ILA-LPM B

Question 1

List the 6 steps to establish experience assumptions

Answer 1

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

Question 2

3 types of primary assumptions needed for an experience study

Answer 2

1. obligation
2. asset
3. scenario

Question 3

Describe primary types of assumptions needed for an experience study:

• Obligation assumptions

Answer 3

• LIABILITY!
• mortality
• lapse
• expense

Question 4

Describe primary types of assumptions needed for an experience study:

• Asset assumptions

Answer 4

• investment income rate
• capital gains rate
• defaults

Question 5

Describe primary types of assumptions needed for an experience study:

• Scenario assumptions

Answer 5

• deterministic vs. stochastic interest rates
• sensitivity testing

Question 6

How do you determine experience classes for an experience study?

Answer 6

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

Question 7

What are the key principles when deciding complexity?

Answer 7

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

Question 8

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

Answer 8

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

Question 9

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

• Evaluate credibility

Answer 9

• quantity of data
• homogeneity
• reasonableness

Question 10

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

• Evaluate quality of data

Answer 10

• Alternative sources?
• Appropriate? Comprehensive enough?

Question 11

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

• Actual vs. similar experience

Answer 11

• Use actual if available and credible

Question 12

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

• Reflect trends

Answer 12

• Example: mortality improvement

Question 13

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

• Reflect company and external factors

Answer 13

• underwriting
• investment policy
• other business practices

Question 14

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

Answer 14

• static (starting reserves)
• dynamic (projected reserves)

Question 15

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

Answer 15

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

Question 16

How should assumptions be documented after an experience study?

Answer 16

• 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

Question 17

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

Answer 17

1. determine structure
2. analyze experience
3. monitor experience

Question 18

How to analyze experience for a mortality assumption

Answer 18

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

Question 19

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

Answer 19

• 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

Question 20

What is the structure of a mortality assumption

Answer 20

• select and ultimate common
• mortality improvement
• ALB vs. ANB

Question 21

Possible variations in the structure of a mortality assumption

Answer 21

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

Question 22

ANB

Answer 22

• age nearest birthday
• q^ANB(x) = 0.5 x [q^ALB(x) + q^ALB(x-1)]

Question 23

ALB

Answer 23

• age last birthday
• q^ALB(x) = 0.5 x [q^ANB(x) + q^ANB(x+1)]

Question 24

List and decribe the 2 main types of mortality studies

Answer 24

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

Question 25

Which risk classes have separate mortality studies?

Answer 25

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

Question 26

Calendar year exposure equality

Answer 26

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

Question 27

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

Answer 27

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

Question 28

Credibility considerations for a mortality study

Answer 28

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

Question 29

Describe conservation of deaths principle

Answer 29

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

Question 30

anti-selections’s effect on mortality

Answer 30

• 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

Question 31

List at least 4 variations in lapse assumption structure

Answer 31

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

Question 32

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

• Product design

Answer 32

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

Question 33

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

• Premium mode

Answer 33

• lapses generally occur on premium due dates
• flexible premium: assume uniform monthly

Question 34

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

• Policy size

Answer 34

• small policies: high early lapses
• large policies: high later lapses

Question 35

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

• Distribution channel

Answer 35

• brokerage: higher lapses
• agency: higher lapses if agent quality is poor

Question 36

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

• Product type

Answer 36

• Deferred annuities: more sensitive to lapse rates than life insurance

Question 37

Exposure for a lapse study

Answer 37

• 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)

Question 38

List specific types of lapses

Answer 38

• 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)

Question 39

Describe the following aspects of interest rate assumption structure.

• Deterministic vs. Stochastic

Answer 39

Investment Assets:

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

Question 40

Describe the following aspects of interest rate assumption structure.

• Policy Loans

Answer 40

• modeled as assets or negative liabilities
• net of policy loan expenses
• utilization rate

Question 41

Formula for determining interest rate on a book value basis

Answer 41

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

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

A = BoY

B = EoY

C = net CFs (assume mid-year)

Question 42

Formula for determining interest rates on a market value basis

Answer 42

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

If done daily, set C = 0

A = BoY

B = EoY

C = net CFs (assume mid-year)

Question 43

Direct vs. indirect expenses

Answer 43

• 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

Question 44

Describe how exposure is determined in an expense study

Answer 44

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

Question 45

4 methods for allocating expenses

Answer 45

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)

Question 46

Define “cell” in terms of an experience study

Answer 46

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

Question 47

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

• decrement rates vs. utilization rates

Answer 47

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

Question 48

Decribe amount-weighted studies, for grouped amount weights

Answer 48

• l_x terms become l_x x avg DB in force
• d_x terms become d_x z avg DB paid on death
• w_x terms become w_x x avg DB on withdrawn policies
• sum amount exposures and calculate q_x’s in the usual way

Question 49

Describe amount-weighted studies, for individual amount weights

Answer 49

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

Question 50

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

Answer 50

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

Question 51

Uses of an A/E analysis

Answer 51

• 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 q_x^e
• valuation, risk management, financial planning, etc.

