizant of how size affects volatility. To illustrate this point, we have applied traditional stochastic reserve methods to the paid loss triangles of more than 450 U.S. P&C insurers. As shown in the graph below, the one year CV of private passenger auto reserves for large companies those with at least USD500 million of carried reserves is 3.4 percent. The average one year CV of reserves of small companies those with USD10 million to USD100 million of carried reserves is 10.2 percent. The difference is 300 percent. Private Passenger Auto One Year Net Reserve CV by Carried Reserve Size One Year Reserve CV 25% 20% 15% 10% Small (USD10M - USD100M); 10.2% Med (USD100M - USD500M); 6.4% Large (>USD500M); 3.4% 5% 0% ,000 Carried Reserves (log scale) USD Millions The relationship between size and reserve volatility holds across all lines of business and is consistent with the results of our loss ratio volatility parameterization. 8

9 Aon Benfield U.S. Reserve Volatility by Line, by Carried Reserve Size One Year Reserve CV Ultimate Reserve CV Line Small USD10M USD100M Medium USD100M USD500M Large > USD500M Small USD10M USD100M Medium USD100M USD500M Large > USD500M All Lines Homeowners Private Passenger Auto Commercial Auto Commercial Multi Peril Workers Compensation Medical PL CM Other Liability CM Other Liability Occ Products Liability Occ 10.4% 8.4% 5.5% 13.4% 10.7% 7.3% 14.5% 12.2% 10.5% 16.9% 13.7% 12.2% 10.2% 6.4% 3.4% 12.8% 7.8% 4.1% 12.3% 7.0% 4.8% 16.0% 9.5% 6.5% 12.8% 10.0% 6.9% 17.0% 13.7% 9.1% 7.5% 4.8% 4.0% 10.1% 6.5% 6.4% 14.4% 13.7% 11.6% 17.6% 16.8% 14.2% 14.2% 13.2% 15.1% 17.4% 16.0% 19.2% 14.9% 11.6% 8.2% 18.6% 15.7% 12.2% 17.9% 11.2% 7.5% 25.0% 16.5% 18.8% Ultimate reserve CV calculated using average of Mack and Over Dispersed Poisson (ODP) Bootstrap methods applied to paid loss triangles by line. One-year reserve CV uses average of the Merz-Wuthrich and ODP Bootstrap methods. All methods adjusted to account for fail factor volatility and reserves more than 10 years old. The table above shows the average measured reserve CVs for insurers with carried reserves within the ranges of USD10 million to USD100 million, USD100 million to USD500 million and more than USD500 million, roughly corresponding with small, medium and large carriers in each line respectively. CVs are shown on both a one year basis, representing the volatility of reserves over the next calendar year, and an ultimate basis, representing the volatility of reserve from their current balance until their full settlement. The all lines reserve CVs are estimated using a single paid loss triangle across all lines of business. To contextualize the USD64 billion industry reserve development on accident years , we can estimate the chance of another reserve event of this magnitude. The table to the right summarizes the implied ultimate reserve risk distribution for the industry. This distribution is based on the P&C industry reserve balance as of December 31, 2010 and assumes that reserves are distributed lognormally with a CV equal to the all lines large company reserve CV of 7.3 percent. On an ultimate basis, adverse development of USD64 billion is a 1 in 15 year event, corresponding roughly to one occurrence each underwriting cycle. These results highlight the importance of developing better processes to measure and manage reserve risk. U.S. P&C Industry Reserve Risk Distribution All Lines, All Years Ultimate Development Return Period % USD Billions % % % % % % % 34.1 Assumptions: Large (>USD500M) All Lines CV with LogNormal Distribution. 9

10 Insurance Risk Study Innovations in Crop Insurance Modeling World population growth will continue to increase demand for food, energy and clothing. With emerging middle classes in Asia and elsewhere, consumers are increasing their per capita consumption of staple foods and also consuming larger quantities of meat, dairy products and vegetable oils. These factors contribute to amplified demand for aggregate grains and oilseeds. Meanwhile, U.S. ethanol policy has increased the demand for corn. All these factors point toward a future with higher crop prices and higher volatility. Data from the Chicago Mercantile Exchange shows that prices for staple crops have more than doubled in the last 10 years. The volatilities implied by options prices spiked in 2008 and have remained significantly above historical levels. Commodity Prices Base Year 1987 = 1.00 Implied Volatility for Crop Commodities Corn Soybeans Wheat