Insurance Risk Study. Brian Alvers Analytics Insights Conference July Prepared by Aon Benfield Analytics

Transcription

1 Insurance Risk Study Brian Alvers 2014 Analytics Insights Conference July Prepared by Aon Benfield Analytics

2 Aon Benfield Insurance Risk Study Informed Parameterization of Risk Models Objective Insurance Risk Study determines credible global insurance volatility benchmarks for use in underwriting risk modeling Motivation: robust empirical quantification of all aspects of underwriting risk Systemic volatility parameters by country, by line Forty nine countries, 90% of global premium Results for eight core lines of business Available as input to any simulation tool Loss ratio correlation between lines within country Correlation between macroeconomic and insurance variables Aon Benfield approach to growth strategies Insurance profitability, economic variables, and demographics by country Detailed country profile of six individual insurance markets Recognized by major US rating agencies Published annually in September Eighth edition released in

3 Introduction Convergence of capital markets and insurance markets a reality for property catastrophe Pension fund, high-net worth individual, hedge fund and other capital Made it cheap to transfer catastrophe risk into the capital markets Exciting prospect: extension to other, largely non-catastrophe driven perils Requires capital market and investor acceptance of underlying risk modeling Just what the Aon Benfield Insurance Risk Study has been focused on since 2006 For the first time, we are reporting combined ratios by country in order to identify potential growth opportunities Plus six in-depth country studies Study is the cornerstone of Aon Benfield Analytics integrated and comprehensive risk modeling and risk assessment capabilities Reinsurance optimization framework ERM and economic capital modeling 3

4 Agenda Section 1 Section 2 Section 3 Section 4 Section 5 Section 6 Global Risk Parameters U.S. Reserve Adequacy and Risk Global Correlation Between Lines Macroeconomic Correlation Global Premium, Profitability and Opportunities Extending Insurance-Linked Securities 4

5 Section 1: Global Risk Parameters

6 Systemic Insurance Risk Asset Portfolio Risk Insurance Portfolio Risk Asset portfolio theory: risk does not diversify beyond systemic market risk Insurance risk by line shows same behavior Risk does not completely diversify with increasing volume Naïve insurance risk model incorrectly assumes risk decreases to zero Level of systemic insurance risk varies by line Aon Benfield Insurance Risk Study determines level of systemic insurance risk by line 6

7 Coefficient Of Variation Of Gross Loss Ratio By Country 7

8 U.S. Risk Parameters Coefficient Of Variation Of Gross Loss Ratio,

9 Impact Of The Pricing Cycle 9

10 Coefficient of Variation of Loss Ratio for Each Line by Country Reported CVs are of gross loss ratios, except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Thailand, Uruguay and Venezuela, which are of net loss ratios. Accident & Health is defined differently in each country; it may include pure accident A&H coverage, credit A&H, and individual or group A&H. In the U.S., A&H makes up about 80 percent of the Other line of business with the balance of the line being primarily credit insurance. Property volatility statistics include catastrophe losses. 10

11 Coefficient of Variation of Loss Ratio for Each Line by Country Reported CVs are of gross loss ratios, except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Thailand, Uruguay and Venezuela, which are of net loss ratios. Accident & Health is defined differently in each country; it may include pure accident A&H coverage, credit A&H, and individual or group A&H. In the U.S., A&H makes up about 80 percent of the Other line of business with the balance of the line being primarily credit insurance. Property volatility statistics include catastrophe losses. 11

12 Coefficient of Variation of Loss Ratio for Each Line by Country Reported CVs are of gross loss ratios, except for Argentina, Australia, Bolivia, Chile, Ecuador, India, Malaysia, Singapore, Thailand, Uruguay and Venezuela, which are of net loss ratios. Accident & Health is defined differently in each country; it may include pure accident A&H coverage, credit A&H, and individual or group A&H. In the U.S., A&H makes up about 80 percent of the Other line of business with the balance of the line being primarily credit insurance. Property volatility statistics include catastrophe losses. 12

