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ILA Track > ILA-LPM B > Flashcards

ILA-LPM B Flashcards

1
Q

List the 6 steps to establish experience assumptions

A
  1. Identify assumptions needed
  2. Determine structure of each assumption
  3. Analysis experience & trends
  4. Review assumptions for reasonableness, consistency
  5. Document assumptions
  6. Monitor experience & update assumptions

I DARDM

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2
Q

3 types of primary assumptions needed for an experience study

A
  1. obligation
  2. asset
  3. scenario
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3
Q

Describe primary types of assumptions needed for an experience study:

  • Obligation assumptions
A
  • LIABILITY!
  • mortality
  • lapse
  • expense
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4
Q

Describe primary types of assumptions needed for an experience study:

  • Asset assumptions
A
  • investment income rate
  • capital gains rate
  • defaults
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5
Q

Describe primary types of assumptions needed for an experience study:

  • Scenario assumptions
A
  • deterministic vs. stochastic interest rates
  • sensitivity testing
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6
Q

How do you determine experience classes for an experience study?

A
  • groups of policies w/ same assumption
  • similar type, structure, marketing objectives
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7
Q

What are the key principles when deciding complexity?

A
  • reflect differences in actual experience
  • use objective definitions
  • be practical and cost effective
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8
Q

List 4 considerations when analyzing experience and trends for an experience study

A
  1. evaluate credibility
  2. evaluate quality of data
  3. actual vs similar
  4. reflect trends
  5. reflect company and external factors
  6. sensitivity test the assumptions
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9
Q

List considerations when analyzing experience and trends for an experience study:

  • Evaluate credibility
A
  • quantity of data
  • homogeneity
  • reasonableness
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10
Q

List considerations when analyzing experience and trends for an experience study:

  • Evaluate quality of data
A
  • Alternative sources?
  • Appropriate? Comprehensive enough?
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11
Q

List considerations when analyzing experience and trends for an experience study:

  • Actual vs. similar experience
A
  • Use actual if available and credible
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12
Q

List considerations when analyzing experience and trends for an experience study:

  • Reflect trends
A
  • Example: mortality improvement
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13
Q

List considerations when analyzing experience and trends for an experience study:

  • Reflect company and external factors
A
  • underwriting
  • investment policy
  • other business practices
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14
Q

Validation checks to review assumptions for reasonableness and consistency in an experience study

A
  • static (starting reserves)
  • dynamic (projected reserves)
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15
Q

Consisitency checks to review assumptions for reasonableness and consistency in an experience study

A
  • inflation consistent w/ investment earnings
  • mortality anti-selection and lapses
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16
Q

How should assumptions be documented after an experience study?

A
  • Actual assumptions: value, applicable class
  • Data: source, values, any concerns, adjustments
  • Methods for development: e.g., credibility
  • How to use:
    • pricing vs. CFT
    • sensitivity testing
    • regulatory requirements
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17
Q

3 key steps to determine a mortality assumption from an experience study

A
  1. determine structure
  2. analyze experience
  3. monitor experience
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18
Q

How to analyze experience for a mortality assumption

A
  • mortality study (e.g. 5-yr CY study)
  • develop expected mortality rates
  • assess credibility
  • adjust mortality rates
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19
Q

What are some of the ways that mortality rates are adjusted?

A
  • trends (improvement, etc.)
  • anti-selection = conservation of deaths
  • blended w/ similar/industry (low cred)
  • adjust for UW, distribution, market, etc
  • blend male/female rates into aggregrate
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20
Q

What is the structure of a mortality assumption

A
  • select and ultimate common
  • mortality improvement
  • ALB vs. ANB
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21
Q

Possible variations in the structure of a mortality assumption

A
  • risk class
  • selection process (type of UW)
  • size of policy (bigger face ⇒ lower mortality)
  • market method (direct, agent, etc)
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22
Q

ANB

A
  • age nearest birthday
  • qANB(x) = 0.5 x [qALB(x) + qALB(x-1)]
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23
Q

ALB

A
  • age last birthday
  • qALB(x) = 0.5 x [qANB(x) + qANB(x+1)]
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24
Q

List and decribe the 2 main types of mortality studies

A
  • CY
    • activity for single CY
    • account for new policies, WDs, and death
  • Anniversary-to-anniversary
    • simpler; coincides w/ policy year
    • issue year/duration basis
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25
Q

Which risk classes have separate mortality studies?

A
  1. non-routine UW
  2. conversions ⇒ usually higher mortality
  3. sub-standards
  4. non-forfeiture (ETI, RPU)
  5. multiple-life
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26
Q

Calendar year exposure equality

A

A + N = W + D + B

of lives…

A = beginning of year

N = enter during year

W = lapse during year

D = die during year

B = alive at end of year

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27
Q

What are the two ways that the balducci assumption can be used to calculate a mortality rate for a mortality study

A
  • Policy level - sum up individual exposures
  • Aggregrate - assume new issues and WDs occur mid-year (s=r=0.5)
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28
Q

Credibility considerations for a mortality study

A
  • multi-year studies - better credibility
    • 5-yrs good; too many years hide trend
  • lower for amount-based studies
  • A/E ratios - credibility & tracking trends
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29
Q

Describe conservation of deaths principle

A

Total mortality = weighted average of:

  1. mortality of “select lives” that lapse in duration r
  2. mortality of lives that stay w/ original policy

Key pricing concept: If you don’t account for selective lapsation, you will under-price the insurance product

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30
Q

anti-selections’s effect on mortality

A
  • healthy lives more likely to lapse than unhealthy - especially w/ high renewal premiums
  • mortality of those left begins to increase by “the conservation of deaths” principle
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31
Q

List at least 4 variations in lapse assumption structure

A
  1. product design
  2. distribution channel
  3. policy size
  4. premium mode
  5. product type
  6. conservation program effectiveness
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32
Q

Describe variations in lapse assumption structure (i.e. ways that lapses vary):

  • Product design
A
  • term: high (shock) lapses when ART period begins
  • annuities: high (shock) lapses after SC period
  • permanent insurance: high 1st year lapses
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33
Q

Describe variations in lapse assumption structure (i.e. ways that lapses vary):

  • Premium mode
A
  • lapses generally occur on premium due dates
  • flexible premium: assume uniform monthly
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34
Q

Describe variations in lapse assumption structure (i.e. ways that lapses vary):

  • Policy size
A
  • small policies: high early lapses
  • large policies: high later lapses
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35
Q

Describe variations in lapse assumption structure (i.e. ways that lapses vary):

  • Distribution channel
A
  • brokerage: higher lapses
  • agency: higher lapses if agent quality is poor
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36
Q

Describe variations in lapse assumption structure (i.e. ways that lapses vary):

  • Product type
A
  • Deferred annuities: more sensitive to lapse rates than life insurance
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37
Q

Exposure for a lapse study

A
  • same as mort study - death and lapses trade places
  • lapses get full year of exposure

w(x) = W / (A + (1 - r)N - (1 - s)D)

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38
Q

List specific types of lapses

A
  • failure to pay premium (term)
  • full/ partial cash surrender
  • policy loan > CSV
  • nonforfeiture transfers - ETI, RPU
  • term conversions
  • deviations in actual prem as a % of target prem (UL)
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39
Q

Describe the following aspects of interest rate assumption structure.

  • Deterministic vs. Stochastic
A

Investment Assets:

  • deterministic
    • portfolio average
    • investment generation
  • stochastic
    • useful for more important risk
    • rates vary w/ time, asset class, quality, & credit risk
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40
Q

Describe the following aspects of interest rate assumption structure.

  • Policy Loans
A
  • modeled as assets or negative liabilities
  • net of policy loan expenses
  • utilization rate
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41
Q

Formula for determining interest rate on a book value basis

A

I = Ai + (B - A - I)(i/2)

i = 2I / (A + B - I)

A = BoY

B = EoY

C = net CFs (assume mid-year)

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42
Q

Formula for determining interest rates on a market value basis

A

r = (B - A - C) / (A + C/2)

If done daily, set C = 0

A = BoY

B = EoY

C = net CFs (assume mid-year)

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43
Q

Direct vs. indirect expenses

A
  • Direct (vary w/ sales)
    • commissions
    • premium taxes
    • UW
  • Indirect (express as per policy, % of premium, or per unit)
    • OH
  • Both
    • maintenance
    • acquisition
    • entering new LOB
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44
Q

Describe how exposure is determined in an expense study

A
  1. Goal: develop a policy count base for per policy expenses
  2. How: Count the number of policy years that start in the study’s calendar year
    • BOYs “crossed” in CY = A + N -W/2
    • Mid-years “crossed” in CY = A + N/2 - W/2
  • Exposure count
    • (A + B + N)/2 for beginning of year expenses
    • (A + B)/2 for mid-year expenses
  • assumes WDs & new issues occur mid-year
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45
Q

4 methods for allocating expenses

A
  1. transaction count (# of premium payments)
  2. transfer costs (employee benefit cost per employee)
  3. employee time spent
  4. index-based allocation (policy count or premium)
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46
Q

Define “cell” in terms of an experience study

A

combinations of data dimensioned by issue age, sex, smoker/nonsmoker, policy year, etc.

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47
Q

Define “rate types” in terms of an experience study:

  • decrement rates vs. utilization rates
A
  • decrement = probabilities from 0 to 1
    • mortality, morbidity, lapse, etc.
  • utilization = NOT probabilities (can exceed 1)
    • WD, option election rates, etc.
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48
Q

Decribe amount-weighted studies, for grouped amount weights

A
  • lx terms become lx x avg DB in force
  • dx terms become dx z avg DB paid on death
  • wx terms become wx x avg DB on withdrawn policies
  • sum amount exposures and calculate qx’s in the usual way
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49
Q

Describe amount-weighted studies, for individual amount weights

A
  • multiply each life year exposure by the policy’s DB
  • sum amount exposures and calculate qx’s in usual way
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50
Q

Describe the relationship between amounted-weighted q and life-weighted q

A

Amount-weighted q will be > life-weighted q if average DB paid on death > average DB in force

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51
Q

Uses of an A/E analysis

A
  • compare actual mortality/lapse to expected
  • develop best estimate assumptions as a multple of expected amounts
    • Best estimate rate for age x = (A/E) x qxe
  • valuation, risk management, financial planning, etc.
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52
Q

Frequency and severity formulas

A
  • fx = nx/Ex = avg claim frequency*
  • sx = Cx/nx = avg claim amount*
  • nx = # of claims incurred at age x
  • Cx = total claim amount incurred at age x
  • Ex = central exposure (or initial)
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53
Q

Experience study calculations:

  • Average withdrawal taken as a % of the max withdrawal formula
A
  • ux = sx/Mx*
  • Mx* = max w/d allowed
  • sx *= average size (severity) of w/d
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54
Q

Experience study calculations:

  • Average size (severity) of withdrawals taken formula
A
  • sx = Wx/nx*
  • Wx* = total actual w/d amount
  • nx* = # of contracts who took a w/d
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55
Q

Experience study calculations:

  • Withdrawal frequency formula
A
  • fx = nx/Ex*
  • Ex* = count-based exposure (deaths and lapses)
  • nx* = # of contracts who took a w/d
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56
Q

Experience study calculations:

  • Count-based exposure (excludes deaths and lapses)
A
  • Ex = lx - dx - wx*
  • wx *= # of lapses
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57
Q

Experience study calculations:

  • Distortions caused by amount-weighted calculations
A
  • large amounts can distort numerator
    • not easy to fix (cap max amount allowed in study)
  • risks/behaviors can differ significantly by amount
    • small policies have less commitment
    • large policies used to defraud
    • solution: add band size to cell
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58
Q

Experience study calculations:

  • List advantages of mutli-year studies
A
  • higher credibility
  • less distortion cause by reporting lags
  • populate many more cells
  • allow study of trends for study-year/CY
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59
Q

Experience study calculations:

  • 4 examples of reporting lags
A
  • death claims - not reported for months
  • LTCI claims not reported til after EP
  • “shoe-boxing” - lag between direct insurers and reinsurers
  • life annuities sometimes continue paying after death
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60
Q

Experience study calculations:

  • 2 possible solutions to reporting lags
A
  • wait a few months after study period before gathering data
  • IBNR claim estimates from previous studies
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61
Q

Experience study calculations:

  • 3 examples of non-uniform events
A
  1. lapses often vary by month
  2. prorated mortality rates tend to underestimate deaths early in year at old ages
  3. LTCI claims immediately following claim
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62
Q

Experience study calculations:

  • 3 ways to compensate for non-uniform distributions
A
  1. shorter interval
  2. adjustment factors
  3. constant force mortality for old ages
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63
Q

Considerations for inclusion of partial policy years in decrement study

A
  • ok when evenly distributed over year
    • common for biologically-driven rates (mortality, morbidity, etc.)
    • can allocate PYs between CYs
  • Otherwise, partial policy years will distort results
    • behavioral rates not usually evently distributed
      • lapse, withdrawal, option election, etc.
    • do not allocate PYs between CYs
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64
Q

Describe the distributed exposure method

A
  • assumes decrement is uniformly distributed
  • allocates exposure/decrements by both PY & CY
  • produces same total PY exposure as annual method, but CY allocation differs
  • exposure missed by intial partial year (so can extend past study period)
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65
Q

Describe the annual exposure method

A
  • consistent with Balducci
  • exposes decrements to end of next anniversary (even beyond study period)
  • disadvantage: overstates rates in first partial year; understates rates in final partial
  • expsoure extended beyond study period:
    • b/c rate is probability
    • exposure missed by intial partial year
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66
Q

Algorithm for calculating distributed exposure

A
  1. calculate annual exposure
  2. 1st half exposure = PY t exposure in CY
    • deaths/surviving policies from anniversary to end of CY
    • other decrements from anniversary to decrement date
  3. 2nd half exposure = total - 1st half exposure
    • allocated to next CY
  • In plain English: switch the partial year formulas in the annual exposure method*
  • Move final partial year deaths to next study period’s first partial year
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67
Q

product-related considerations that may affect experience study calculations

A
  • deaths in premium grace period (30-60 days)
  • compromised and denied claims
  • study cariables changing over time
  • reinsured amounts
  • substandard and uninsurable lives
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68
Q

Describe product-related considerations that may affect experience study calculations:

  • Deaths in premium grace period (30–60 days)
A
  • actual DB paid is net of overdue premiums, but use full DB in mortality study
  • expose lapses to beginning of grace period
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69
Q

Describe product-related considerations that may affect experience study calculations:

  • Study variables changing over time
A
  • admin system changes
  • may change plan codes or billing frequencies (distorts exposure)
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70
Q

Describe product-related considerations that may affect experience study calculations:

  • Compromised and denied claims
A
  • don’t count as death claims in mortality study
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71
Q

Describe product-related considerations that may affect experience study calculations:

  • Reinsured amounts
A
  • can study amounts net of reinsurance if material enough
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72
Q

Describe product-related considerations that may affect experience study calculations:

  • Substandard and uninsurable lives
A
  • typically excluded
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73
Q

List at least 4 claim characteristics that are shared by LTCI and DI products

A
  1. multiple payments that can last months or years
  2. usually paid monthly
  3. EP before claim payments start (up to 1 year)
  4. usually limited in some way (e.g. max age for DI, max total benefits, etc.)
  5. paid when insured meets certain conditions
  6. claims stop when insured recovers, but can start again
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74
Q

Describe the following types of morbidity studies for LTCI and DI:

  • Benefit utilization rate studies
A

(LTCI only)

Benefit Utilization Rate = Actual Benefits / Max Benefits

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75
Q

Describe the following types of morbidity studies for LTCI and DI:

  • Claim termination studies
A
  • separate studies for recovery and death
  • DI claims terminate when proof of diability not submitted
  • LTC claims terminate when requests for reimbursement stops
  • termination rates are highest and most volatile for new claims
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76
Q

Describe the following types of morbidity studies for LTCI and DI:

  • Claim severity studies
A

Average length of claim = total months of claims/# of claims

Average cost per claim = total claims paid/# of claims

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77
Q

Describe the following types of morbidity studies for LTCI and DI:

  • Claim incidence studies
A
  • claim incidence rates = annual probabilities
  • usually done on “all claims”
  • usually based on annual exposure method
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78
Q

List 4 DI-specific considerations for experience studies

A
  1. EPs - claims are usually reported after EP
  2. partial DI benefits - reduces avg cost per claim
  3. recovery followed by relapse within 6 months
  4. claim settlements - lump sum paid in lieu of future monthly
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79
Q

List 5 LTCI-specific considerations for experience studies

A
  1. EP - 30, 60, 90, 180, and 365
  2. some benefits indexed to inflation
  3. mortality rates do not follow any established table
  4. diagnosis - must capture (drives claim length)
  5. claim data not always organized - requires work
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80
Q

Experience study considerations for deferred annuities

A
  • deferred annuity utilization rates
    • GMxBs: compare benefit paid to the amount payable in absence of GMxB
  • contract year data challenges
    • data usually monthly
    • common approach: convert account balances to contract year basis using a 13-month average
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81
Q

Describe experience study considerations for payout annuities:

  • Structured settlements
A
  • from injury lawsuits
  • one of more lump sums (not lifetime payouts)
  • mortality higher than immediate annuities
  • amount exposure - base on reserves or premium paid
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82
Q

Describe experience study considerations for payout annuities:

  • immediate annuities
A
  • self-selection - people buy IAs when they think they are healthier than average
  • choice of immediate annuity may indicate health
  • larger IAs tend to have lower mortality
  • amount exposure should be based on monthly payment, not reserves
  • mortality improvement is a key consideration
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83
Q

6 major steps in table development

A
  1. develop table
  2. identify table dimensions (exploration techniques)
  3. populate table (graduation/interpolation/modeling)
  4. extend and project rates
  5. review and adjust rates
  6. finalize
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84
Q

List 4 preliminary activities of data development and analysis

A
  1. review previous studies
  2. clarify puprose of table
  3. ensure confidentiality of each contributor’s data
  4. review available data (homogeneity, credibility, etc.)
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85
Q

Define and describe data call process

A
  • data call: formal request for data from contributors
  • simpler - more successful
  • document what is needed for each data item (columns)
  • define data structure (e.g. relational database)
  • create detail records (rows)
  • document all table relationships
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86
Q

List 6 common data challenges for table development

A
  1. incomplete data
  2. terminology may vary by contributor
  3. data may not arrive on time
  4. wrong or improperly formatted data
  5. time lags in reporting
  6. lack of resources to transform data
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87
Q

List 6 components in an experience study calculation

A
  1. study anniversary date - bday, policy anniversary, etc.
  2. age basis (ANB, ALB, etc)
  3. experience study summary records
  4. count-based and amount-based rates (qx’s, etc.)
  5. expected results (A/E ratios)
  6. summary results for each record (event counts and amounts)
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88
Q

List 3 ways to address distortion caused by having mix of age bases in experience data

A
  1. assume all ages based on most common method
  2. recalculate on common basis for each record
  3. weighted-average age
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89
Q

List 8 steps in data analysis process (table development)

A
  1. acquire data
  2. data validation, preliminary exploration, outlier analysis
  3. data visualization and preparation
  4. analytical approach: exploratory vs. advance analytics
  5. model creation and assessment
    • select model(s) w/ explanatory ability, predictive power, and implementation ease
  6. select final model
  7. minimize table dimensions
  8. replacing grids w/ factors if possible

THINK EXAM PA!

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90
Q

List the 3 steps in the modeling process, w/ in the data analysis process

A

The modeling process:

  1. model fitting: select variables w/ lowest p-value
  2. avoid confounding
  3. create transformed variables, functions, interaction terms, or stratification

AGAIN - THINK PA!

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91
Q

Describe the purpose of graduation

A
  • Graduation is a mathematical process that smooths an array of rates
    • assumes “true” rates follow continuous curve
    • required when rates don’t come from model
    • can still be used on modeled rates
    • goal: balance fit and smoothness
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92
Q

List properties of a preferred graduation method

A
  • produces same total events as ungraduated
  • parameters control amount of smoothness
    • input fit, table fit, smoothness
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93
Q

Describe credibility and data grouping considerations when graduating rates for an experience table

A
  • non-credible (“incredible”) rates distort graduated rates
  • smoothness calc weights credible and non-credible rates equally
    • biggest problem: very young and very old ages
  • increasing credibility comes at cost of graduation
    • grouping rates by age (e.g. quinquennial)
    • grouping data by calendar or policy years
  • possible solutions: use exposure-weighted averages for graduation
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94
Q

Describe the 4 steps of the graduation process

A
  1. Collect and populate graduation input
  2. Review and adjust input as needed
  3. Run graduation process for 3 parameters: input fit, table fit, smoothness
    • Set parameter value and run the graduation algorithm
    • Review/graph/evaluate results
    • Repeat until satisfied with parameter
  4. Run a final graduation using the best fit and smoothness parameters
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95
Q

3 ways to approximate total variance for all lives (from least refined to most)

A
  1. overall average size
  2. average size for lives contributing to qr
  3. allocate average size between 2 most common sizes in size groups (can be used to develop factors for 1 & 2)
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96
Q

Describe common interpolation methods for table development

A
  • 1-dimensional methods
    • linear
    • cubic spline
    • log-linear or log-cubic (good for mort)
  • 2-dimensional methods
    • bilinear
    • bicubic (smoothest)
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97
Q

Describe 3 methods for extending rates in a table

A

Often necessary for very young or old ages where credibility is low

  1. use rates from existing credible table
    • use slope as guid
    • grade from study’s rates to existing table’s rates
  2. other data sources
    • SSA for old ages
  3. formulas
    • should reproduce rates for nearby credible ages
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98
Q

Provide 5 reasons why initial rates developed for table may be deficient

A
  1. nonsensical (mort rate > 1)
  2. data had poor credibility
  3. nonsensical pattern
  4. suspicious differences in arrays of rates
  5. very wide CIs
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99
Q

List the 5 componencts of enforcement reviews

A
  1. define relationships to be enforced
  2. define when relationships will be checked
  3. create spreadsheets to check relationships (automation)
  4. check S&U mort rates
  5. adjust rates to enforce relationships
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100
Q

Describe 4 considerations for projecting future rates

A
  1. historical data - consistent population
  2. mortality trends - difficult to estimate
  3. connect cause and effect
  4. different types of projections
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101
Q

3 different types of projections of future rates

A
  1. mid-point of experience data to final table effective date
  2. beyond effective date at single rate of improvement
  3. beyond effective date at a varying rate of improvement
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102
Q