Corn Soybeans Wheat Crop insurance is growing rapidly worldwide, both due to recognition of the aforementioned risks and due to additional stimulus from government subsidies Crop Insurance Premium, USD Billions U.S. China , Est. U.S. Focus The U.S. crop insurance program is the largest in the world with USD110 billion in insured value. Driven by the rise in commodities prices, premiums for 2011 are expected to be more than triple the levels of 10 years ago. No other U.S. insurance product matches this premium growth during the last decade. Approved insurance providers now face a wide variety of risks through their participation in multi-peril crop insurance policies. The Risk Management Agency of the U.S. Department of Agriculture has significantly changed its rating and underwriting over time, and the new Standard Reinsurance Agreement threatens future returns. Moreover, the popularity of revenue-based products exposes insurers to substantial commodity price risk at a time when price volatilities are at historically high levels. All of these factors mean that relying on historical performance alone to guide risk management decisions is no longer a viable option. To maximize profitability and manage risk, insurers must understand the risk-reward relationship of each underwritten policy. Aon Benfield Crop Reinsurance Solution (ACReS) provides innovative risk assessment tools for managing the agricultural exposures of today s crop programs. ACReS is designed to provide clients with cutting-edge unit level risk analytics within a scalable and customizable platform. ACReS contains two probabilistic models designed to simulate many crop seasons for each insured unit. Premium, loss and underwriting gain are calculated for each simulated crop season. 10

11 Aon Benfield ACReS Advantages > Provides a comprehensive reflection of current risk, including the current products, premium rates, underwriting methods, unit structure, crop mix and market conditions. > Combines econometric relationships with futures market expectations, maintaining the supply and demand relationships which cause yields and prices to be correlated. > Models risk at the unit level, recognizing differences between individual crop producers. This is also the level at which fund allocation decisions are made. > Provides a long-term view on risk using 115 years of weather data, giving appropriate context for abnormal events. > Allows for accurate forecasting of insured acreage and premium. Many risk management decisions including fund allocation must be made before the crop is planted, when key information such as insured acreage and premium is not available. Beginning in spring 2011, Aon Benfield has partnered with Planalytics, the business weather intelligence firm, to more accurately forecast weather and yields for the next growing season. We can now provide a forward looking fund allocation analysis using Planalytics s forecasting capabilities, in addition to the standard analysis giving equal weight to all historical years. China Focus China s agricultural insurance market has experienced rapid growth as a result of increased government subsidy and program improvements, with nationwide premiums increasing dramatically from USD100 million in 2006 to USD2 billion in It has grown to become the second largest crop insurance market in the world. Currently, only 30 percent of the total value of production is insured, suggesting that China s program could see considerable growth for years to come. An ACReS model has also been developed for China and it represents a significant step forward in the technology available for risk assessment in this rapidly emerging market. There are several key features that distinguish the ACReS model from currently applied risk assessment methodology in China. > Risk is modeled at the county level, using yield data for each of China s 2,876 counties. This granular data significantly improves the accuracy of the model s results compared to currently applied methods. > The ACReS model generates results based on the current policy terms and conditions, yield productivity levels, premium rates, underwriting methods, crop types insured and loss adjustment practices. An analysis based on historical data alone will not capture material changes in these variables. > Explicit consideration is given to the risk of low frequency events. For portfolios that have already experienced a major loss, the model will place this loss into