13 Section 2: U.S. Reserve Adequacy and Risk

14 U.S. P&C Industry Reserve Development ( ) (5.0) (10.0) (15.0) (20.0) (25.0) One Year Reserve Development ($B) (0.6) Adjusted * (10.5) (14.1) (22.3) Calendar Year *Adjustments include Financial Lines development in and AIG adverse development in development per P&C Industry data as compiled by SNL through May 6, 2014 Total favorable development in 2013 of USD14.8 Billion 14

15 U.S. Reserve Estimated Adequacy at YE 2013 (USD Billions) Reserve Development Summary ($B) Estimated Booked Est. Redundancy Favorable / (Adverse) Development Years at Line Reserves Reserves at YE Average Run Rate Personal Lines Commercial Lines (2.8) N/A Commercial Property Commercial Liability Workers Compensation (4.6) (0.5) (1.6) (0.0) (0.3) N/A Financial Guaranty (1.4) (0.4) N/A Total P&C Industry undiscounted statutory reserves as of December 31, 2013 estimated to be USD6.5 Billion redundant USD14.8 Billion reserves released in calendar year 2013, also the highest amount since 2010 At the current average run rate, the redundancy should be eliminated within half a year 15

16 Drivers of 2013 Reserve Adequacy Deterioration All Lines 15 Drivers of 2013 Reserve Adequacy Deterioration All Lines ($B) (14.8) YE2012 Reserve Redundancy Reserves Release During 2013 Prior Year Favorable Loss Emergence Accident Year 2013 Redundancy YE2013 Reserve Redundancy The total amount of industry reserve redundancy declined USD2.7B during 2013 Waterfall exhibit shows that the decline is driven by: Large amount of reserves released during 2013 of USD14.8B, offset by Favorable loss emergence from prior years worth USD11.9B Most of the reserve cushion of USD6.5B at YE2013 has already been released as of Q1 2014, with USD5.4B in reserve releases 16

17 Drivers of 2013 Reserve Adequacy Deterioration Personal & Commercial Lines Drivers of 2013 Reserve Adequacy Deterioration Personal Lines ($B) Drivers of 2013 Reserve Adequacy Deterioration Commercial Lines ($B) (6.0) (0.9) (8.8) (3.4) (2.8) YE2012 Reserve Redundancy Reserves Release During 2013 Prior Year Favorable Loss Emergence Accident Year 2013 Redundancy YE2013 Reserve Redundancy -12 YE2012 Reserve Deficiency Reserves Release During 2013 Prior Year Favorable Loss Emergence Accident Year 2013 Deficiency YE2013 Reserve Deficiency Personal lines reserve redundancy declined USD0.8B during 2013 Driven by USD6.0B in reserves released during 2013, offset by Favorable prior year loss emergence and AY2013 conservatism worth approximately USD5.2B Some carriers have already released another USD4.1B of reserves through Q Commercial lines reserve deficiency increased USD1.9B during 2013 Driven by USD12.2B in reserve releases in 2013 and AY2013 deficiency, offset by Favorable prior year loss emergence of USD10.2B Continued reserve releases during Q of USD1.4B have pushed the commercial sector further into the hole 17

18 Summary of Favorable / (Adverse) Reserve Development Q by Company Focus 2014 Q1 Reserve Development Summary Favorable / (Adverse) Company Focus Development ($B) Personal 4.1 Commercial 1.4 Other 0.0 Total 5.4 Source: SNL Financial Companies continued to release reserves in the first quarter of 2014 Over 40% of the year end 2013 personal lines redundancy Increased pressure on commercial lines as more releases despite deficiency at year end