Describe items to consider when assessing financial impact of a table

A
  • reserves and nonforfeiture values
    • prescribed industry tables affect prescribed stat reserves and nonforfeiture values
    • PBR can be impacted by internally developed rates
    • impact can vary a lot from company to company
  • premiums and PH dividends
    • could be impacted by new industry or internally-developed tables
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103
Q

Describe 5 ways to create additional tables from final table rates

A
  1. w/ or w/ out projected trend factors
  2. w/ or w/ out valuation loading factors
  3. multiple age definitions (ANB, ALB, etc.)
  4. unisex
  5. relative risk versions for UW purposes
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104
Q

Describe the 4 major steps in valuation loading process for the commissions’ valuation table (CVT)

A
  1. develop experience table - soley on experience data
  2. develop valuation basic table (VBT)
  3. apply loading factors to experience table to create a loaded experience table
  4. create CVT (used for stat reserves)
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105
Q

Describe how to calculate a credibility-weighted A/E ratio

A
  • for both LFM & Buhlmann, credibility estimate:

Estimated Company A/E = Z x (Company A/E) + (1 - Z) x (Overall A/E)

  • Z = credibility factor ranging from 0 to 1
    • Z = 1 means company experience fully credible
    • Calc for Z different under LFM and Buhlmann
  • overall A/E ratio based on average of all companies in study
    • lower Z is, more weight placed on overall experience
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106
Q

Describe limited fluctuation credibility method

A
  • based on CI and only uses data from particular company being studied to determine the credibility factor
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107
Q

Describe properties of Bühlmann Empirical Bayesian Method

A
  • based on empirical application of Bayesian stats
  • starts w/ initial or prior distribution based on past data, professional experience, or option
  • observed results used to formulate predictive or posterior distribution
  • considers variance between insurers, not just insurer specific like LFM
  • Z calc more complex
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108
Q

Compare LFM and Buhlmann credibility methods

A
  • Buhlmann considers 2 sources of variance, LFM only 1st:
    1. company-specific
    2. among companies
  • LFM only needs data from company studied (simpler)
  • Both:
    • calculate estimated company A/E
    • assume constant overall A/E ratio
    • additional adjustments maybe needed based on actuarial judgment
  • Comparison for 10 insurers surveyed:
    • count-based A/E estimates similar
    • amount-based A/E estimates differed more
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109
Q

What are advantages of traditional approach to experience studies

A
  1. Commonly accepted/well established
  2. easy to produce results
  3. management familiar with ⇒ can apply to management decisions
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110
Q

List 7 key steps in predictive analytics project

A
  1. project scope
  2. data collection and validation (MOST IMPORTANT)
  3. initial factor anlysis
  4. model building
  5. model validation
  6. final calibration
  7. implementation
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111
Q

List the key steps in a predictive analytics project:

  • Data Collection and Validation (most critical step)
A
  • clean data and consider variables that may not be obvious
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112
Q

List the key steps in a predictive analytics project:

  • Model Building
A
  • multiplicative or additive
  • statistical tests, business knowledge, and interactions to include
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113
Q

List the key steps in a predictive analytics project:

  • Model Validation
A
  • compare A/E ratios
  • use hold-out samples
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114
Q

List the key steps in a predictive analytics project:

  • Initial Factor Analysis
A
  • do univariate analysis
  • then remove highly correlated variables
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115
Q

List the key steps in a predictive analytics project:

  • Final Calibration
A
  • refit model using all data and make final adjustments
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116
Q

List the key steps in a predictive analytics project:

  • Implementation
A
  • use model results to set assumptions, do underwriting, etc.
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117
Q

List the key steps in a predictive analytics project:

  • Project Scope
A
  • (target variable, LOB, timeline, resources)
  • Assemble team of technical and business experts
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118
Q

How can UW type and geo-demographic variables can aid UW?

A

Traditional techniques don’t use these but predictive analytics could. . .

  • UW type
    • could use process results (BP, LDL, BMI)
    • limitation: data availability
  • geo-demographic variables
    • categorize socio-economic composition of a particular zip code
    • apply multipliers that vary by face amount, duration, or distribution channel
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119
Q

List general advantages of predictive analytics for experience studies

A
  1. Interactions of factors better understood
  2. better credibility for specific factor combinations
  3. standardizes all factors ⇒ isolates “true effect” of each
  4. backed up by statistical tests
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120
Q

List new business valuation advantages of predictive analytics for experience studies

A
  • assess NB value at more granular level
  • allows management to make more specific changes if needed
    • revise benefits, increase fees, change producer compensation
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121
Q

Disadvantages of predictive analytics for experience studies

A
  • significant expertise required
  • risks: misinterpret data and flawed models
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122
Q

Limitations of predictive analytics (and traditional) in experience studies

A
  • lack of data at old ages (85+)
    • possible solutions: create trend lines or adjust standard models
  • not doing experience studies at all
    • some companies lack resources
  • pricing models may not support the more refined assumptions
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123
Q

List 4 main steps for building a predictive model

A
  1. collect and organize data
  2. prepare data
  3. build model
  4. final adjustments
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124
Q

List criteria for data that will be used in a predictive model

A
  1. relationship with target variable
  2. statistically signifcant
  3. correlation vs. cost to obtain
  4. regulatory/legal concerns
  5. 12-18 months of data
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125
Q

List and describe 2 classes of data that can be used in a predictive model for UW

A
  • Traditional
    • app data
    • medical information bureau (MIB)
    • motor vehicle record (MVR)
    • electronic Rx profile
    • traditional medical UW results
  • Non-traditional 3rd party data
    • credit scores (FCRA)
    • marketing data
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126
Q

List 4 main steps to prepare data for a predictive model

A
  1. generate variables
  2. exploratory data analysis
  3. variable transformation
  4. partition into 3 sets
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127
Q

List and describe 4 main steps to prepare data for a predictive model:

  • Generate variables
A
  • synthetic vs. disease-state
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128
Q

List and describe 4 main steps to prepare data for a predictive model:

  • Exploratory Data Analysis
A
  • distributional vs. univariate analysis
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129
Q

List and describe 4 main steps to prepare data for a predictive model:

  • Variable Transformation
A
  1. Group values into buckets
  2. Replace missing values
  3. Reduce effect of extreme values/outliers
  4. Convert variables into numerical values to capture trends
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130
Q

List and describe 4 main steps to prepare data for a predictive model:

  • Partition Data Into 3 Sets
A
  1. Train
  2. Validation
  3. Test
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131
Q

Describe additional adjustments that might be appropriate for a predictive underwriting model

A
  • layering on existing UW guidelines
    • example: exceptions for rare conditions
  • decision trees
    • used to narrow applicant pool into good/bad risks
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132
Q

Describe the following considerations with respect to predictive modeling for
underwriting.

  • Anti-selection concerns
A
  1. PH can’t manipulate 3rd party data
  2. model relies on data in total, not individual
  3. allow reduced UW requirements for some
  4. randomly traditionally UW some applicants (for checks)
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133
Q

Describe the following considerations with respect to predictive modeling for
underwriting.

  • Legal and ethical concerns
A
  1. collection of consumer data
  2. marketing data not subject to FCRA
  3. social views on privacy
  4. compliance and legal staff should review variables
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134
Q

Explain why credit data is useful for mortality prediction

A
  • main idea: qx = f(credit behavior)
  • Credit behavior indicates an individual’s conscientiousness
  • conscientious people tend to have better credit history ⇒ lower mortality
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135
Q

Give reasons why conscientious people tend to have better credit history, and therefore, lower mortality

A
  • Engage in healthier behavior/environments
  • Healthier friendships
  • Better education, career, and higher incomes
  • Have less unhealthy stress
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136
Q

2013 study showed what about why credit data is useful for mortality prediction?

A
  • lowest conscientious US adult mortality = 3.2x highest conscientious
  • falls to 1.6x after adjusting for smoking, alcohol, and waist size
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137
Q

List criteria for using credit data for UW purposes

A
  • adjacent markets utilize similar data/approaches
  • heavily regulated - allows for consumer dispute resolution
  • updated in near real time
  • used to develop model that is fit for purpose: mortality
  • credible population - allows for a separate holdout sample
  • available for vast majority of adult population
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138
Q

List the credit attributes that influence the TrueRisk Life score

A

The score is a function of 25 credit attributes across 4 categories:

  1. credit-seeking activity - recency/frequency of credit inquiries/trades
  2. credit tenure - credit history length, active trade count, months since oldest trade
  3. severity/frequency of derogatory credit info - bankruptcies, collections
  4. credit usage - utilization percentages, usage patterns, recency
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139
Q

Describe the data used to develop the TrueRisk Life model

  • Credit archive data were divided into 3 groups
A
  • 44M: training data used to create model w/ multivariate logistic regression
  • 30M: test/validation data to prevent overfitting
  • 18M: holdout data for mortality validation
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140
Q

Describe the data used to develop the TrueRisk Life model

  • Data sources
A
  • 1998 TransUnion credit archive: 92M useable records
    • originally 175M (roughly 90% US pop)
    • useable set only includes adulst w/ a SSN, credit history, and age 21-70
  • 2011 SS Death Master File (death data)
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141
Q

Characteristics needed for final variables in predictive analysis

A
  • low correlation
  • high predictive power
  • stable over time
  • non-gameable
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142
Q

Describe how to interpret the TrueRisk Life score with respect to mortality prediction

A
  • TrueRisk Life score for each individual = 1 to 100
    • 1 = best credit-based score possible (100 is worst)
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143
Q

How does age affect a TrueRisk Life score?

A
  • credit history generally improves w/ age
    • A/Es still increase within each TrueRisk Life score slightly w/ age
    • ages 60+: health factors play a bigger role
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144
Q

How are A/E ratios directionally related to a TrueRisk Life score?

A
  • A/E ratios increase w/ TrueRisk score (worsening credit)
    • worst 5% of scores mortalty more than 6x the best 5%
    • A/E ratio curve smooth and monotonically increasing

Credit segmentation does not wear off over time

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145
Q

Describe how the TrueRisk Life score relates to traditional forms of UW

A
  • Compared to full & non-medical UW
    • FU business skews toward lower TrueRisk Life score (better credit)
    • non-medical skews toward higher
    • A/E ratios increase with score for both UW types
    • early duration lapse rates increase w/ score
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146
Q

Review the FCRA Regulations on the back of this card

A

TrueRisk Life is subject to the FCRA

  • Governs collection, assembly, and use of consumer report information
  • Model does not use:
    • Credit card transactions
    • Social media
    • Income
    • Race
    • Criminal records or court filings
    • Property records
    • Religion or national origin
    • IRS data
    • Education
    • Checking/savings account information
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147
Q

Describe the LexisNexis Risk Classifier methodology

A
  • score ranges from 200 to 997 (worst to best)
  • Population studied: 8 million insurance shoppers ages 18–90
  • Study period: 2006–2010
  • based on public records, credit information, motor vehicle history
  • Death sources: SS Death Master File, state records, other
  • Expected mortality basis: 2008 VBT
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148
Q

Describe how the LexisNexis Risk Classifier score relates to each of the following:

  • Mortality risk within age, gender, and duration
A
  • Risk Classifier stratifies mortality risk across age, gender, and duration
  • Mortality risk decreases with score within age bands and gender
  • Older ages (70+) have less differentiation by score (health plays a bigger role)
  • Credit tends to improve with age
  • Differentiation does not wear off with time
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149
Q

Describe how the LexisNexis Risk Classifier score relates to each of the following:

  • Public records
A
  • Better public records and credit indicate better mortality
  • Mortality risk increases as past due balances increase
  • Other attributes that result in higher mortality:
    • Derogatory public records (bankruptcies, liens, judgments)
    • Felony and criminal convictions
  • People with professional licenses have lower mortality
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150
Q

Describe how the LexisNexis Risk Classifier score relates to each of the following:

  • Wealth
A
  • Risk Classifier scores generally increase with wealth
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151
Q

Describe how the LexisNexis Risk Classifier score relates to each of the following:

  • General population mortality risk
A
  • Mortality risk decreases as the Risk Classifier scores increase
  • People with better credit/behavior have lower mortality
  • Exposure is concentrated in the middle range of the scores
  • Exposure is very low at the best scores (900+)
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152
Q

List 4 possible methods for ending a mortality table

A
  1. Forced Method
  2. Blended Method
  3. Pattern Method
  4. Less-Than-One Method
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153
Q

List and describe 4 possible methods for ending a mortality table:

  • Less-Than-One Method
A
  • Choose an ultimate age
  • No changes to other qx ’s
  • At ultimate age, qx may be 1.000
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154
Q

List and describe 4 possible methods for ending a mortality table:

  • Pattern Method
A
  • qx ’s continue normally until they reach 1.000
  • Age where qx = 1.000 is ultimate age
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155
Q

List and describe 4 possible methods for ending a mortality table:

  • Blended Method
A
  • Choose ultimate age
  • Grade qx ’s to 1.000 at ultimate age
  • i.e. “blend” the qx ’s to 1.000
156
Q

List and describe 4 possible methods for ending a mortality table:

  • Forced Method
A
  • Choose ultimate age
  • Set ultimate qx = 1.000 at ultimate age
  • No changes to other qx ’s
157
Q

Describe difficulties when ending the mortality table

A
  • Difficulties with ultimate age and mortality levels
    • Little data at old ages
    • Questionable accuracy
158
Q

Describe slope considerations when ending the mortality table

A
  • Female slope higher at old ages (catching up to male)
  • Mortality increases at a decelerating rate from age 100 to 110
159
Q

Why is policyholder behavior getting more attention in the insurance industry?

A
  1. Increased product flexibility
  2. More investment components
  3. Volatility in financial markets
  4. Sophisticated financial reporting and regulation
  5. Social and technological advances
  6. Emerging behavioral economics research
160
Q

Explain the importance of understanding policyholder behavior

A
  • Policyholder behavior risk: Non-diversifiable and non-hedgable
    • Elective, unlike mortality risk
161
Q

How does policyholder behavior affect many areas of insurance operations?

A
  • Product design – early mistakes were made:
    • VA GMDB dollar-for-dollar withdrawals, LTCI lapse rates set too high
  • Reserves & capital – Premium paid, investment elections, fund transfers, etc.
  • ALM – Liability durations change as policyholders react to changing interest rates
  • ERM – Tail risk and other operational risk
162
Q

List 5 challenges in understanding policyholder behavior

A
  1. Lack of time
  2. Information asymmetry
  3. Curse of knowledge
  4. Data challenges
  5. Human decision making is not fully understood (but improving)
163
Q

Describe 5 challenges in understanding policyholder behavior:

  • Human decision making is not fully understood
A
  • (but improving)
  • Behavioral economics, Big Data, computing power, dynamic modeling
164
Q

Describe 5 challenges in understanding policyholder behavior:

  • Data challenges
A
  • Lack of data, credibility, and causal linkages
  • Systems weren’t designed with policyholder behavior in mind
  • Data not in required format
165
Q

Describe 5 challenges in understanding policyholder behavior:

  • Curse of knowledge
A
  • Significant gap between policyholder and actuarial product views
166
Q

Describe 5 challenges in understanding policyholder behavior:

  • Information asymmetry
A
  • Insurers have incomplete information about policyholder
  • Policyholders have incomplete understanding of products
167
Q

Describe 5 challenges in understanding policyholder behavior:

  • Lack of time
A

Time is spent preparing data, not analyzing it

168
Q

Describe how data sources for policyholder behavior assumptions vary

A
  • Own experience with the highest credibility:
    • Surrenders
    • Lapses/premium persistency
    • Loan utilization
    • Dividend elections
  • Own experience with the lowest credibility:
    • Mortality shifts due to lapses deviations
    • Shock mortality and lapse
    • Index elections/transfers
169
Q

Describe dynamic policyholder behavior assumptions

A
  • Dynamic Assumptions – vary with external factors in model
    • common uses: ALM, risk management
170
Q

List common dynamic policyholder behavior assumptions

A
  • Surrenders (UL, SGUL, annuities)
    • SGUL driver: in-the-moneyness
    • UL and annuity driver: credited rates
  • VA and EIA living benefit utilization
  • VA withdrawals and transfers
  • Others: loan utilization, funding, partial withdrawals, anti-selection
171
Q

Describe static policyholder behavior assumptions

A
  • Static Assumptions - does NOT vary with external factors
    • Common uses: pricing, valuation, forecasting
172
Q

Define financial efficiency

A

how optimally policyholders maximize their contract values

173
Q

Describe how companies typically account for financial efficiency in
policyholder behavior assumptions

A
  • most companies assume less than 100% (consistent with history)
  • most companies assume “neutral” to “very efficient”
  • may increase with sophistication of products/policyholders
    • Half of companies vary with product
    • Some companies vary with distribution channel
174
Q

What is a formal governance process? List model governance practices used by companies

A

Formal governance process – agreed-upon / well-communicated procedure for. . .

  1. Updating experience data
  2. Reviewing changes in experience
  3. Recommending modifications of assumptions
  4. Discussing consistency of assumptions across products/areas
  5. Sign-off on recommended changes
  6. Validating, updating, and reviewing actuarial models
175
Q

Techniques used by companies for validating, updating, and reviewing actuarial models

A
  • Common techniques: A/E analysis, dynamic validation
  • Newer techniques: back-testing, 3rd party review, stress/scenario testing
176
Q

Top policyholder behavior modeling challenge

A

Data availability and credibility

177
Q

Describe policyholder behavior modeling techniques

A
  1. Traditional actuarial modeling (by far most popular)
  2. Predictive modeling
  3. Monte Carlo simulation
178
Q

Give recommendations for handling the following aspects of policyholder behavior assumption setting

  • modeling
A
  • Balance dynamic assumption complexity with value added
  • Identify (in)consistencies across lines/areas
179
Q

Give recommendations for handling the following aspects of policyholder behavior assumption setting

  • Data collection, analysis, and assumption setting
A
  • Review data sources
  • Consider:
    • Customer service and web-based data
    • Applicability of in-force block experience to new products
    • Policyholder behavior under extreme scenarios
  • Apply predictive modeling / statistical techniques
  • Centralize internal data sources
180
Q

Give recommendations for handling the following aspects of policyholder behavior assumption setting

  • Governance Process Steps
A
  • Implement:
    • Formal process for experience data updates
    • Review procedure
  • Establish cross-functional:
    • Discussions about inconsistencies
    • Data analytics repository
  • Experiment with advanced statistical techniques
181
Q

Give recommendations for handling the following aspects of policyholder behavior assumption setting

  • Validation
A
  • Establish formal process
  • Document changes
  • “Model Sherriff”
182
Q

List benefits of utilizing behavioral economics in policyholder behavior assumptions

A
  1. Identify common shortcuts used in consumer decision-making
  2. Explain external variables affecting consumer decision-making
  3. Explain market inefficiencies
  4. Offer insights into product design and marketing
183
Q

List 5 decision shortcuts (heuristics)

A
  1. Relative choices
  2. Reliance on defaults
  3. Mental accounting
  4. Framing
  5. Status quo bias
184
Q

List and define 5 decision shortcuts (heuristics):

  • Status quo bias
A

people tend to stick with prior choices

185
Q

List and define 5 decision shortcuts (heuristics):

  • Framing
A

how choices are labeled can influence consumer decisions

186
Q

List and define 5 decision shortcuts (heuristics):

  • Mental accounting
A

tendency to create artificial budgets covering different categories of spending and saving

187
Q

List and define 5 decision shortcuts (heuristics):

  • Reliance on defaults
A

tendency to rely on assumptions set in the past

188
Q

List and define 5 decision shortcuts (heuristics):

  • Relative choices
A

tendency to make (sometimes irrational) decisions by comparing available choices

189
Q

List 4 value assessments (PH behavior)

A
  1. Love of free
  2. Anchoring
  3. Endowment effect
  4. Hyperbolic discounting
190
Q

List and define 4 value assessments:

  • Hyperbolic discounting
A

people tend to heavily discount the importance of future events (procrastinate

191
Q

List and define 4 value assessments:

  • Endowment effect
A

people don’t like to give up something they already have

192
Q

List and define 4 value assessments:

  • Anchoring
A

tendency for people’s numerical estimates to be influenced by suggested numbers, even if completely irrelevant

193
Q

List and define 4 value assessments:

  • Love of free
A

people have a disproportionate love of free things

194
Q

List 6 emotional impacts

A
  1. Risk aversion
  2. Overconfidence
  3. Loss aversion
  4. Self-control facilitation
  5. Hot and cold states
  6. Self-herding
195
Q

List and define 6 emotional impacts

  • Self-herding
A

future decisions may be made to justify past behaviors

196
Q

List and define 6 emotional impacts

  • Hot and cold states
A

being in a good or bad mood can lead to irrationally
optimistic or pessimistic decisions

197
Q

List and define 6 emotional impacts

  • Self-control facilitation
A

willingness to accept less freedom in order to facilitate saving

198
Q

List and define 6 emotional impacts

  • Loss aversion
A

bias to hold on to “losers” too long to avoid realizing losses

199
Q

List and define 6 emotional impacts

  • Overconfidence
A

overestimating one’s ability to make correct choices

200
Q

List and define 6 emotional impacts

  • Risk aversion
A

primary reason policyholders buy insurance: people don’t like risk

201
Q

Define the bandwagon effect (social impact)

A

people do what they see other people do

202
Q

List 4 behavioral economics principles that affect life insurance purchasing behaviors

A
  1. Decision Shortcuts
  2. Value Assessment
  3. Emotional Impacts
  4. Social Impacts
203
Q

Describe behavioral economics principles that affect life insurance purchasing
behaviors

  • Social Impacts
A
  • “Masculine” societies buy more life insurance than “feminine” societies
204
Q

Describe behavioral economics principles that affect life insurance purchasing
behaviors

  • Emotional Impacts
A
  • Loss-aversion – buying life insurance feels like a “loss” (buyer won’t get benefits)
  • Overconfidence bias – people underestimate risk of death (especially men!)
  • Hot and cold states – people are more likely to buy insurance after a tragedy
205
Q

Emotional Impacts - solutions for insurers

A
  • Solutions for insurers:
    • Stretch the time horizon over which risk is measured
    • Frame life insurance as an investment in loved ones
    • Associate positive emotions with life insurance (e.g. par WL becomes “free”)
206
Q

Describe behavioral economics principles that affect life insurance purchasing
behaviors

  • Value Assessment
A
  • life insurance is “sold, not bought”
  • people anchor on unlikely-to-happen current health risks
  • Insurers show/quantify the value over a lifetime
207
Q

Describe behavioral economics principles that affect life insurance purchasing
behaviors

  • Decision Shortcuts
A
  • Life insurance is associated with a negative event (death)
  • Framing: agents should sell a policy as a “disciplined, mandatory savings plan”
208
Q

Describe behavioral economics principles that affect annuity purchasing behaviors:

  • Decision Shortcuts
A
  • annuitization involves complex decisions
  • Mental accounting – consumers that view annuitization as a gamble will segregate the annuity
  • Framing – people annuitize more often when they see it as consumption rather than investment
  • Default decision – many people opt out for a lump sum
    • Possible solution: let people “test drive” the annuity
209
Q

Describe behavioral economics principles that affect annuity purchasing behaviors:

  • Value Assessment
A
  • people hyperbolically discount future annuity payments (devaluing them)
  • endowment effect – people don’t want to give up their assets
  • availability heuristic + conjunction fallacy – people underestimate longevity risk
210
Q

Describe behavioral economics principles that affect annuity purchasing behaviors:

  • Social Impacts
A
  • need to minimize the association between profit and death
211
Q

Describe behavioral economics principles that affect annuity purchasing behaviors:

  • Emotional Impacts
A
  • Loss aversion – people are unwilling to “gamble” on “losing” their savings
  • People don’t like the word “annuity”
212
Q

Describe 2 actuarial academic lapse theories

A
  • Interest rate hypothesis
    • High guaranteed credited rates lead to lower lapses
    • Lapses increase with market interest rates and stock returns
  • Emergency funds hypothesis
    • Policyholders surrender their policies due to financial distress

These are NOT based on behavioral economics

213
Q

How do decision shortcuts affect policyholders’ decision to lapse or surrender? How should insurers respond?