appropriate perspective. For newly established portfolios, appropriate treatment of disaster events will improve accuracy and bring stability to premium rates, coverage and reinsurance arrangements. Sample of ACReS China Risk Mappings Heilongjiang Province Juisan Crop Model Simulation Results Most Risky Risky Neutral Less Risky Least Risky Hegang Insurers and reinsurers face significant challenges in evaluating the risk associated with these insurance programs. In conjunction with their rapid expansion, insurance is being offered in additional locations and the coverage provided is continually changing. Furthermore, insurers have limited historical underwriting results to draw on for ratemaking. Harbin Jixi 11

12 Insurance Risk Study An Optimal Insurer in a Solvency II World Solvency II (SII) is changing the way in which regulatory capital is assessed for European insurers. The results of the latest impact assessment study, QIS 5, suggest that the average solvency ratio for non-life European insurers will drop from over 200 percent to 165 percent. Additionally, unlike the existing Solvency I regime, SII uses a risk-based approach to set the level of each insurer s solvency capital thus requiring more capital to be held for riskier insurance and investment activities. This means that insurers who take a higher level of risk, as measured by SII, will suffer a far greater fall in solvency ratio than those with less risky portfolios (whose solvency ratio may even improve). Despite presenting clear challenges, SII also offers insurers the opportunity to improve their business strategy by better allocating risk and capital to target opportunities that provide the highest return per unit of risk. SII encourages firms to view risk, capital and value from an enterprise-wide perspective. At Aon Benfield, we recognize that insurers must set strategy in accordance with two sets of constraints simultaneously: the capital constraints imposed by regulators, and the economic constraints imposed by stakeholders, including shareholders, policyholders and management. To maximize performance, insurers must pursue a combined strategy for both sides of the balance sheet a strategy that comprehends the potential dependence between insurance and asset risk behavior. To date, very few organizations have optimized their allocation of risk and capital using a framework that captures these important dependencies. In practice, assets and liabilities have been managed by several business units, without a full understanding of the impact on enterprise level risk and capital. For example, credit insurance losses are highly correlated with economic risks; to set asset strategy without considering the impact on insurance risks may result in a strategy that increases overall risk to the firm. Aon Benfield has developed an optimization process for setting consistent strategy across asset and liability risks, recognizing all relevant economic and capital constraints. We believe this process will support insurers to better manage their risk and capital under SII. A brief description of the process follows, with sample exhibits for a hypothetical insurer. 1) Risk Tolerance, Capital Target & Drivers of Value The binding capital metric for many insurers will be the Solvency II capital requirement (SCR) under the Standard Formula. We measure the capital utilization of insurance and asset risks by their contribution to the overall SCR. Insurance company management must set the company s overall risk appetite, target capital and return levels. Risk appetite is often set to maximize shareholder value. Aon Benfield s price-to-book regression study (see page 25) points to a volatility measure of risk as best capturing investor risk tolerances. For example, the insurer may select an overall risk tolerance of 10 percent volatility of surplus and a 165 percent SII ratio as the long term capital target. 2) Identify Optimal Allocation of Insurance Risk Shareholders of non-life insurers normally desire firms with a carefully selected portfolio of insurance risks and an asset strategy that supports their liabilities and enhances their risk-adjusted return. Therefore, when optimizing the strategy of an insurer, the first stage is to optimize the insurance portfolio. An insurer will determine upper and lower bounds for premium by class of business, creating a range of possible portfolios for a given premium volume (see table below). Sample of Premium Allocation by Line LOB Allocation Initial Min Max Motor, Vehicle Lliability 33.2% 28.0% 38.0% Motor, Other Classes 18.0% 15.5% 21.0% Marine, Aviation & Transport 3.7% 2.5% 4.5% Property 30.1% 25.5% 34.5% General Liability 11.5% 8.5% 14.5% Credit And Suretyship 3.5% 2.5% 4.5% 12