19 U.S. Reserve Volatility By Line, By Carried Reserve Size U.S. Reserve Volatility by Line, by Reserve Size One Year Reserve CV Ultimate Reserve CV Line Small $10M - $100M Medium $100M - $500M Large > $500M Small $10M - $100M Medium $100M - $500M All Lines 11.1% 8.4% 5.8% 13.8% 10.6% 7.4% Large > $500M Homeow ners 16.7% 12.6% 10.0% 19.2% 14.4% 11.0% Private Passenger Auto 8.9% 6.5% 3.0% 11.2% 7.7% 3.5% Commercial Auto 12.5% 6.8% 4.4% 16.0% 9.0% 5.9% Commercial Multi Peril 12.5% 10.6% 6.7% 16.4% 14.4% 8.5% Workers Compensation 7.3% 5.3% 2.4% 9.7% 7.1% 3.7% Medical PL - CM 17.8% 13.0% 7.6% 21.2% 15.8% 9.8% Other Liability - CM 14.3% 15.1% 11.3% 17.2% 18.6% 14.1% Other Liability - Occ 15.2% 11.9% 5.9% 19.0% 14.9% 8.8% Products Liability - Occ 18.0% 12.7% 4.3% 23.5% 19.5% 11.0% Ultimate reserve CV calculated using average of Mack and 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 tail factor volatility and reserves more than 10 years old. 19

20 Section 3: Global Correlation Between Lines

21 U.S. Underwriting Correlation between Lines HO PPA CMP CA WC OLO MMC OLC PLO HO 100.0% 9.6% 17.8% 14.3% 3.0% -0.3% 5.4% -0.9% 8.9% PPA 9.6% 100.0% 29.1% 31.2% 31.7% 33.3% 52.8% 42.0% 47.0% CMP 17.8% 29.1% 100.0% 55.1% 45.3% 50.1% 57.8% 43.9% 40.6% CA 14.3% 31.2% 55.1% 100.0% 61.0% 67.0% 72.3% 42.9% 71.0% WC 3.0% 31.7% 45.3% 61.0% 100.0% 60.8% 67.2% 60.0% 63.3% OLO -0.3% 33.3% 50.1% 67.0% 60.8% 100.0% 77.1% 58.6% 66.3% MMC 5.4% 52.8% 57.8% 72.3% 67.2% 77.1% 100.0% 71.2% 72.2% OLC -0.9% 42.0% 43.9% 42.9% 60.0% 58.6% 71.2% 100.0% 34.7% PLO 8.9% 47.0% 40.6% 71.0% 63.3% 66.3% 72.2% 34.7% 100.0% Correlation is a measure of association between two random quantities. It varies between -1 and +1, with +1 indicating a perfect increasing linear relationship and -1 a perfect decreasing relationship. The closer the coefficient is to either +1 or -1 the stronger the linear association between the two variables. A value of 0 indicates no linear relationship whatsoever. All correlations in the Study are estimated using the Pearson sample correlation coefficient. In each table the correlations shown in bold are statistically different from zero at the 90 percent confidence level. 21

22 Volatility and Correlation Link to Volume Correlation Correlation between segments increases as volume grows, as impact of process risk diversifies away Volatility decreases as volume grows, as impact of process risk diversifies away, leaving systemic/parameter risk Volatility Segment Volume 22

23 International Underwriting Correlation between Lines China Correlation matrix calculated for each country where we estimate more than one line of business loss ratio CV 23

24 Section 4: Macroeconomic Correlation

25 U.S. Macroeconomic Correlation 25

26 U.S. Macroeconomic Correlation Highlights CPI and PPI highly correlated, but not strong with other factors GDP growth shows strong negative correlation with changes in unemployment Treasury yields and corporate bond spreads are inversely related VIX is sensitive to fear and directionally has the appropriate signs: Positive correlation with spreads and unemployment Negative correlation with GDP and equity returns 26

27 Section 5: Global Premium, Profitability and Opportunities

28 Global 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. 28

29 Top 50 P&C Markets by Gross Written Premium 29

30 U.S. Profitability by Line of Business 30

31 Underwriting Outperformance in the Long Run One Year Transition Matrix All Lines Combined Ratio Five Year Transition Matrix All Lines Combined Ratio Transition matrices measure percentage of companies in each underwriting performance decile at the start and end of evaluation period Results suggest sustained underwriting outperformance is achievable 57% of top decile writers were top decile one year later 37% of top decile writers were top decile five years later 31

