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Transcript of Post-Retirement: Models, Markets and Products - Models Markets... · Post-Retirement: Models,...
© Oliver Wyman
Post-Retirement: Models, Markets and Products
11th May 2015
Phil Joubert [email protected]
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Human mortality
1 What does human mortality look like currently?
2 How has it changed in the past?
3 How might it develop in the future?
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Infant mortality Linear increase in log hazard rates with age
Males Females 81.17 86.75
Life expectancy at birth1:
1 Calculated from the same series of tables
1 Current human mortality Lets take Hong Kong as an example
Suggests a survivor function “something like”:
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Historic development of human mortality Steady decrease of mortality rates at all ages
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For 160 years, best-performance life expectancy has steadily increased by a quarter of a year per year, an extraordinary constancy of human achievement. Mortality experts have repeatedly asserted that life expectancy is close to an ultimate ceiling; these experts have repeatedly been proven wrong. Jim Oeppen & James W. Vaupel From “Broken Limits to Life Expectancy” (Science, May 2002)
Historic development of human mortality Steady increase of life expectancy
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Historic development of human mortality Period vs. Cohort
Expectancies based on longitudinal mortality will underestimate the true picture
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Historic development of human mortality Period vs. Cohort
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The actual mortality experienced by an individual is a concave to a straight line
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M1 Lee-Carter Explanatory models developed by demographers
M2 Lee-Carter + cohort effect
M3 Age, Period, Cohort
M4 CMI spline model Interpolation model developed by UK actuaries (not stochastic)
M5 CBD log-linear in age Explanatory models developed by actuaries M6 M5 + cohort
M7 M6 + quadratic term in age
M8 M5 + decreasing cohort effect
Future human mortality Stochastic models of future mortality
Cairns, Blake & Dowd define a “standard suite” of models:
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Future human mortality Structural models of future mortality
As with credit models, I would expect stochastic models to outperform structural ones
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Future human mortality Projecting future mortality
κ1
κ2
A: Fit model to historic data
B: Fit a n-dim random walk to
parameters
C: Run MC projections of random walk
D: Reconstruct qx and life tables
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You can now estimate the mean values of future mortality, and the risk associated
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3 Future human mortality Despite all this data and all these models, there is a long tradition of under-estimating future improvements in longevity
“The main source of longevity risk is (the) discrepancy between expected and actual life spans, which have been large and one-sided; forecasters, regardless of the techniques they use, have consistently underestimated how long people will live.” (IMF, Global Financial Stability Report 2012)
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The need for retirement products
1 Demographics and the changing nature of aging
2 Mis-estimation of life expectancy by retirees
3 Large variance of life spans after retirement
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Source: United Nations, World Population Prospects: The 2012 Revision
0
10
20
30
40
50
1950 1975 2000 2025 2050 2075 2100
China
Eastern Asia
Japan
Western Europe
US
Popu
latio
n 60
and
ove
r (%
)
Demographics and the changing nature of aging Asian populations are aging rapidly
Population 60 and over around the world
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Source: United Nations Department of Economic and Social Affairs, 2012
By 2050, one in four Asians will have retired.
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101520253035404550
1950 1970 1990 2000 2005 2010 2015 2020 2030 2050 2075 2100
Ratio
(%)
Year
Asia's Old-age Dependency Ratio (1950- 2100)
Demographics and the changing nature of aging This threatens traditional retirement support systems
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Present morbidity
Extension of Longevity
Compression of Morbidity AND Extension of Longevity
Morbidity Death
75y 55y
80y 55y
75y 60y
80y 70y
Compression of morbidity
Demographics and the changing nature of aging The main goal in health is compression of Morbidity AND Extension of Longevity
Are we achieving this goal?
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Demographics and the changing nature of aging Extreme old age presents unique challenges and expenses
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2 Mis-estimation of life expectancy by retirees Studies consistently show that people tend to under-estimate life spans
This creates a genuine risk that they will outlive their savings – but also presents a communication problem
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131415161718192021
1988 1992 1996 2000 2004 2008
Expe
cted
num
ber o
f yea
rs
of li
fe re
mai
ning
Male at age 65 Female at age 65
Survival probabilities of 65 year olds Segmented by gender, 2010
Men’s and Women’s longevity risk over time 1988–2008
17.3
20.0
0%10%20%30%40%50%60%70%80%90%
100%
65 70 75 80 85 90 95 100Female Male
18%
16%
13%
9%
9%
7%
2%
Biggest concern about retirement Having enough money
Staying productive and useful Providing for your own/spouse's/partner's long-term care needs
Outliving retirement money Being able to afford health care in your retirement years
Having to work full or part-time to live comfortably in retirement Having a comprehensive financial plan for retirement
Source: GfK custom research with MetLife, HLTC4
3 Large variance of life spans after retirement Even if they had perfect life expectancy forecasts, pensioner still have a large chance of outliving their savings
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The supply side: reasons for insurers to get involved
Customers’ needs change as they age and transition into retirement: • They no longer need traditional protection
products
• They do need longevity and care insurance
• They remain the wealthiest segment of the population in most economies
Pensioners are a large and attractive group of customers
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LIF/RIF with institutional pooled funds
GMWB products
“Ideal” product combination
Life annuities, deferred annuities
Fixed term annuities
The retirement trilemma Low cost, high return and flexible products…
Access to capital
Protection from risk
Good returns
… delivered by the Easter Bunny
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Innovation in post-retirement products – the buzz New products launched every week, much innovation expected in the UK
"iPipeline Launches New Retirement Planning Solution”
“Allianz Life introduces two new annuity investment options”
“Cooking up a storm with a new recipe for retirement income solutions”
“Canada Life […] will launch three retirement income products…”
“New York Life debuted its Clear Income Fixed Annuity”
“Voya Financial unveils new deferred index-linked variable annuity”
“New PIMCO head convenes task force to tackle retirement income solutions”
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Focus on distribution, communication and lowering costs, rather than innovation?
