Actuarial Risks for Annuities - Considering Dynamic Policyholder Behavior
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1. The document discusses predictive modeling techniques used to study policyholder behavior for annuity products and living benefits. 2. Predictive modeling allows analysis of multiple risk drivers simultaneously and requires less data to converge than traditional methods. It also enables testing how behavior changes over time. 3. For annuity guaranteed lifetime withdrawal benefits, integrated behavioral cohorts are used including "efficient", "partial", "excess", and "waiting" users. Assumptions need to consider lapse and utilization rates holistically. 4. Behavior modeling interacts with economic scenario modeling from simple to complex approaches. Pitfalls to avoid include not fully using data, ignoring key risk drivers, and not accounting for seasoning effects over time.
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Actuarial Risks for Annuities - Considering Dynamic Policyholder Behavior
- 1. © 2015 Oliver Wyman Guillaume Briere-Giroux, FSA, MAAA, CFA Actuarial Risks for Annuities Session 60: Considering Dynamic Policyholder Behavior 2015 Valuation Actuary Symposium Boston – September 1, 2015
- 2. 11© 2015 Oliver Wyman Agenda I. Annuity behavior risks II. Assumption setting practices III. Industry modeling practices IV. Pitfalls to avoid V. Closing thoughts
- 3. 22© 2015 Oliver Wyman Annuity behavior risks High Low Product Longevity Base lapse Dynamic lapse Withdrawals or annuitization Morbidity VA GMAB VA GLWB VA GMIB FIA GLWB1 SPIA DIA LTC2 1) With nursing home benefit 2) LTC shown for comparative purposes only Risk level Annuity behavior risks
- 4. 33© 2015 Oliver Wyman Predictive modeling is increasingly used to study annuity policyholder behavior Product Surrenders Utilization Mortality VA Living Benefits FIA Living Benefits Fixed Deferred Annuities Payout Annuities Source: Oliver Wyman research Assumption setting practices
- 5. 44© 2015 Oliver Wyman Benefits of using predictive modeling 1 Studying behavior “all else equal” by analyzing drivers simultaneously 2 Less data needed to converge relative to traditional analysis 3 Can more easily analyze and interact risk drivers 4 Can more readily test consistency of behavior over time 5 Better ability to put experience in context Assumption setting practices
- 6. 55© 2015 Oliver Wyman Annuity GLWB modeling makes increased use of integrated behavioral cohorts Cohort of GLWB Observed behavior “Efficient” users • Utilize 100% of GLWB maximum income • Strong utilization “feature skew” • Low lapse rate • More efficient dynamic lapses “Partial” users • Utilize less than 100% of GLWB maximum income • Weaker utilization skew • Higher lapse rate than efficient users • Less efficient dynamic lapses “Excess” users • Utilize more than 100% of GLWB maximum income • Very high lapse rates • Least efficient dynamic lapses “Waiting” users • Have not yet utilized • Low lapse rates • Efficient dynamic lapses • Waiting for rollup? This creates four cohorts to model, i.e., a “policyholder behavior scenarios” dimension Assumption setting practices
- 7. 66© 2015 Oliver Wyman It is important to view GLWB lapse and utilization assumptions holistically 1.0% 2.0% 3.0% 1.0% 2.0% 3.0% Lower Higher Lower Higher 1.5% ? ? ? Lower ? ? ? Lower 2.5% ? ? ? ? ? ? 3.5% ? ? ? Higher ? ? ? Higher 1.5% ? ? ? Higher ? ? ? Higher 2.5% ? ? ? ? ? ? 3.5% ? ? ? Lower ? ? ? Lower Higher Lower Higher Lower 16 18 20 InterestRate Index Account Growth Index Account Growth Years until AV Depletion InterestRate % of Deaths before AV Depletion % of Deaths after AV Depletion % of Lapses before Utilization % of Lapses after Utilization Assumption setting practices
- 8. 77© 2015 Oliver Wyman Behavior modeling and interaction with modeling of economic scenarios 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 Industry modeling practices
- 9. 88© 2015 Oliver Wyman Pitfalls to avoid 1 Not fully leveraging experience data 2 Using the wrong risk drivers or ignoring key drivers 3 Not performing analysis “all else equal” 4 Not testing the consistency of behavior over time 5 Extrapolating out of context 6 Ignoring seasoning and survivorship bias 7 Not thinking about assumptions holistically 8 Not thinking about broader dynamics Pitfalls to avoid
- 10. 99© 2015 Oliver Wyman Closing thoughts 1 Humans are humans, but… 2 Build the right team 3 Never stop challenging Closing thoughts