  • Mental accounting
A
  • Premiums: expenses or savings?
  • CSV: spending money or asset accumulation?
  • Lapses decrease as savings mindset increases
  • Marketing and product features can influence policyholder mindset
214
Q

How do decision shortcuts affect policyholders’ decision to lapse or surrender? How should insurers respond?

  • Relative choices
A
  • Policyholder’s “fair value” of current contract may be irrational
  • May lapse if other contracts’ features seem more attractive
  • Insurer should:
    • Be careful in how product is presented
    • Be aware of other products policyholders will compare against
215
Q

How do policyholders assess the value of contracts, and how does that affect their decision to lapse?

A
  • “Value” is a relative concept
    • Influenced by emotions rather than rational decisions
    • Tone of questions can influence answers
      • “Live to X years” vs. “die by X years”
  • Hyperbolic discounting
    • Policyholders are likely to postpone financial planning
  • Love of free
    • Policyholders may prefer self-supporting products that seem “free”
216
Q

Emotional component of life and annuity products

A
  • Life: Pay premiums but never see death benefit
  • Annuity: Avoid relying on family members in retirement
217
Q

How do emotional impacts impact policyholders’ decision to lapse?

  • Hot and cold states
A
  • Anxiety may influence lapse and withdrawal decisions
  • Marketing and other communications could trigger hot states
218
Q

How do emotional impacts impact policyholders’ decision to lapse?

  • Self-control facilitation
A
  • Some European countries require higher CSVs
  • Increasing policyholder options may not be in policyholders’ best interests
219
Q

How do emotional impacts impact policyholders’ decision to lapse?

  • Loss aversion
A
  • Policyholders may hold a “loser” VA contract (but guarantee may be a “winner” if ITM)
  • LTCI: policyholders persist to avoid pain of “losing” premiums paid
220
Q

How do emotional impacts impact policyholders’ decision to lapse?

  • Risk aversion
A

Lapses increase when risks go away (pay off mortgage, child goes to college)

221
Q

How do social connections impact policyholders’ decision to lapse?

A
  • Decisions are affected by family, friends, and other social contacts
  • Bandwagon effect
    • May affect mass lapse risk and liquidity strain
222
Q

How do decision shortcuts affect premium and funding levels?

  • Reliance on defaults
A
  • Policyholders. . .
    • May follow target premium illustrations
    • Tend to pay same premium as last period
  • Many actuaries assume recent patterns experience will continue
223
Q

How do decision shortcuts affect premium and funding levels?

  • Status quo bias
A
  • Increases policyholder inertia and procrastination
224
Q

How do value assessments affect premium and funding levels?

  • Anchoring
A
  • Numbers in marketing materials may influence funding
  • Avoid unintended anchoring in communications
225
Q

How do value assessments affect premium and funding levels?

  • Hyperbolic discounting
A
  • Decisions to pay premiums may trigger “pain”
  • Higher premium payment frequencies = more pain per year
  • Solution: frame premiums as savings rather than expenses
226
Q

How does self-herding affect premium and funding levels?

A
  • Policyholders may continue paying a premium because it’s what they did in the past
227
Q

How do decision shortcuts affect the timing of withdrawals?

  • Mental accounting
A
  • A savings perception may delay taking withdrawals
  • Tendency to see annuities as a “first source” of retirement income
228
Q

How do decision shortcuts affect the timing of withdrawals?

  • Reliance on defaults
A
  • Communications may signal when they should begin withdrawing
  • Product design may send signals (e.g. setting “full-benefit age,” bonuses)
229
Q

How doese overconfidence affect the timing of withdrawals?

A
  • Tendency to underestimate retirement date
  • Tendency to base financial planning on “best case” scenarios
  • Actual product use may be very different than planned
  • Tendency to underestimate future expenses
230
Q

How do decision shortcuts affect the amount of withdrawals?

  • Mental accounting
A
  • Policyholders may not realize how fungible money is
  • Withdrawal patterns may be tied to “mental buckets”
  • Understanding how policyholders perform mental accounting may reduce uncertainty
231
Q

How do decision shortcuts affect the amount of withdrawals?

  • Reliance on defaults
A
  • Withdrawals may be based on amounts endorsed by experts or social norms
232
Q

How do value assessments affect the amount of withdrawals?

  • Anchoring
A
  • Withdrawals may be influenced by irrelevant numbers shown to policyholders
233
Q

How do relative choices affect investment decisions and fund transfers?

A
  • “1/n rule” and “conditional 1/n rule”
    • Tendency to allocate money equally over all or a subset of choices
  • Overwhelmed policyholders seek safer options
  • Number and presentation of investment options has a large impact on allocations and policyholder satisfaction
234
Q

How does framing affect investment decisions and fund transfers?

A
  • “Retirement account” vs. “asset accumulation tool”
  • More form lines ⇒ more selections made by policyholders
235
Q

How does mental accounting affect investment decisions and fund transfers?

A
  • Policyholders may allocate “old money” vs. new money
236
Q

How does reliance on defaults affect investment decisions and fund transfers?

A
  • Tendency to follow insurer’s suggested allocations
  • Relying on defaults reduces policyholder’s future regret
237
Q

How does procrastination affect investment decisions and fund transfers?

A
  • Tendency to put off changing allocations for long periods of time
  • Tendency to leave designated “savings” alone despite better opportunities
  • Insurer considerations: balance profit opportunities with regulatory scrutiny
238
Q

How does risk aversion impact investment decisions and fund transfers?

A
  • Risk averse policyholders more likely to choose bond funds over stock funds
239
Q

How does loss aversion impact investment decisions and fund transfers?

A
  • Tendency to hold “loser” stocks to avoid realizing losses
  • Policyholder’s decision environment is different that stock investor’s:
    • VA reallocations don’t trigger taxes
    • Insurance guarantees may offset account losses
  • Insurer should:
    • Present distribution of outcomes over a long-term horizon
    • Reduce feedback frequency
240
Q

How does overconfidence impact investment decisions and fund transfers?

A
  • May result in high levels of trading (men suck!)
  • “Law of small numbers” – drawing inference from small data sets
  • “Availability bias” – overweighting salient, personal, or friends’ experience
241
Q

How do hot/cold states impact investment decisions and fund transfers?

A
  • Market signals trigger emotional states (fear, anxiety, optimism)
  • Insurers/advisors may also stimulate policyholder states
242
Q

What social impacts affect investment decisions & fund transfers?

A
  • Bandwagon effect
    • Your beliefs ≈ your friends’ beliefs
    • More pronounced during unusual changes in society, technology, or economy
    • May result in poor market timing
243
Q

How is annuitization affected by decision shortcuts?

  • Reliance on defaults
A
  • Annuitization usually requires an active decision (opt-in)
  • Insurers could make annuitization a default option
244
Q

How is annuitization affected by decision shortcuts?

  • Framing
A
  • Annuitization utilization may increase with:
    • Marketing and education for the need for retirement income
    • Increased focus on income stream, not account balance
    • Avoid making the income stream seem like a “loss”
    • Offer trial periods
245
Q

How is annuitization affected by emotional impacts?

  • Risk aversion
A

Certain income streams create mental ease

246
Q

How is annuitization affected by emotional impacts?

  • Loss aversion
A

Income stream more likely to seem like a “loss” if policyholder thinks it lasts only a few years

247
Q

Describe drawbacks of the traditional actuarial approach to modeling policyholder behavior

A
  • Aggregate level modeling – does not consider socio-demographic, attitudinal, or behavioral factors
    • Causal factors behind lapse decisions are lost
    • Removing variation in data hurts model
  • Rational approach – does not account for social, cognitive, and emotional factors
248
Q

What are the limitations of predictive analytics for modeling policyholder behavior?

A
  • Fails to capture causal influences and non-quantitative factors
  • Fails to model decision shortcuts, psychological biases, etc.
  • Doesn’t capture life events (e.g. marriage) that trigger a life insurance purchase
249
Q

How is predictive analytics is an improvement over traditional actuarial models for modeling policyholder behavior?

A
  • Can operate on variables about the individual (age, education, etc.)
  • Can predict behaviors triggered by environmental changes
250
Q

Describe possible approaches with the most potential for policyholder behavior modeling

A
  • Move from statistical averages to understanding household decision-making
  • Model the causal structure of decision making, including the effect of biases
251
Q

Describe possible future advances in policyholder behavior modeling based on developments in other industries

A
  • “Smarter” databases containing data “micro data”
  • Exponential advances in computational power
  • Better understanding of decision-making processes and behaviors
252
Q

Profitability test with zero lapse rate or under illustration rules

A
  • Lapse-supported if
    • PV profits under test < PV profits with normal lapse assumption
    • Else, the product is unlikely to ever be lapse-supported
  • ASOP 24: can’t show illustrations if policy fails self support test
253
Q

Common features of lapse-supported insurance

A
  • guaranteed level premiums
  • low/zero CSV
  • high late benefits
254
Q

What happens when lapse-supported insurance premiums are too low

A
  • results in insufficient reserves
    • warning sign: higher-than-expected early profits
255
Q

Where is the risk in lapse-supported insurance?

A
  • ultimate lapses, not early lapses
    • lower-than-expected early lapses increase profit
    • lower-than-expected ultimate lapses increase benefit costs
256
Q

What is the highest risk for lapse-supported insurance?

A
  • High income market
    • Competitive/marketing pressures keep premiums low
    • Policyholders are least likely to lapse
    • Agent persistency data may be an illusion due to past replacement activity
257
Q

What are the benefits of ROP w.r.t. lapse risk?

A
  • Less risky if lapses are driven by independent events (e.g. employee turnover)
  • Help blocks/companies with poor persistency
  • Increase profitability in low income markets
258
Q

Describe how return-of-premium riders can affect lapse risk

A
  • ROP riders create a tontine – everyone pays in, but only “survivors” collect ROP
    • Bad investment for policyholder on lapse-adjusted basis
  • ROP cash value is not enough to lower the risk of low lapses
  • ROP can convert limited-pay products to lapse-supported
259
Q

What was the key finding from the SOA’s study on flexible premium UL premium persistency?