13 Aon Benfield We create an internal model of the insurer and, using our proprietary optimization framework, determine the economic and capital efficient frontiers of insurance portfolios the portfolios that provide the maximum expected profit for a specified level of economic volatility or SII capital utilization, respectively. The SII capital efficient frontier differs from the economic frontier, and capital allocations that are efficient under the proposed Standard Formula can be suboptimal from an economic perspective. This is because the proposed SII Standard Formula assesses capital based on prescribed volatilities and correlations for non-cat underwriting risk and prescribed events for natural and man-made catastrophes these prescribed factors are not based on economic best estimates and are often conservative. The goal of our optimization is to identify portfolios of insurance risk that are efficient from both capital and economic perspectives, i.e. portfolios where the two frontiers overlap (see points A and B on the graph). We then select the insurance portfolio on the economic efficient frontier with the highest Sharpe ratio and ROE that is also acceptable given our capital constraint, leading us to select point B. In the next table, this is portfolio 3. 3) Optimization of Asset Strategy Having selected the optimal insurance portfolio, we now optimize the asset strategy within the remaining risk and capital budget for the firm. Since the insurer already has a target SCR level and we know the contribution from the optimal insurance portfolio, we can infer the contribution to the SCR from market risk. We then determine a constrained efficient frontier of asset portfolios with the required market risk capital at this level. The optimal asset portfolio will then be the one on the efficient frontier that achieves our overall target surplus volatility. > The asset strategy that is numerically optimal may still lack desirable qualitative features. We can refine our optimal asset portfolio with qualitative constraints, such as a minimum allocation to cash equivalents for liquidity purposes and a maximum permissible asset/liability mismatch at key rate durations. For a model insurer based on the average non-life company in Europe, the overall financial and economic impact of the balance sheet optimization is an increase to expected profit of EUR1.64 million on a EUR100 million premium base, an improvement of shareholder return from to percent and no increase in volatility or required capital under SII. Economic and Capital Efficient Frontiers Profit Optimization of Insurance Risk Under Economic Risk Measures Portfolio Statistics A Economic Capital Initial 1 2 Initial Portfolio 8.0% 8.1% 8.2% 8.3% 8.4% 8.5% 8.6% 8.7% 8.8% Economic Volatility 3 Optimal 4 Economic Volatility 8.62% 8.0% 8.4% 8.65% 8.77% Profit Non-Life SCR Sharpe Ratio 10.2% 22.3% 31.1% 31.0% 31.0% Return on Capital 5.9% 4.8% 6.2% 6.7% 6.9% Motor, Vehicle Liability 33.2% 28.0% 28.0% 28.0% 28.0% B Several factors are important to consider at this stage: > The asset strategy optimization must be performed in the context of the overall balance sheet, so that we capture correlation among economic liabilities and the interaction of liability uncertainty with economic risk. Allocation Motor, Other Classes 18.0% 18.0% 15.5% 15.5% 15.5% Marine, Aviation & Transport 3.7% 2.5% 4.5% 4.5% 4.5% Property 30.1% 25.5% 26.9% 31.1% 33.0% General Liability 11.5% 8.5% 10.5% 13.7% 14.5% Credit and Suretyship 3.5% 2.5% 4.5% 4.5% 4.5% 13

14 Insurance Risk Study Innovations in Hedging Variable Annuity Basics The variable annuity (VA) market globally has total deposits of approximately USD2.0 trillion of which roughly USD400 billion have embedded guarantees. The largest VA markets are the U.S. and Japan. A typical guarantee on a VA contract provides a financial guarantee such as a death benefit, living benefit or an income benefit. Insurance companies typically charge policyholders a rider fee of between 60 and 200 basis points of account value for providing these guarantees. The fee is intended to cover the economic cost of the guarantee, which amounts to the cost of replicating the guarantee in the financial