Oliver Wyman Modeling Managing and Pricing of Living Benefit Risks May 2014

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2014 Life and Annuity Symposium Session 83 / Virtual Broadcast: This presentation provides an industry overview of practices related to the modeling, pricing and risk management of living benefit risks and qualitatively positions FIA GLWBs (covered by co-speakers in more detail via a case study) relative to other living benefits.

Business Economy & Finance

© 2014 Oliver Wyman
Guillaume Briere-Giroux, FSA, MAAA, CFA
Modeling, Managing and Pricing Living Benefit Risks
Overview of Industry Approaches
2014 Life and Annuity Symposium
Atlanta – May 20, 2014

© 2014 Oliver Wyman 11© 2014 Oliver Wyman
Overview of industry approaches
I. What living benefits?
II. What risks?
III. What scenarios and what value lenses?
IV. Industry modeling practices
After this overview, co-speakers will expand on select modeling, pricing and risk
management issues with a case study focused on a fixed indexed annuity (“FIA”)
with guaranteed living withdrawal benefit (“GLWB”)

© 2014 Oliver Wyman 22© 2014 Oliver Wyman
What living benefits?
Sales data from LIMRA
LowerHigherMarketRisk
Mostly elective
Both elective and non-elective
Non-elective
Insurance risk type
Elective
Size of bubbles represents order
of scale for recent new business
volumes (LTC converted to
single premium equivalent)
Lower HigherInsurance Risk

© 2014 Oliver Wyman 33© 2014 Oliver Wyman
What insurance risks?
High
Low
Product Longevity Base lapse Dynamic lapse
Withdrawals or
annuitization
Morbidity
VA GMAB
VA GLWB
VA GMIB
FIA GLWB*
SPIA
DIA
LTC
*With nursing home benefit
Risk level

© 2014 Oliver Wyman 44© 2014 Oliver Wyman
What market risks?
Product Credit Interest rates Equity Volatility
Fund
correlation /
basis risk
VA GMAB
VA GLWB
VA GMIB
FIA GLWB*
SPIA
DIA
LTC
*With nursing home benefit
High
Low
Risk level

© 2014 Oliver Wyman 55© 2014 Oliver Wyman
What scenarios and what value lenses?
Real World Risk NeutralValue lenses
SimpleComplex
Dynamic policyholder behavior
Static behavior scenarios
None
Behavior “scenarios”
Size of bubbles represents order
of scale for recent new business
volumes (LTC converted to
single premium equivalent)
Sales data from LIMRA
Deterministic+
sensitivities
Stochastic
Nested
stochasticDeterministic
Integrated dynamic behavior scenarios
Economicscenarios

© 2014 Oliver Wyman 66© 2014 Oliver Wyman
Industry modeling practices
Product
Stochastic
equity returns
(RW)
Stochastic
interest rates
(RW)
RN cost of
guarantees
Behavioral
cohorts
Dynamic
behavior
VA GMAB
 ?  
VA GLWB
    
VA GMIB
   ? 
FIA GLWB*
   
SPIA
?
DIA
?
LTC
? ?

© 2014 Oliver Wyman 77© 2014 Oliver Wyman
Key points
1 Market risks impact pricing approaches
2 Accounting and risk management practices drive “scenario layers”
3 Behavior risk drives modeling granularity and complexity
4 Assumption modeling is becoming increasingly sophisticated

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一比一原版(QMUE毕业证书)英国爱丁堡玛格丽特女王大学毕业证文凭如何办理

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Oliver Wyman Modeling Managing and Pricing of Living Benefit Risks May 2014

1. © 2014 Oliver Wyman Guillaume Briere-Giroux, FSA, MAAA, CFA Modeling, Managing and Pricing Living Benefit Risks Overview of Industry Approaches 2014 Life and Annuity Symposium Atlanta – May 20, 2014

2. © 2014 Oliver Wyman 11© 2014 Oliver Wyman Overview of industry approaches I. What living benefits? II. What risks? III. What scenarios and what value lenses? IV. Industry modeling practices After this overview, co-speakers will expand on select modeling, pricing and risk management issues with a case study focused on a fixed indexed annuity (“FIA”) with guaranteed living withdrawal benefit (“GLWB”)

3. © 2014 Oliver Wyman 22© 2014 Oliver Wyman What living benefits? Sales data from LIMRA LowerHigherMarketRisk Mostly elective Both elective and non-elective Non-elective Insurance risk type Elective Size of bubbles represents order of scale for recent new business volumes (LTC converted to single premium equivalent) Lower HigherInsurance Risk

4. © 2014 Oliver Wyman 33© 2014 Oliver Wyman What insurance risks? High Low Product Longevity Base lapse Dynamic lapse Withdrawals or annuitization Morbidity VA GMAB VA GLWB VA GMIB FIA GLWB* SPIA DIA LTC *With nursing home benefit Risk level

5. © 2014 Oliver Wyman 44© 2014 Oliver Wyman What market risks? Product Credit Interest rates Equity Volatility Fund correlation / basis risk VA GMAB VA GLWB VA GMIB FIA GLWB* SPIA DIA LTC *With nursing home benefit High Low Risk level

6. © 2014 Oliver Wyman 55© 2014 Oliver Wyman What scenarios and what value lenses? Real World Risk NeutralValue lenses SimpleComplex Dynamic policyholder behavior Static behavior scenarios None Behavior “scenarios” Size of bubbles represents order of scale for recent new business volumes (LTC converted to single premium equivalent) Sales data from LIMRA Deterministic+ sensitivities Stochastic Nested stochasticDeterministic Integrated dynamic behavior scenarios Economicscenarios

7. © 2014 Oliver Wyman 66© 2014 Oliver Wyman Industry modeling practices Product Stochastic equity returns (RW) Stochastic interest rates (RW) RN cost of guarantees Behavioral cohorts Dynamic behavior VA GMAB  ?   VA GLWB      VA GMIB    ?  FIA GLWB*     SPIA ? DIA ? LTC ? ?

8. © 2014 Oliver Wyman 77© 2014 Oliver Wyman Key points 1 Market risks impact pricing approaches 2 Accounting and risk management practices drive “scenario layers” 3 Behavior risk drives modeling granularity and complexity 4 Assumption modeling is becoming increasingly sophisticated

Oliver Wyman Modeling Managing and Pricing of Living Benefit Risks May 2014

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