A
  • Many companies simply assume 100% premium persistency
    • Aggregate premium falls with duration due to limited pay business
    • Companies often vary persistency by duration
260
Q

Pricing assumptions vs. CFT and GAAP/IFRS from the SOA’s study on flexible premium UL premium persistency

A
  • Pricing assumes more patterns in general
  • Some companies use a weighted average persistency assumption
  • Companies adjust ULSG pricing persistency to prevent lapses
261
Q

Areas for improvement from the SOA’s study on flexible premium UL premium persistency

A
  • Dynamic premium persistency assumptions
  • Sensitivity testing

*few companies currently doing

262
Q

Describe a viatical settlement and the viatical market

A
  • when an insurer buys a life insurance policy and then recieves the death benefit when the insured dies
  • the first life settlements that showed up in the 1980s (focused on life expectancies < 2 years)
263
Q

Describe the traditional life settlement market

A
  • High growth market
  • Popular with large face policies owned by impaired lives
  • Policyholders sell their policies to the highest bidder
  • Policyholder receives more than CSV
  • Investor holds policy at fair market value
264
Q

Problems that have occurred due to increasing bidding activity in the traditional life settlement market

A
  • Investors taking more risk—buying policies with longer life expectancies
  • Overpayment due to intense bidding
  • Lack of compensation disclosure
265
Q

List 3 forms of premium financing life settlements

A
  1. Insured Borrows Money To Buy Policy
  2. Charity-Owned Life Insurance
  3. Stranger-Owned Life Insurance
266
Q

Describe 3 forms of premium financing life settlements

  • Stranger-Owned Life Insurance
A
  • Works like charity-owned
267
Q

Describe 3 forms of premium financing life settlements

  • Charity-Owned Life Insurance
A
  • Investors lend money to purchase a policy
  • The charity has an insurable interest in the donor
  • When insured dies, investors and charity split the DB
268
Q

Describe 3 forms of premium financing life settlements

  • Insured Borrows Money To Buy Policy
A

Wait 2 years, then policyholder can. . .

  • Repay loan and keep policy or
  • Transfer policy to lender as a life settlement
269
Q

What is another proposed solution to life settlements?

A
  • Pay policyholders a higher CSV based on updated underwriting
270
Q

How have insurers have responded to life settlements?

A
  1. Limit number of illustrations provided and response time
  2. Public announcements portraying settlements as a bad deal for policyholders
  3. “If you can’t beat ’em, join ’em”

Insurers mostly don’t like life settlements

271
Q

What 5 factors are likely to drive growth in settlement market?

A
  1. Continued strong demand for larger face policies
  2. Premium-financed policies transfered to investors
  3. Settlement companies’ ability to estimate life expectancy more accurately than standard tables
  4. Preferred underwriting may wear off => settlement value > cash value
  5. Investors will likely target low premium products to exploit mispricing
272
Q

How will life settlements impact pricing?

A
  • Settlement policies will get minimum funding needed to keep them in force
  • High persistency and high mortality, especially at older ages
  • Inefficiencies in policyholder behavior may go away
  • AXXX reinsurance is getting harder to find
  • Economic underwriting – underwriting may ultimately improve thanks to investors
  • High settlement activity can be a sign that the product is mispriced
273
Q

Describe key variations in life expectancy data related to life settlements

A
  • LE falls with age and is lower for males
  • Most life settlements involve impaired insureds (lower LE than standard)
274
Q

Difference between standard and impaired life expectancy

A
  • difference falls with age
  • difference has been falling overall with time
275
Q

Why standard and impaired LEs in life settlements have been converging

A
  • Underwriting is getting better and/or more conservative
  • LEs may be improving
  • Increased investor demand for longer LEs
276
Q

Describe the typical types of life insurance involved in life settlements

A

Typical life settlement: $1–10MM UL policy on a NS Male age 70-something

  • Mostly UL (88%), then VUL, WL, and term
  • Most owned by trusts or individuals
  • Standard life settlement (most common) – medically underwritten (DB>$1MM)
  • Simplified life settlement – less underwriting, smaller policies (DB
  • Most involve single lives (3 out of 4 are male)
  • Nearly all are non-smoker
  • Insured age distribution is symmetric around age 75
277
Q

Describe trends in DB retention in life settlements and simplified life settlements

A

Retained DB has been increasing over time

  • Benefits for investors
    • Better aligns incentives of investors and policyholders
    • Death is reported more promptly
  • Benefits for policyholders
    • Retain some DB while eliminating premium payments
    • Appealing to policyholders who can’t afford premiums or new coverage
278
Q

Ways simplified life settlements are gaining popularity

A
  • Close faster (less underwriting)
  • Market is spreading to middle class
  • Baby boomers need the cash for other things
279
Q

How is the expected IRR of a life settlement determined?

A

Expected IRR = discount rate such that:

PV(DB Payable to Investor) - PV(Premiums) = Purchase Price

*IRR can also be expressed as a spread over treasuries
ILA

280
Q

average expected IRR of a life settlement

A
  • 12.5%
  • IRRs are positive even if actual LE is 3 years higher than expected
281
Q

3 competing hypotheses for the adverse selection hypotheses that might explain expected IRR on a life settlement

A
  • H0: Retained DB has no effect on expected IRR
  • H1: Higher retained DB ⇒ lower expected IRR
  • H2: Higher retained DB ⇒ higher expected IRR
282
Q

Properties of adverse selection hypotheses that might explain the expected IRR on a life settlement

  • H2: Higher retained DB ⇒ higher expected IRR
A
  • “Continued demand hypothesis”
  • Policyholders are willing to accept lower offers to retain more DB
  • Lower price ⇒ higher IRR for investor
283
Q

Properties of adverse selection hypotheses that might explain the expected IRR on a life settlement

  • H1: Higher retained DB ⇒ lower expected IRR
A
  • Adverse selection: unhealthy insureds should want to retain more DB
  • Investors are more likely to overpay as retained DB decreases (“single-crossing property”)
  • Higher price ⇒ lower IRR for investor
284
Q

Describe supply side hypotheses that might explain the expected IRR on a life settlement

A
  • H3: Higher premium convexity ⇒ higher IRR
    • Increasing premium schedules create risk for investor
  • H4: Smaller policies create diversification ⇒ lower IRR
    • Buying larger numbers of small policies creates diversification
  • H5: Large policies dilute fixed costs ⇒ lower IRR
    • Cost per amount of DB is lower for high DB policies
  • H6: More underwriting LE estimates ⇒ lower IRR
    • More information on life expectancy reduces investor risk

H4 & H5 oppose each other!

285
Q

Describe regression model variables that predict the IRR of a life settlement

  • HighNDB
A
  • ß Value = 0.00 to 0.03
  • Interpretation: >10 million face policies have up to 3% higher
    IRR than < 1 million face policies
286
Q

Describe regression model variables that predict the IRR of a life settlement

  • LEest2, LEest3–4
A
  • ß Value = -0.0055, -0.0044
  • Interpretation: IRR is lower if have more than 1 LE estimate
    (supports H6)
287
Q

Describe regression model variables that predict the IRR of a life settlement

  • SLS
A
  • ß Value = -0.04
  • Interpretation: Simplifed settlements for lower face policies
    have lower IRRs (supports H4, not H5)
288
Q

Describe regression model variables that predict the IRR of a life settlement

  • Convexity
A
  • ß Value = 0.02
  • Interpretation: IRR increases as premium slope increases
    (supports H3)
289
Q

Describe regression model variables that predict the IRR of a life settlement

  • RDB/NDB
A
  • ß Value = 0.11 to 0.14
  • Interpretation: Presence of retained DBs increases IRR
    (supports H2, not H1)
290
Q

Describe regression model variables that predict the IRR of a life settlement

  • MediumNDB
A
  • ß Value = 0.00 to 0.01
  • Interpretation: 1–10 million face policies have up to 1% higher
    IRR than
291
Q

What hypotheses are supported by the regression model testing performed by the authors of “Testing for Adverse Selection in Life Settlements?”

A
  • The results show that expected life settlement returns are driven by economic factors:
    • H2: Continued demand for life insurance hypothesis
    • H3: Premium convexity hypothesis
    • H4: Diversification hypothesis
    • H6: Model uncertainty hypothesis
  • Expected IRRs are not driven by adverse selection by policyholders
292
Q

Compare the popularity of guaranteed living withdrawal benefits with other types of guaranteed living benefits

A
  • GLWB and GMIB are most popular by far
    • 88% of VA contracts in study had a GLWB or GMIB
    • GLWB has the highest sales growth (most premium occurs year 1)
    • The GLWB is significantly more popular than the non-lifetime version (GMWB)
  • GLWB and GMIB have the highest persistency (very low surrender rates)
  • GMAB is popular with younger buyers
293
Q

List the 4 inter-connected relationships involving GLWB and GMIB utilization

A
  1. When withdrawals start
  2. Method of withdrawals
  3. Amount of withdrawal taken
  4. Surrender rates
294
Q

Describe the inter-connected relationships involving GLWB and GMIB utilization:

  • Surrender rates
A
  • factors that result in low surrenders:
    • Older owners (65+) in general—even lower if taking withdrawals
    • Younger owners not taking withdrawals yet
    • SWPs have lower surrenders than non-systematic
    • Withdrawal amount ≈ 100% of max amount (not too far below/above 100%)
    • Higher ITM ⇒ lower surrenders
    • Still in surrender charge period (higher lapse when exiting)
295
Q

Describe the inter-connected relationships involving GLWB and GMIB utilization:

  • Amount of withdrawal taken
A
  • Most owners take the max allowed by contract (e.g. 4% of benefit base per year)
  • Younger owners are more likely to exceed max
  • ITM has little to no effect
296
Q

Describe the inter-connected relationships involving GLWB and GMIB utilization:

  • Method of withdrawals
A
  • SWPs most significant
  • SWPs are popular for lifetime benefits and older owners (65+)
  • Withdrawals usually continue once they start (low surrenders)
297
Q

Describe the inter-connected relationships involving GLWB and GMIB utilization:

  • When withdrawals start
A
  • age and source of funding most significant
  • after age 70, withdrawals increase with age and duration
  • 2/3 of contracts are funded with qualified money (tax-favored retirement funds)
298
Q

List action steps and issues that an actuary should consider when pricing VA GLBs

A
  • Expect high utilization of lifetime benefits (hence low lapses)
    • Many will be used to fund retirement
    • GLB riders are popular
  • Qualified money has the highest withdrawal risk
  • Withdrawal activity can predict surrender activity
  • Modeling GLB utilization is important, especially for lifetime options
299
Q

Product design of VA GLBs

  • GMWB
A
  • All offer step-up options (i.e. ratchet to AV)
  • Differences with GLWB
    • No automatic increase to benefit base if delay withdrawals
    • No cap on benefit base
300
Q

Product design of VA GLBs

  • GMAB
A
  • Most common benefit base design: 100% of premiums paid in
  • Typical waiting period: 10+ years
301
Q

Product design of VA GLBs

  • GMIB
A
  • Most common design: roll-up or max(roll-up, ratchet)
  • Most are B-share (have SCs)
  • Annuitization rates are extremely low (0.3%), but increase with age, size, and ITM
302
Q