market using derivatives and other financial instruments. In order to properly hedge the embedded market risks in theses guarantees, insurers employ various strategies, including dynamic hedging. For a dynamic hedge to be effective, the underlying assets need to be rebalanced as their market prices change. In practice, however, insurance companies typically set trading tolerances and only rebalance if the risk breaches these tolerances. The main market risk factors affecting the value of VA guarantees are changes in equity markets, changes in interest rates and changes in implied volatility. It is common to refer to these sensitivities as Greeks: Delta for the effect of changes in equity markets, Rho for changes in interest rates and Vega for changes in implied volatility. To calculate these risk factors on large portfolios representing millions of policyholders, insurers have been required to deploy massive amounts of computing power. Sometimes thousands of processors are used to produce estimates of the risks factors required for hedging. Until recently, the software used to estimate the risk required eight hours or more to produce a range of Greek estimates under various market scenarios. Given the slow runtime, insurers will often run these large computations overnight and use only rough approximations to estimate the risks during the next trading day. These estimation techniques are prone to substantial errors, which grow larger the more that markets move just when the greatest accuracy is required. The impact of miscalculating the actual risk can translate into significant gains or losses due to the large quantity of assets used to hedge a VA portfolio. We estimate that North American VA writers lost USD3 billion due to hedge estimation errors during the financial crisis. Introducing Pathwise To help our clients better manage these hedge positions, Aon Benfield s Annuity Solutions Group developed PathWise, the industry s fastest VA risk management system. By leveraging the latest technology and employing cutting-edge parallel processing techniques, the Annuity Solutions Group can perform computations which typically take eight hours of runtime in just a few minutes. What does this mean for risk management? By using PathWise, insurers can substantially reduce the risk profile of their VA exposure by managing their portfolio in real-time. Trading is now based on timely and accurate intraday information about the liabilities and assets rather than being based on yesterday s information and potentially large estimation errors. A Case Study in Hedging The following study compares three dynamic hedging strategies. The first strategy uses information from nightly simulations and, using Taylor approximation techniques, estimates the Greeks at the end of the next day this is the method used by many insurance companies today. The second strategy uses real-time simulation to calculate Greeks once at the end of the trading day and rebalances once per day. The third strategy uses real-time simulation to calculate Greeks intraday and executes trades based on real-time risk factors. 14

15 Aon Benfield Weekly Net Hedged P&L Real-Time Simulation, Intraday Rebalance Real-Time Simulation, End of Day Rebalance Taylor Approximation Greeks, End of Day Rebalance 0 Net P&L (USD Millions) /1/07 1/1/08 7/1/08 1/1/09 7/1/09 The chart and table illustrate the performance of each of these strategies employed by a hypothetical insurer with a USD10 billion VA portfolio from the middle of 2007 through the end of The hypothetical insurer s profit/loss volatility is 56 to 88 percent lower using real-time simulation techniques in their hedging strategy. Further, these strategies have reduced their total hedging loss by 24 to 54 percent. Executing an accurate real-time hedging strategy requires cutting-edge tools and sophisticated computing power. Pathwise makes such a strategy possible. Comparison of VA Hedging Strategies (USD Millions) Statistic Taylor Approximation Greeks End of Day Rebalance Real-Time Simulation End of Day Rebalance Real-Time Simulation Intraday Rebalance Standard Deviation of P&L Standard Deviation improvement (relative to Taylor Approximation) 56% 88% Cumulative Loss 285 bps 218 bps 130 bps Cumulative Loss Improvement (relative to Taylor Approximation) 24% 54% Aon Benfield s Annuity Solutions Group believes that new technology can be leveraged by VA product carriers to improve computation run time and to create