Innovation in post-retirement products – the reality The current industry suite
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Offer components – examples
Offer components – considerations Example players
• Home assistance • Travel • Lifestyle
• Sourcing service providers
• Economics/ Payment model
• Role in customer acquisition/retention
• Banking • Direct share trading • Own portfolio mgt. • Home equity release • Planning, advice and
education
• De-siloing FS conglomerates
• Single customer view • Integration with
advice model
• Lifetime annuities (immediate and deferred)
• Variable annuities • Enhanced annuities
• Risk appetite • Balance sheet
strength • Pricing and
source of value
• Income funds • Lifecycle funds • Risk-managed funds
(CPPI, managed vol etc.)
• Breadth of fund range vs. focus of offer
• Price vs. value • Income vs. growth
Fund products
Balance sheet products
Personal financial management
Retirement lifestyle services
Expanding scope of retirement offer
The current suite, seen from a different angle Insurers must use their balance sheets to differentiate themselves
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• Tax treatment
• Capital requirements
• Risk of mis-selling Tax & Regulation
• Long bond market
• Illiquids
• Equity index Futures
• Swaps
• Reinsurance Hedging Markets
• Risk Appetite
• Risk Management
• Systems & Processes
Internal Capability
Branding & Distribution
• Access to retirees
• Trusted name
Building a post-retirement business Internal and external factors play a part
Customer
• Segmentation
• Channel
• Analytics
• Customer experience
Alternatives • Fund
managers
• Banks
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• The UK was the world’s largest annuity market
• Annuitisation at retirement was forced by law
• Most retirees rolled their savings over into the default annuity offered by the same provider who managed their accumulation phase
• The market was ripe for disruption
Case Study: UK enhanced annuity and equity release players
• Several niche players started offering annuities at favourable rates to smokers and those in poor health
• They backed the annuity liabilities by a combination of corporate bonds and equity release mortgages
• Hannover Re supported them with reinsurance and technical knowhow
• Just Retirement grew its balance sheet from zero to over £10Bn in under ten years
• Together the EA / ER players were capturing significant chunks of the open-market annuity flow
• Several were backed by PE money and floated / traded in 2013
• Then came the budget of 2014…
The initial situation The disrupting model The result
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Guarantee types
Move to CPPI style Prudential HDL John Hancock
Innovative underlyings
Capped vol Target vol Internal fund hedging Restricted fund choice
AXA GMIBs Met GMIB Max Transamerica
Activity based adjustments
Shift to a lower risk fund strategy when policyholder triggers a higher cost event
Ameriprise SS
Benefit innovations
Variable fees …
SunAmerica benefits
• Risk management options are available in the various markets (active futures exchange, liquid swaps market)
• Product launches outside the US have failed – customer interest has been muted, perhaps due to the complicated nature of the products
• Japan has a large market for VA’s, and products have recently been launched in Korea
1. VA’s represent an attractive client proposition
2. But have not been great for providers
3. Modern designs try to mitigate the risks, but retain the attractiveness
4. Things to consider in the Asian context:
Case Study: Variable Annuities
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Summary
A Longevity risk – can be modelled
B Insurance post-retirement – mutual interest?
C Products – packaging, manufacturing and delivery
? Questions or comments?
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Speaker Bio
11 May 2015
Phil Joubert is a principal in the Financial Services practice of Oliver Wyman, based in Hong Kong Office. Phil has fifteen years of experience in the financial services industry, having worked in areas as diverse as actuarial consulting and derivatives trading. He has worked in both Europe and Asia-Pac for a variety of insurers, banks and software houses, and specialises in risk and capital modelling and systems design Recent experience
• Regulatory capital model design and implementation for several insurers in Europe • Economic capital implementation for leading pan-Asian insurance group • Derivatives trading and market risk management • Capital aggregation systems design for leading ESG provider • Actuarial automation implementation project for specialist life insurer
Phil holds an MSc in Finance & Mathematics from Imperial College and is a Fellow of the Faculty of Actuaries. He joined Oliver Wyman in 2014, having spent several years as an independent actuarial consultant. Previously he worked at Deutsche Bank and Natixis as a trader and he started his career with Deloitte Actuarial.
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