Product design of VA GLBs

  • GLWB
A
  • Lifetime benefits available from ages 50–99 (usually 54–84)
  • Median max withdrawal allowed = 4%
  • Benefit base usually reduced proportionally for excess withdrawals
303
Q

Describe the purpose and scope of ASOP 54

A
  • IN Scope: applies to actuaries who price life and annuity products
    • Products written on individual policy forms or equivalent
  • NOT in scope:
    • Changes to non-guaranteed elements or dividends on in-force policies
      • See ASOP 2
    • Reinsurance contract pricing
    • Illustration of non-guaranteed charges or benefits
304
Q

Describe initial pricing considerations under ASOP 54

  • Relevant Characteristics of the Product
A
  • Intended product design objectives
  • Intended market, anticipated sales, and competitive alternatives
  • How the product will be sold (underwriting, distribution, etc.)
  • How the product will be administered (valuation systems, operational risks, etc.)
  • Risk mitigation strategies (reinsurance, hedging, etc.)
  • Applicable law (statutes, regulations, etc.)
  • Tax treatment of the product
305
Q

Describe initial pricing considerations under ASOP 54

  • Criteria of the Actuary’s Principal
A
  • Choice of profitability metrics
  • Targets for profitability metrics (product level, cell level, etc.)
  • Approach for reflecting risk capital
  • Relevant risk management policies (e.g. earnings volatility limits)
306
Q

Describe considerations for selecting profitability metrics under ASOP 54

A
  • actuary should consider more than one (IRR, ROE, BEY, etc.)
  • Considerations When Selecting a Profitability Metric
    • Expected pattern of profits over time
    • Significance of underlying risks (e.g. size and pattern of risk capital)
    • Any other relevant considerations
307
Q

List model development and selection considerations under ASOP 54

A
  • Time horizon
  • Granularity
  • Dynamic assumptions
  • Asset returns
  • Economic scenarios (MC or RW)
  • Accounting and actuarial bases
  • Risk capital framework
  • Taxes
  • Risk evaluation
  • Risk mitigation
  • Model validation
308
Q

List model development and selection considerations under ASOP 54

  • Time horizon
A
  • sufficient to capture profitability and risks
309
Q

List model development and selection considerations under ASOP 54

  • Granularity
A
  • include necessary detail (time intervals, assumptions, etc.)
310
Q

List model development and selection considerations under ASOP 54

  • Dynamic assumptions
A
  • policyholder behavior, etc.
311
Q

List model development and selection considerations under ASOP 54

  • Asset returns
A
  • should be consistent with company practice
312
Q

List model development and selection considerations under ASOP 54

  • Economic scenarios
A
  • (MC or RW) – represent an appropriate range
313
Q

List model development and selection considerations under ASOP 54

  • Accounting and actuarial bases
A
  • statutory, GAAP, tax, etc.
314
Q

List model development and selection considerations under ASOP 54

  • Risk capital framework
A
  • reflect the framework used in practice
315
Q

List model development and selection considerations under ASOP 54

  • Taxes
A
  • model should be based on expected tax structure and related items
316
Q

List model development and selection considerations under ASOP 54

  • Risk evaluation
A
  • model should evaluate risks appropriately
317
Q

List model development and selection considerations under ASOP 54

  • Risk mitigation
A
  • reflect strategies that will support the product
318
Q

List model development and selection considerations under ASOP 54

  • Model validation
A
  • model should be sufficiently transparent
319
Q

Describe pricing assumption considerations under ASOP 54

A
  • based on relevant and credible data
  • margins (credibility, uncertainty, appropriateness in aggregate)
  • consistent internally, with company practices, and with similar assumptions
  • reflect interdependencies with each other
  • document assumptions, margins, rationale, and any data modifications
320
Q

Specific assumption setting considerations under ASOP 54

A
  • Anticipated sales distribution
  • RW or MC theory for investment/economic market assumptions
  • Mortality/morbidity
  • Policyholder behavior (elective benefits, lapses, etc.)
  • Anticipated expense inflation and expense allocation basis
  • In-force management strategies (e.g. policyholder dividends)
  • Capital market assumptions – replicating portfolio performance
321
Q

Describe risk evaluation considerations when performing profitability analysis under ASOP 54

A
  • Sensitivity Analysis – evaluate how assumption deviations impact profitability (more for more impactful assumptions)
  • Stochastic Analysis – appropriate for interest rate and equity returns
  • Consider risk mitigation strategies
322
Q

Describe governance and control considerations under ASOP 54

A

*Consider and document any used in pricing

  • Effective oversight of methods and assumptions
  • Protection of the model from unintentional/untested changes
  • Model input validation
  • Consistency of model values with those calculated outside the model
  • Validation of projected model values
  • Review of assumptions by a knowledgeable person
323
Q

List actuarial communications required by ASOP 54

A

Required disclosures:

  • Criteria of the actuary’s principal
  • Relevant product characteristics
  • Profitability metrics
  • Considerations used to determine the model
  • Material pricing assumptions
  • Results of risk evaluation
  • Reliance on
    • Governance and controls used by others
    • Data or other information supplied by others
    • Assumptions provided by others
  • Results of the profitability analysis
324
Q

Which ASOP is for general guidance on actuarial communications

A

ASOP 41

325
Q

Describe the following with respect to the Proposed ASOP on Setting Setting Assumptions:

  • Effective Date
A
  • actuarial work with an information date >= 12 months after this ASOP is adopted by the ASB
326
Q

Describe the following with respect to the Proposed ASOP on Setting Setting Assumptions:

  • Scope
A
  • ALL practice areas
    • Developing, selecting/choosing assumptions, analysis of data/experience,
    • industry studies, trends, economic forecasts, etc.
    • Includes the selection of assumption methodology
    • Specific practice ASOPs govern if they conflict with this ASOP
327
Q

Describe the following with respect to the Proposed ASOP on Setting Setting Assumptions:

  • Purpose
A
  • Provides guidance to actuaries performing actuarial services that include
  • Setting assumptions
  • Assessing the reasonableness of assumptions set by others
328
Q

Describe general considerations for the actuary when setting or assessing the reasonableness of assumptions.

A
  • Nature of assignment
  • Availability/relevance/credibility of data
  • Other available/relevant information
  • Will future experience differ significantly from past experience?
329
Q

List ways to assess reasonableness of assumptions

A
  • Consider reasonableness of material assumptions, selection methodology, and any tendency to misestimate
  • Assess whether assumptions are reasonable in aggregate (determine adjustments)
  • Ensure assumptions are NOT set to counteract prescribed assumptions
  • Disclose material inconsistencies in non-prescribed assumptions
330
Q

Describe considerations for the actuary when setting margins for adverse deviation and considering changes in conditions through the information date

A
  • Margins for Adverse Deviations – consider appropriateness
    • Consider uncertainty in underlying data
  • Changes in Conditions Through the Information Date:
    • Internal circumstances: changes in claims processing, business mix, etc.
    • External circumstances: changes in economy, laws, regulations, technology, etc.)
331
Q

Describe proposed ASOP guidance for the following issues related to assumption settting:

  • Prescribed assumptions set by law
A
  • disclose but don’t assess
332
Q

Describe proposed ASOP guidance for the following issues related to assumption settting:

  • Assumptions set by others
A
  • assess reasonableness
  • follow Precept 8 of the Code of Professional Conduct (don’t mislead!)
333
Q

Describe proposed ASOP guidance for the following issues related to assumption settting:

  • Reliance on others
A
  • Disclose reliance on experts (investment advisers, economists, etc.)
  • Actual assumption MUST be set based on the actuary’s judgment
  • If actuaries collaborate, the signing actuary is responsible
334
Q

Describe proposed ASOP guidance for the following issues related to assumption settting:

  • Alternative assumptions
A
  • Evaluate the alternative assumptions using sensitivity analysis
335
Q

What actuarial communications are required for assumption setting?

A

Disclose the following if practical and relevant:

  • Material assumptions – provide sufficient detail for another qualified actuary to assess
  • Material changes in assumptions since the most recent comparable results
  • Events on or after the information date that may affect the assumptions
  • Material inconsistencies in assumptions along with reasons for
  • What the actuary is taking responsibility for (setting the assumptions)

Refer to ASOP 41: “Actuarial Communications” for additional guidance

336
Q

List challenges associated with developing mortality assumptions

A
  1. Lack of past experience consistent with today’s UW criteria
  2. Uncertainty about:
    • Preferred/residual ratios—how they change over time
    • Slope of aggregate rates over S&U period
    • Anti-selection after level term period unknown
  3. Mortality improvement
    • Lack of consistent data
    • Uncertainty whether past improvement will continue in future
337
Q

Dukes MacDonald (D-M) method

A
  • n% of reverters are fully select
338
Q

Becker-Kitsos (B-K)

A
  • all reverters’ mortality = fully select + extra amount that grades off
339
Q

Compare the Dukes MacDonald (D-M) and Becker-Kitsos (B-K) methods

A
  • Both take a conservation of deaths approach
  • Both assume the excess lapse group is made up of
    1. Select lives
    2. Normal lives
  • Effect of both: Lower reverter mortality => higher persister mortality
340
Q

Describe the following with respect to term mortality and lapses.

  • Recommended Sensitivity Tests
A
  • Vary total and excess lapse rates assumed
  • Vary parameters in D-M and B-K approaches
  • Assume no profits after level premium period
341
Q

Describe the following with respect to term mortality and lapses.

  • Disadvantages of Not Offering Post-Level Term Coverage
A
  • Loss of profit from high premiums paid by persisters
  • Lower GAAP income
  • Not as marketable to policyholders
342
Q

Describe the following with respect to term mortality and lapses.

  • Limitations of D-M and B-K
A
  • No classification by underwriting class
  • Ignores premium size in ART period
  • Assumes 100% rational policyholder behavior
343
Q

Describe the key shock lapse patterns from the SOA’s “Report on the Lapse and Mortality Experience of Post-Level Premium Period Term Plans”:

  1. Shock lapse rate relative to level period
  2. Factors that drive variations in post-level lapse experience
A
  • Aggregate duration 10, 11, and 12 lapses = 70%, 40%, < 20%
  • Shock lapses increase with:
    • Premium jump size
    • Issue age
    • Males
    • Super preferred
    • Face amount
    • Annual mode (lower frequencies)
    • Issue age and/or face within premium jump group
  • Shock lapses are heavily skewed to EOY 10 and BOY 11
344
Q

Describe the relationship between lapse and mortality in the post-level period

A
  • Mortality deterioration is highly correlated with shock lapses
    • Rises at an exponential rate with shock lapse size
345
Q

Describe key patterns in post-level mortality experience uncovered by the SOA’s “Report on the Lapse and Mortality Experience of Post-Level Premium Period Term Plans.”

A
  • Aggregate duration 11 mortality = 300% of level period
    • Drops to 200% in duration 12, then declines slowly
  • Mortality increases with the same factors that increase shock lapse:
    • Premium jump size
    • Issue age
    • Males (slightly)
    • Super preferred
    • Face amount
  • Mortality deterioration is skewed toward BOY 11
346
Q

Describe how early post-level term experience has emerged

A
  • Overall: experience is very limited
    • Degree of mortality deterioration is unknown
    • Early lapse experience has been lower than expected
347
Q

Possible explanations for lower post-level term lapses

A
  • Policyholder complacency (automatic bank draft)
  • Still shopping for lower rate
  • Some may be OK with higher cost
  • Reluctance to go through underwriting
  • Personal situations that delay lapse (e.g. divorce)
348
Q

Describe traditional approach for determining post-level term premiums and its advantages and disadvantages.