the ability to execute data driven hedges in real-time. Even though VA risk management is complex and multifaceted, the advantages of speed translate directly to improved product design and competitiveness as well as substantial bottom line savings. PathWise and all other securities-related advice, products and services of Aon Benfield are offered and distributed solely through Aon Benfield s affiliate, Aon Benfield Securities, Inc. ( Aon Benfield Securities ) which is a member of the Financial Industry Regulatory Authority and is registered as both a brokerdealer and investment adviser with the Securities and Exchange Commission. The above information regarding PathWise and variable annuity hedging is not intended, nor should be considered, as (1) an offer to sell any security, loan or financial product, (2) a solicitation or basis for any contract for purchase of any security, loan or other financial product, (3) an official confirmation, or (4) a statement of Aon Benfield Securities or any of its affiliates. No representation is made that the products or services described are suitable or appropriate for any party, in any location or jurisdiction. Potential users of such products or services are advised to undertake an independent review of applicable legal, tax, regulatory, actuarial and accounting issues. Any offer of such products or services will be made only through definitive agreements and related documents provided by Aon Benfield Securities or an appropriately licensed affiliate. 15

16 Insurance Risk Study Correlation and the Pricing Cycle Correlation of Underwriting Results Correlation between different lines of business is central to a realistic assessment of aggregate portfolio risk, and in fact becomes more and more significant for larger and larger companies. Modeling is invariably performed using an analysis-synthesis paradigm: analysis is carried out at the product or business unit level and is then aggregated to the company level. In most applications, results are more significantly impacted by the correlation and dependency assumptions made during the synthesis step than by all the detailed assumptions made during the analysis step. The Study determines correlations between lines within each country. Although not shown here, we have also calculated confidence intervals for each correlation coefficient. Correlation between lines is computed by examining the results from larger companies that write pairs of lines in the same country. The tables below show a sampling of the results available for Australia, China, Germany, Japan, U.K. and U.S. Strong positive correlation is evident between most pairs of standard lines in these matrices. Australia General Liability Marine, Aviation & Transit Motor Property Workers Comp General Liability 19% 21% -24% 21% China Accident & Health Agriculture Credit Engineering General Liability Marine, Aviation & Transit Motor Property Marine, Aviation & Transit 19% 31% -3% 21% Motor 21% 31% 25% 14% Property -24% -3% 25% -6% Workers Comp 21% 21% 14% -6% Correlation is a measure of association between two random quantities. It varies between -1 and +1, with +1 indicating a perfect increasing linear relationship and -1 a perfect decreasing relationship. The closer the coefficient is to either +1 or -1 the stronger the linear association between the two variables. A value of 0 indicates no linear relationship whatsoever. All correlations in the Study are estimated using the Pearson sample correlation coefficient. In each table the correlations shown in bold are statistically different from zero at the 90% confidence level. Accident & Health 23% 24% 12% 27% 15% 45% 56% Agriculture 23% 46% 8% 28% 13% 22% -3% Credit 24% 46% 68% 18% 17% 30% 23% Engineering 12% 8% 68% 36% 37% 48% 24% General Liability 27% 28% 18% 36% 33% 52% 44% Marine, Aviation & Transit 15% 13% 17% 37% 33% 42% 18% Motor 45% 22% 30% 48% 52% 42% 52% Property 56% -3% 23% 24% 44% 18% 52% 16

