Interesting and unusual risks

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Susan Charles
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1 Interesting and unusual risks Client seminar April 2013 Eric Welz

2 Risk 2

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4 Unusual risks 4

5 Some unusual risks Insure your smile for $10 million 5

6 Wedding insurance Cost of wedding can easily exceed R100,000 Cancellation because of extreme weather, missing caterer Change of heart bride/groom left standing at altar Rider: Cost of professional counselling to mend a broken heart 6

7 Unusual Life risks Cover the armed forces eg South Korean army majority of deaths are suicides (before Kim launches nuclear armageddon) SANDF majority of deaths are HIV/AIDS related Cover security personnel in Afghanistan and Iraq Astronaut insurance consider past crashes Extreme sports/hazardous pursuits 7

8 Aviation risks 8

9 Flying when to panic 9

10 Aviation risk by type of aircraft Flat Extra Type of aircraft Fatal accident rate Relative Extra Mort rate per hour flight time 50 hr/yr 100 hr/yr 200 hr/yr 500 hr/yr 800 hr/yr 1000 hr/yr Fixed wing % Glider % Helicopter % Light sport / Lighter than air % Other % Grand Total %

11 Aviation risk by type of aircraft Fatality incidence - private pilots Fixed Wing Helicopter and Gyro Fixed wing is less risky than helicopters Gyrocopters & microlites are more prone to crash Additional mortality for private pilots is high Although credibility is low Civil Aviation Authority has improved standards 11

12 Aviation risks Cover for aviation risk can be significant Expensive hobby, affluent lives One has to be quite selective about these types of risk Relatively small pool Consider the experience & qualification of the pilot Example: instrument rating? Number hours he/she intends to fly Commercial or Private pilot? Where is the pilot flying to? Example: registered or unregistered airstrips, central African countries? Ideally would like to build a psychological profile Example: A pilot with R20m cover is drunk at Sun City and flies his plane into a hill, killing wife and friend. Difficult to assess attitude Consider if a pilot is fit to fly 12

13 Underwriting large covers 13

14 Assessment of large covers Collate information from: Proposal Form Financial / Key Person Questionnaire Financial statements Medical information 14

15 Financial Statements Auditors report Balance sheet (financial position) Income statement (performance over a period) Cash flow statement (cash flow over a period) Statement of changes in equity Directors statement 15

16 Large covers - example In 1996 a doctor applies for cover of R personal cover Close to R20m in current financial terms Application form details Earning R per annum Qualified doctor Husband is owner of policy On face value appears to be reasonable Important to check the evidence for any discrepancies Per financial questionnaire it turns out that she is only a intern Earnings R per annum Why would she need so much cover? What is the true insurable interest? Possibly about R2m 16

17 Large covers A hijacking occurs and the doctor is kidnapped Husband is thrown from the car The husband hires private investigators to find his wife The private investigators including former hit squad CCB operator Slang van Zyl who cooperate with the police 17

18 Large covers The doctor is found murdered in the veld north of Pretoria They establish that the husband paid three men to kidnap and murder his wife so that he can claim the insurance money The husband is psychiatrist Dr Sabadia, a compulsive gambler, serving a life sentence The insurance money is paid out in trust to the children 18

19 Large cover example Male ANB 49 Diamond Mine Cover applied for: Whole Life: R100m contingent liability/surety for loans Two shareholders: Assured 60% Partner 40% Personal income: R1.2m per annum Cover in force: R63m personal cover 19

20 Large cover Diamond mine Diamond mine that was purchased from De Beers Diamonds of R20m have been sold since acquiring the mine Accountant has provided a valuation, based on a geological study, plant and equipment, investments and loans 20

21 Large cover Diamond mine Investments: R7m Liabilities: Bank loans & other creditors: R65m There are also shareholders loans of R20m Value of tailings: Total in-situ value as per geologist report - R757m Estimated recovery expenses as per Directors (80%) - R606m Net value - R151m 21

22 Large cover calculations The banks would have conducted extensive due diligence studies prior to granting the loans - as required by South African law (National Credit Act). Total value of loans (contingent liability): R65m x life assureds shareholding = R40m Shareholders loans: R20m Allow 60% of shareholders loans based on assureds shareholding = R12m Offered total of R Could have considered R Terms subject to only one proposal completing in the market 22