32 Premium Growth and Loss Ratio Performance by Country across Lines 32

33 Premium Growth and Loss Ratio Performance by Country across Lines 33

34 Aon Benfield Country Opportunity Index 34

35 Contender Geography Growth, Profitability and Volatility 35

36 Contender Geography Demographics 36

37 Contender Geography Credit Rating and Risk Perspectives 37

38 Market Concentration 38

39 Section 6: Extending Insurance-Linked Securities

40 Characteristics of Property Cat Risk Natural Demand Property values and demographics have driven huge concentrations in the industry relative to even today s very adequate capital levels, which in turn drives a natural demand for risk transfer products. Loss Modeling The development of computer models for natural catastrophes, on-going since the late 1980 s, has created a generally accepted currency to value the loss potential a given risk portfolio. Modeling has been successful in part because natural catastrophe events are driven by laws of nature and are not social science phenomenon with changing, reflexive and reactive parameters and causes. Loss Triggers Modeling, and the physical drivers of loss, also allows for a range of non-indemnity loss triggers. Rating Agency Capital Rating agency capital models have separated out catastrophe risk and assigned a clearly defined capital charge specifically to cover it, unencumbered by complications of diversification benefits and other technicalities. The rating agency credit is often as much, or greater than, a company s own economic capital model credit. Credit Risk Purchasers of traditional covers are always concerned with the credit risk of the product they purchase, especially for top layers, as well as the historical volatility in pricing and availability. As a result, they are open to alternative solutions and find fully collateralized ILS structures very attractive. 40

41 Characteristics of Property Cat Risk, ctd. Default Profile The loss profile of high layer catastrophe programs mirrors bond default profiles very closely: there is a low probability of a loss, but given a loss a reasonably high probability of a total loss, producing a loss profile familiar to fixed income investors. Equity Tranche As a result of the loss profile, there is no need for an equity tranche in a cat bond. Equity tranches are a big complication in many (non-insurance) securitizations because they create a Variable Interest Entity (VIE) residual interest that generally remains on the balance sheet of the issuer. Uncorrelated Returns The loss profile of cat bonds is manifestly uncorrelated with other asset classes, at least a priori. Investors saw the attractiveness of the ILS asset class during the financial crisis (collateral trust problems notwithstanding, but these problems have now been solved). Quick Emergence Major property catastrophe risk events are headline news; the fact of a loss or potential loss emerges very quickly, indeed instantaneously for earthquakes. Quick Loss Settlement Losses from property catastrophe events generally reach their final settlement valuation in a matter of months. There are very few issues with late reported claims or slow loss development. 41

42 Lines Potentially Suitable for Non-Cat Securitizations Characteristic Property Risk Workers Comp Commercial Auto Medical PL Vanilla GL D&O Aviation General Comments Natural Demand (Reinsurance purchased) Loss Modeling Poor to moderate data Large losses High limit occurrence Evolving, no standard Data rich Detailed rating Good exposures Occ and cat with MOAL Predictive models Same as WC Extends personal auto Some occ Predictive models Frequency risk = tort reform Moderate occ / agg Good, but tort risk Data rich Detailed rating Heterogeneous Little for low limits Predictive models Data rich Systemic? Little, unappetizing product Varies by primary carrier Data rich Already cat like Substantial; but traditional is cheap Very good; AeroMetrica Uncorrelated Returns / Terror / Terror? / Terror Quick Emergence Slow Quick Loss Settlement Slow, but consistent; BobCat solution Moderate Good: claims made Slow CWA vs CWOP Moderate Good: claims made Fast Moderate Moderate Moderate 42

43 Contacts Brian Alvers, FCAS, MAAA Head of Actuarial - Americas Aon Benfield Analytics brian.alvers@aonbenfield.com 43