17 Aon Benfield Germany Accident & Health 46% 34% 43% -28% -8% 15% Credit 46% 53% n/a -14% 25% 19% General Liability 34% 53% 34% 28% 2% 24% Legal Protection 43% n/a 34% -48% -34% 13% Marine, Aviation & Transit -28% -14% 28% -48% 42% 28% Motor -8% 25% 2% -34% 42% 7% Property 15% 19% 24% 13% 28% 7% Japan Accident & Health 22% 2% 48% 35% 50% General Liability 22% -1% 3% 33% 26% Marine, Aviation & Transit 2% -1% 15% 33% -2% Motor 48% 3% 15% 58% 41% Property 35% 33% 33% 58% 31% Workers Comp 50% 26% -2% 41% 31% U.K. U.S. Commercial Auto Commercial Multi Peril Homeowners Medical Malpractice CM Other Liability CM Other Liability Occ Personal Auto Liability Products Liability Occ Workers Comp Accident & Health Commercial Lines Liability Commercial Motor Commercial Property Financial Loss Household & Domestic Private Motor Accident & Health General Liability Marine, Aviation & Transit Motor Property Workers Comp Accident & Health Credit General Liability Legal Protection Marine, Aviation & Transit Motor Property Accident & Health 50% 74% 57% 9% 17% 52% Commercial Lines Liability 50% 53% 36% -1% 44% 52% Commercial Motor 74% 53% 55% -11% 23% 69% Commercial Property 57% 36% 55% -26% 60% 37% Financial Loss 9% -1% -11% -26% 8% 13% Household & Domestic 17% 44% 23% 60% 8% 14% Private Motor 52% 52% 69% 37% 13% 14% Commercial Auto 54% 9% 72% 44% 66% 30% 72% 60% Commercial Multi Peril 54% 22% 58% 42% 48% 29% 41% 42% Homeowners 9% 22% 1% -2% 1% 8% 14% -4% Medical Malpractice CM 72% 58% 1% 71% 76% 48% 73% 68% Other Liability CM 44% 42% -2% 71% 58% 39% 31% 61% Other Liability Occ 66% 48% 1% 76% 58% 33% 67% 62% Personal Auto Liability 30% 29% 8% 48% 39% 33% 43% 32% Products Liability Occ 72% 41% 14% 73% 31% 67% 43% 63% Workers Comp 60% 42% -4% 68% 61% 62% 32% 63% 17

18 Insurance Risk Study Macroeconomic Correlation Correlation among macroeconomic factors is a very important consideration in risk modeling. The interaction of inflation and GDP growth with loss ratios and investment returns has a profound effect on insurer financial health and stability as the market reaction to a potential double dip recession in August shows. The following matrix shows correlation coefficients for various macroeconomic variables that impact an insurer s balance sheet. The Consumer Price Index and Producer Price Index are highly correlated, but they do not show particularly strong correlation with other factors. This may be because inflation has been relatively tame for the last 25 years. GDP growth shows strong negative correlation with changes in unemployment. When GDP drops or unemployment increases credit spreads tend to increase, property values fall and the CBOE Volatility Index (VIX) increases. Treasury yields and corporate bond spreads are inversely correlated; financial market fears may push investors to flee corporates for the safety of Treasuries, causing corporate yields to rise and Treasury yields to fall. The VIX is sensitive to fear and directionally has the appropriate signs: positive correlation with spreads and unemployment, negative correlation with GDP and stock returns. These coefficients represent only the beginning of an analysis of macroeconomic dependency. Lags may be appropriate among certain variables. For example, GDP and Stock Returns show the strongest correlation when stock returns lead GDP by two quarters, suggesting that stock prices adjust as soon as expectations for GDP change. This result is consistent with the Efficient Market Hypothesis. It is also important to consider values that shift over time. In successive eight-quarter periods, stock returns and property returns showed zero or negative correlations until the recent financial crisis when correlations turned strongly positive. This fact alone suggests that a simplistic view of correlation among macroeconomic factors will significantly underestimate material balance sheet risks. Macroeconomic Correlations Inflation (CPI-U) Inflation (PPI) GDP Growth Unemployment Change 3-Month T-Bill Rate 1-3 Year Treasuries AAA-AA 3-5 Year Spread BBB 3-5 Year Spread S&P 500 Returns VIX Property Returns Inflation (CPI-U) 78% -3% -2% 32% 27% -11% -26% -13% -23% 13% Inflation (PPI) 78% 4% -7% 30% 9% -4% -20% -7% -22% 12% GDP Growth -3% 4% -70% -4% 24% -64% -69% 5% -44% 51% Unemployment Change -2% -7% -70% -2% -25% 62% 76% -1% 57% -49% 3-Month T-Bill Rate 32% 30% -4% -2% 98% -32% -58% -8% -24% 16% 1-3 Year Treasuries 27% 9% 24% -25% 98% -38% -60% 12% -27% 13% AAA-AA 3-5 Year Spread -11% -4% -64% 62% -32% -38% 85% -42% 62% -63% BBB 3-5 Year Spread -26% -20% -69% 76% -58% -60% 85% -33% 67% -53% S&P 500 Returns -13% -7% 5% -1% -8% 12% -42% -33% -50% 9% VIX -23% -22% -44% 57% -24% -27% 62% 67% -50% -30% Property Returns 13% 12% 51% -49% 16% 13% -63% -53% 9% -30% 18