23 Large covers - conclusions Need to establish if there is insurable interest (eg loans, key man insurance) Essential that the underwriter conducts the required research and applies sound underwriting practices. Prevent anti-selection Rules, formulas and guidelines assist in a consistent approach Formulas are only the starting point 23

24 Large covers - conclusions Assess the overall risk profile Consider the medical history in conjunction with the financial underwriting and other aspects Would not want to offer R100m cover to a person with a history of major depression and the company is in financial trouble The company needs to be solvent and ideally profitable Market practice is to offer whole life cover while the loan is a term & decreasing Mismatch but commission driven 24

25 Claim risks 25

26 Functional impairment vs Disability A policyholder has an accident above the knee amputation He meets the functional impairment definition Benefit is payable until NRA However still able to work as a video editor 26

27 Business owner with own occupation Cover is granted to a business owner on an own occupation basis The business owner submits a claim Only at claim stage it is established that he runs two businesses: tow trucking & auto electrician business He is unable to work as a tow truck driver The claim of R2m has to be paid But he can still manage the auto electrician business The policyholder should have been insured on an own/suited occupation basis 27

28 Accidental death? Very high number of accidental deaths Person with a large cover amount phones the insurer to confirm that cover is in place Next day: person hires a rental car and has a head on collision with a truck A policyholder with a history of mental health problems Parks car in motorway and walks in front of a truck Very difficult to prove that such cases are accidents or suicide Are there possible underwriting implications? 28

29 Disability risks ability to anti-select Doctors with disability cover, some with very high covers They have extensive medical knowledge and a network of doctors who are able to support their claim Example: Pregnant dentist who fainted once at work Gynaecologist diagnosed her with a severe cardiac problem and booked the dentist off work for 2 months 29

30 Disability risks ability to anti-select Gynaecologist insisted that the dentist cannot work, drive and must stay at home However, fainting is common during pregnancy RGA refused to pay the claim However the insurer and the CMOs of another two reinsurers decide to give the claimant the benefit of the doubt and paid the claim If the policyholder were a hairdresser/beautician, would the response have been different? 30

31 Tail risks 31

32 Black swans Actuaries mainly work with averages Low probability events are often disregarded However tails risk shouldn t be underestimated Various techniques Simple scenarios tests Doctor age 40 with cover of R100,000 per month Cover escalates at CPI and 5% Expected duration is 5 years Reserve is 100,000 x 12 x 4.85 = R5.8m. If he remains in force until age 70, the cost increases to 100,000 x 12 x 25.8 = R31m or 5.3 times higher than the mean. 32

33 Adverse shocks Catastrophes, bird flu Strikes, civil unrest, wars Economic conditions: low interest rates or negative real rates? Economic downturn: affects disability experience, retrenchment experience Concentration risk within certain group schemes 33

Correlations Correlation: independence => zero correlation But zero correlation does not necessarily imply independence If there is a linear correlation, there is still uncertainty about

34 Correlations Correlation: independence => zero correlation But zero correlation does not necessarily imply independence If there is a linear correlation, there is still uncertainty about the degree of dependence/causality Correlations are distorted by outliers Standard correlation methods struggle with non-linear trends Source: Tail Risk, Systemic Risk & Copulas by A Staudt 34

35 Tail risk Most standard distributions like the normal distribution and gamma distribution underestimate tail risk Consider Extreme Value Theory Generalised Pareto Distribution Copulas: describe dependence between marginal distributions 35

36 Tail risk Empirical data + generalised pareto distribution to reflect tail risk 36

Catastrophe Pricing Model four key variables: Annual number of catastrophes Number of deaths by catastrophe Number of claims by catastrophe SAR distribution Annual Number of catastrophes Poisson

37 Catastrophe Pricing Model four key variables: Annual number of catastrophes Number of deaths by catastrophe Number of claims by catastrophe SAR distribution Annual Number of catastrophes Poisson Process Number of deaths by catastrophe Generalized Pareto Distribution Number of claims by catastrophe Beta Binomial Distribution Input for Monte Carlo scenarios generator Sar Distribution Gamma Distribution

38 Conclusions 38

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40 Almost all consequential events in history come from the unexpected - Nassim Taleb Thank you for you attention 40

Actuarial Society of India

Actuarial Society of India EXAMINATION 30 th October 2006 Subject ST1 Health and Care Insurance Specialist Technical Time allowed: Three hours (14.15* pm 17.30 pm) INSTRUCTIONS TO THE CANDIDATE 1. Enter