A
  • The Approach: Assume shock lapses and develop a single YRT ceiling
  • Advantage: simplest to administer
  • Disadvantages
    • Worst risks remain in force
    • Claims volatility due to limited credibility
    • Potential for negative publicity
349
Q

Describe the simplified re-underwriting approach for determining post-level term premiums and its advantages and disadvantages

  • The Approach
A
  • The Approach:
    • Optional questionnaire determines PLT rate class (e.g. SM/NS)
    • If decline survey: default to original YRT ceiling
350
Q

Describe the simplified re-underwriting approach for determining post-level term premiums and its advantages and disadvantages

  • Advantages
A
  • Advantages:
    • Greater sense of fairness to policyholders and regulators
    • Lowers selective lapsation
    • Insured gets lower rate and insurer has more confidence in rates
351
Q

Describe the simplified re-underwriting approach for determining post-level term premiums and its advantages and disadvantages

  • Disadvantages
A
  • Disadvantages:
    • May be an early signal that increases lapses and/or term conversions
    • Must solve implementation problems
      • Communication to policyholders, questionnaire design, how to handle incomplete questionnaires, how to maximize response rate
352
Q

Describe the graded approach for determining post-level term premiums and its advantages and disadvantages

  • The Approach
A
  • The Approach:
    • PLT premiums increase at a smaller increment intially (e.g. 5 years) before reaching YRT scale
353
Q

Describe the graded approach for determining post-level term premiums and its advantages and disadvantages

  • Advantages
A
  • Advantages:
    • Makes initial PLT rates more attractive (lower lapses)
    • Insurer can still increase to YRT ceiling
    • Avoids underwriting
    • Low administrative cost
    • Early experience suggests it works as intended
354
Q

Describe the graded approach for determining post-level term premiums and its advantages and disadvantages

  • Disadvantages
A
  • Disadvantages:
    • Best risks can still lapse to get better rates
    • No reliable experience for post-graded YRT period
    • Experience is Canadian ⇒ will the U.S. be the same?
355
Q

Describe the class continuation approach for determining post-level term premiums and its advantages and disadvantages.

  • The Approach
A
  • The Approach:
    • Maintain level term class structure into PLT period
    • Develop separate YRT scales by class
    • All YRT scales converge to an ultimate scale (original YRT scale)
356
Q

Describe the class continuation approach for determining post-level term premiums and its advantages and disadvantages.

  • Advantages
A
  • Advantages:
    • May be “the fairest approach” since it uses original underwriting
    • YRT scale encourages/discourages lapses based on class
    • Permanent insurance experience could be used for classes
357
Q

Describe the class continuation approach for determining post-level term premiums and its advantages and disadvantages.

  • Disadvantages
A
  • Disadvantages:
    • Lack of experience with the approach
    • Preferred classes will have lowest initial YRT premiums but steepest slope
    • Permanent pricing can’t be used directly
    • Selective lapse risk from preferred insureds who have become impaired
358
Q

Describe shock lapses in the context of the Dukes-MacDonald selective lapsation model

A
  • Shock lapse – high, additional lapse rate at end of level premium period
    • Healthy lives aren’t willing to pay the higher post-level premiums
359
Q

Describe the Dukes-MacDonald selective lapsation model

A
  • D-M predicts the expected mortality after a shock lapse
  • Split shock lapses into and persisters and reverters:
    • Persisters – lives remaining after the shock lapse
    • Reverters – lives in the shock lapse group
    • Effectiveness rate – The percentage of reverters that qualify for select mortality
  • Use the conservation of deaths principle to solve for persister mortality
360
Q

Give a formula for Conservation of Deaths.

A

qnorm = qsel x weff + qnorm x (wnon-eff + wbase) + qpers x (1 - w)

qsel = select issue age mortality

qnorm ​= normal, point-in-scale mortality

qpers ​= mortality of persisters remaining in force

w ​ = total lapse rate at the time of the shock lapse

w<span>base </span>= normal base lapse rate in absence of shock lapse

w<span>shock </span>= w - wbase

weff ​ = “effective reverters” = effectiveness rate x wshock

wnon-eff ​ = non-effective reverters = wshock - weff

361
Q

Describe important considerations for setting a post-level mortality assumption

A
  • Shock lapses and expected mortality are closely related
    • Do not set assumptions independently!
  • D-M mortality predictions are sensitive to the effectiveness rate assumed
  • Effectiveness depends on policyholder behavior
    • Information about health
    • Ability to replace coverage
  • Other factors also drive shock lapses, but D-M provides useful boundaries
362
Q

How does ultimate lapse experience vary by level term length (10, 15, and 20 years)?

A
  • Ultimate lapse rate falls as term length increases:
    • T10: 6%
    • T15: 4%
    • T20: 3%
363
Q

How do level term lapse rates vary with the following?

  1. Issue age
  2. Risk class
  3. Smoker vs. non-smoker
  4. Joint lives vs. single lives
  5. Gender
A
  • *Lower lapses for. . .**
    1. Older issue ages
    2. Preferred classes
    3. Non-smokers
    4. Joint products

Male lapses ≈ female lapses

364
Q

What did the industry learn from Canadian experience with early T100 products?

A
  • T100: Guaranteed Level Premiums to 100, no CSV
    • Early pricing assumed 6% ultimate lapse rates
    • Actual ultimate lapse rates were less than 2% (oops!)
    • Lower lapses led to much higher reserves and lower income
    • Lessons learned can apply to other lapse supported products
      • Examples include SGUL, LTCI, ROP Term
365
Q

“Death Supported” Concept from Canadian experience with early T100 products

A
  • Ceding company gains if mortality > expected
  • Possible if reinsurance premiums > T100 premium
366
Q

Describe challenges with term conversions

A
  • Term conversions have a higher mortality cost due to anti-selection: qc[x] + t > qsx + t
  • Companies may not even know the cost of their term conversions
  • Data credibility problems: administrative system data is poor or doesn’t exist
  • Historical data may understate conversion activity
367
Q

Describe solutions with term conversions

A
  • Use industry data to validate company-specific data
  • Reinsurers can help direct writers by sharing data and insight
368
Q

Describe price and competitive pressures with term conversions

A
  • Term products are VERY competitive
  • Sales are VERY sensitive to price
  • LOTS of pressure to keep premiums LOW
  • Conversions add value but also cost → where do you reflect the cost?
369
Q

How can term products be designed to restrict or encourage conversions?

A
  • Features that restrict conversions
    1. Limit to N years after issue
    2. Maximum attained age
    3. Not available during active disability premium waiver
  • Products that encourage conversions
    • ART-to-permanent conversions (targeted at younger buyers)
370
Q

How do direct writers reflect conversions in pricing?

A
  • Include in permanent product pricing
    • Advantage: keeps term premium low
    • Difficulties: hard to estimate conversion utilization and permanent plan sales volume
    • Solutions:
      • Use conservative load in permanent premium
      • Develop a conversions-only product (may be unattractive)
  • Include in term pricing
    • Advantage: aligns conversion cost with originating term product
    • Include cost of agent compensation and conversion credits
  • Include in term pricing but scale back availability
    • Limit to N years after issue
    • Offer 2 term products
371
Q

How do reinsurance treaties cover term conversions that occur in term block reinsured?

A
  • Use point-in-scale YRT rates
  • Coinsurance treaties: YRT rates apply only to conversions
  • YRT treaties:
    • Increase overall YRT rates OR
    • Use separate YRT rates for conversions
372
Q

How do reinsurance treaties cover term conversions in permanent plan reinsurance pricing?

A
  • May already be covered if also reinsure term plans
  • Future future conversions, 3 areas of uncertainty: mortality, volume, mix
  • Solutions:
    • Separate YRT scale for converted policies
    • Single, substantially loaded YRT scale
373
Q

Describe how to develop point-in-scale mortality rates

A
  • First, calculate the following A/E ratios:
    • Converted A/E
    • Level Period A/E
  • Can be count- or amount-based
  • PISM ratios = ratios of the above A/E ratios:

Converted A/E / Level Period A/E

  • level period experience is a proxy for true permanent policy experience
374
Q

Describe key patterns in conversion rates uncovered by the SOA’s study of conversion experience

A
  • Term conversion rates are highest at end of level period
    • Late duration conversion rate ≈ 5x first year
      • Under 1% in LP, then jump to 5% late in LP
    • Larger policies are more likely to convert
375
Q

Describe key patterns in post-conversion mortality uncovered by the SOA’s study of conversion experience

A
  • Post-conversion mortality is much higher for late duration converters
    • PISM ratios are 180–220% in the first year after conversion
    • Mortality is higher for large policies (PISM by amount > PISM by count)
    • PISM ratios decline but stay above 100% (anti-selection reduces)
376
Q

Describe key patterns in post-conversion lapse rates uncovered by the SOA’s study of conversion experience

A
  • Highest in the initial durations after conversion (4–6%)
  • Late converters have lower lapse rates than early or mid
377
Q

Describe the importance of a grace period adjustments in a lapse or conversion study

A
  • 30–60 day period after premium due date
  • Policyholder can still make past due payment without lapsing
  • Without adjustments, lapses will spike in month 2 of each policy year
  • Adjustment moves the lapse back to premium due date
  • Results in higher proportion of lapses in month 12 of each policy year
  • Same concept applies to conversions
378
Q

List 3 philosophies for covering the cost of conversions

A
  1. Reflect conversion cost in term pricing
  2. Develop separate permanent premiums for conversions
  3. Reflect extra conversion mortality in permanent policy pricing
379
Q

Describe philosophy covering the cost of conversions:

  • Reflect extra conversion mortality in permanent policy pricing
A
  • Might be appealing if insurer has mature experience
  • Can make permanent mortality artificially high
380
Q

Describe philosophy covering the cost of conversions:

  • Develop separate permanent premiums for conversions
A
  • Avoids cross-subsidization but is administratively challenging
  • Alternatively, could reinsure conversions using YRT PISM rates
381
Q

Describe philosophy covering the cost of conversions:

  • Reflect conversion cost in term pricing
A
  • (most common)
  • This is what the author of this article advocates
  • Allows reserves to build for conversions
  • Aligns revenue with the source of risk (term “swaption”)
382
Q

Describe the two stage model for pricing term conversions

A
  1. At issue, determine the expected extra mortality cost for term conversions
  2. Calculate the charge for term policyholders
383
Q

At issue, how do you determine the expected extra mortality cost for term conversions?

A

For each year when a conversion is possible calculate:

Extra NSP for Conversions = Kx,r = PVFBConverted - PVFBStandard WL

= reserve for conversions at duration r

384
Q

How do you calculate the level conversion charge for term policyholders?

A

Ax,r = rpx x ex,r x Kx,r x vr

Total PV of Expected excess mortality = Σ Ax,r

Level Conversion Charge = (Total PVFB of Excess Mortality / äx:n)

385
Q

Describe factors that cause the cost of conversions to vary

A
  • Conversion cost increases when base mortality increases
    • Higher issue ages
    • Males (compared to females)
    • Smokers (compared to non-smokers)
    • Non-preferred (compared to preferred classes)
386
Q

Describe the relationship between conversion period length and conversion cost

A
  • Restricting the conversion period reduces the cost of conversion
    • Disallowing conversions after year 10, reduces cost roughly 30%
    • Caveat: policyholders will accelerate conversions in response

ILA Track (5 decks)

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