19 Aon Benfield Global Market Review With rates continuing to soften and investment yields depressed, insurers are under intense pressure to expand their top lines. The next several pages present a summary of global insurance markets: the size of each market by premium, premium relative to GDP (insurance penetration ratio), loss ratios and volatility of loss ratios. We have segmented premium into motor, property and liability lines for the top 50 markets. Global Premium by Product Line Motor: USD532B U.S. Middle East & Africa Rest of Europe Property: USD381B U.S. Middle East & Africa Liability: USD268B U.S. Middle East & Africa Brazil Brazil U.K. Rest of Euro Area Rest of Europe Canada Rest of Americas China Japan France Germany U.K. Rest of Euro Area South Korea Rest of APAC Canada Rest of Americas China Japan South Korea Brazil Canada Rest of Americas China Japan Rest of Europe U.K. Rest of Euro Area Rest of APAC France Germany South Korea Rest of APAC France Germany Notes: Numbers presented are the latest available. Motor includes all motor insurance coverages. Property includes construction, engineering, marine, aviation and transit insurance as well as property. Liability includes general liability, workers compensation, surety, bonds, credit and miscellaneous coverages. Top 50 Markets by Gross Written Premium Country P&C GWP (USD Billions) GDP (USD Billions) Population (Millions) Premium / GDP Ratio GDP per Capita U.S , % 46,795 Japan , % 43,161 Germany , % 40,697 U.K , % 35,846 France , % 39,541 China , , % 4,398 Italy , % 33,681 South Korea , % 20,656 Canada , % 46,254 Spain , % 30,156 Australia , % 56,763 Brazil , % 10,275 Netherlands % 46,494 Russia , % 10,560 Switzerland % 68,557 Belgium % 44,641 Norway % 88,337 Austria % 45,860 Mexico , % 9,137 Sweden % 50,155 Denmark % 56,196 Poland % 12,188 India , , % 1,293 Venezuela % 10,518 Turkey % 9,416 Argentina % 8,865 South Africa % 7,290 Ireland % 43,730 Czech Republic % 18,857 Finland % 45,488 Portugal % 21,313 Iran % 4,250 U.A.E % 58,633 Israel % 28,522 Thailand % 4,779 Malaysia % 8,283 Greece % 28,384 Colombia % 6,384 Taiwan % 8,853 Luxembourg % 109,179 Ukraine % 3,022 Chile % 12,039 Indonesia % 2,877 Hong Kong % 11,978 Romania % 7,379 New Zealand % 29,527 Singapore % 46,976 Slovenia % 23,923 Puerto Rico % 17,020 Hungary % 12,927 Grand Total 1, , , % 12,111 Note: Ranks are based on total P&C Written Premium 19

20 Insurance Risk Study Global Statistics: Motor Gross Written Premium Average Loss Ratio Graph Country Latest (USD Millions) 5 Yr Annual Growth 1 Yr 3 Yr 5 Yr 10 Yr SD 10 Yr CV 5 Yr LR Argentina 3, % 66.2% 65.6% 66.8% 3.5% 5.2% Australia 8, % 90.4% 94.9% 90.7% 7.2% 7.8% Austria 3, % 71.8% 67.2% 65.4% 6.0% 8.8% Belgium 4, % 73.8% 81.0% 77.9% 9.5% 12.1% Brazil 13, % 63.2% 61.5% 60.0% 4.0% 6.5% Canada 15, % 74.2% 73.5% 72.1% 6.2% 8.3% Chile % 72.0% 68.6% 66.9% 4.5% 6.5% China 31, % 55.4% 55.6% 55.5% 3.6% 6.4% Colombia 1, % 52.1% 54.9% 53.7% 2.4% 4.4% Czech Republic 2, % 51.5% 50.7% 51.3% 3.4% 6.5% Denmark 2, % 68.7% 63.6% 62.1% 9.9% 14.8% Finland 1, % 62.5% 70.8% 73.5% 6.1% 8.1% France 24, % 88.2% 83.9% 82.8% 3.4% 4.1% Germany 27, % 98.5% 97.1% 94.4% 4.7% 5.1% Hong Kong % 47.5% 57.4% 57.3% 6.0% 10.6% Hungary % 62.4% 60.3% 59.3% 2.1% 3.6% India 3, % 69.7% 70.5% 58.8% 39.2% 54.3% Indonesia % 54.0% 51.6% 50.0% 5.4% 11.6% Iran 2, % 73.4% 77.3% 79.9% 6.9% 8.4% Ireland 1, % 94.8% 85.0% 76.6% 14.2% 18.1% Israel 2, % 107.9% 106.2% 100.4% 8.0% 8.1% Italy 27, % 85.6% 80.2% 77.9% 5.5% 7.1% Japan 45, % 70.4% 69.0% 67.5% 2.6% 4.0% Luxembourg % 74.6% 70.7% 69.7% 4.2% 6.0% Malaysia 1, % 84.0% 77.0% 74.9% 8.2% 11.9% Mexico 3, % 71.3% 71.7% 72.4% 3.1% 4.4% Morocco % 73.3% 73.5% 76.9% 8.4% 11.3% Netherlands 5, % 76.6% 71.9% 69.8% 4.7% 6.6% New Zealand % 66.7% 70.0% 68.8% 2.4% 3.5% Norway 2, % 70.7% 70.9% 69.9% 3.3% 4.8% Poland 4, % 74.9% 72.0% 69.3% 4.8% 7.0% Portugal 1, % 80.3% 76.4% 72.5% 4.5% 6.3% Puerto Rico % 61.0% 60.1% 61.8% 3.9% 6.1% Romania 1, % 62.1% 64.1% 63.7% 4.4% 7.1% Russia 7, % 68.7% 66.0% 60.2% 13.0% 23.0% Saudi Arabia % 58.1% 56.5% 54.7% 4.8% 8.8% Singapore % 73.1% 78.3% 78.4% 10.2% 13.0% Slovenia % 70.8% 67.2% 65.2% 4.1% 6.3% South Africa 2, % 70.9% 70.0% 69.2% 3.9% 5.4% South Korea 8, % 74.2% 71.1% 72.5% 4.1% 5.8% Spain 15, % 78.0% 72.9% 74.5% 6.4% 8.5% Switzerland 5, % 68.5% 62.3% 62.1% 4.4% 6.9% Taiwan 1, % 61.9% 59.2% 58.6% 2.7% 4.4% Thailand 2, % 53.1% 55.5% 56.0% 2.2% 3.8% Turkey 3, % 85.0% 76.5% 75.2% 9.3% 13.1% U.A.E 1, % 70.2% 68.2% 67.9% 7.6% 10.6% U.K. 19, % 95.0% 83.7% 81.6% 5.9% 7.5% U.S. 187, % 62.4% 62.7% 61.1% 4.2% 6.8% Ukraine % 48.5% 48.7% 45.4% 7.5% 16.4% Venezuela 4, % 60.8% 55.8% 54.8% 6.9% 12.5% Grand Total 522, % 70.3% 70.1% 68.2% 2.6% 3.9% 0% 50% 100% 20