Question 52

Frequency and severity formulas

Answer 52

• f_x = n_x/E_x = avg claim frequency*
• s_x = C_x/n_x = avg claim amount*
• n_x = # of claims incurred at age x
• C_x = total claim amount incurred at age x
• E_x = central exposure (or initial)

Question 53

Experience study calculations:

• Average withdrawal taken as a % of the max withdrawal formula

Answer 53

• u_x = s_x/M_x*
• M_x* = max w/d allowed
• s_x *= average size (severity) of w/d

Question 54

Experience study calculations:

• Average size (severity) of withdrawals taken formula

Answer 54

• s_x = W_x/n_x*
• W_x* = total actual w/d amount
• n_x* = # of contracts who took a w/d

Question 55

Experience study calculations:

• Withdrawal frequency formula

Answer 55

• f_x = n_x/E_x*
• E_x* = count-based exposure (deaths and lapses)
• n_x* = # of contracts who took a w/d

Question 56

Experience study calculations:

• Count-based exposure (excludes deaths and lapses)

Answer 56

• E_x = l_x - d_x - w_x*
• w_x *= # of lapses

Question 57

Experience study calculations:

• Distortions caused by amount-weighted calculations

Answer 57

• 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

Question 58

Experience study calculations:

• List advantages of mutli-year studies

Answer 58

• higher credibility
• less distortion cause by reporting lags
• populate many more cells
• allow study of trends for study-year/CY

Question 59

Experience study calculations:

• 4 examples of reporting lags

Answer 59

• 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

Question 60

Experience study calculations:

• 2 possible solutions to reporting lags

Answer 60

• wait a few months after study period before gathering data
• IBNR claim estimates from previous studies

Question 61

Experience study calculations:

• 3 examples of non-uniform events

Answer 61

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

Question 62

Experience study calculations:

• 3 ways to compensate for non-uniform distributions

Answer 62

1. shorter interval
2. adjustment factors
3. constant force mortality for old ages

Question 63

Considerations for inclusion of partial policy years in decrement study

Answer 63

• 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

Question 64

Describe the distributed exposure method

Answer 64

• 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)

Question 65

Describe the annual exposure method

Answer 65

• 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

Question 66

Algorithm for calculating distributed exposure

Answer 66

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

Question 67

product-related considerations that may affect experience study calculations

Answer 67

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

Question 68

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

• Deaths in premium grace period (30–60 days)

Answer 68

• actual DB paid is net of overdue premiums, but use full DB in mortality study
• expose lapses to beginning of grace period

Question 69

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

• Study variables changing over time

Answer 69

• admin system changes
• may change plan codes or billing frequencies (distorts exposure)

Question 70

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

• Compromised and denied claims

Answer 70

• don’t count as death claims in mortality study

Question 71

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

• Reinsured amounts

Answer 71

• can study amounts net of reinsurance if material enough

Question 72

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

• Substandard and uninsurable lives

Answer 72

• typically excluded

Question 73

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

Answer 73

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

Question 74

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

• Benefit utilization rate studies

Answer 74

(LTCI only)

Benefit Utilization Rate = Actual Benefits / Max Benefits

Question 75

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

• Claim termination studies

Answer 75

• 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

Question 76

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

• Claim severity studies

Answer 76

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

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

Question 77

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

• Claim incidence studies

Answer 77

• claim incidence rates = annual probabilities
• usually done on “all claims”
• usually based on annual exposure method

Question 78

List 4 DI-specific considerations for experience studies

Answer 78

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

Question 79

List 5 LTCI-specific considerations for experience studies

Answer 79

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

Question 80

Experience study considerations for deferred annuities

Answer 80

• 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

Question 81

Describe experience study considerations for payout annuities:

• Structured settlements

Answer 81

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

Question 82

Describe experience study considerations for payout annuities:

• immediate annuities

Answer 82

• 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

Question 83

6 major steps in table development

Answer 83

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

Question 84

List 4 preliminary activities of data development and analysis

Answer 84

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

Question 85

Define and describe data call process

Answer 85

• 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

Question 86

List 6 common data challenges for table development

Answer 86

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

Question 87

List 6 components in an experience study calculation

Answer 87

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 (q_x’s, etc.)
5. expected results (A/E ratios)
6. summary results for each record (event counts and amounts)

Question 88

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

Answer 88

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

Question 89

List 8 steps in data analysis process (table development)

Answer 89

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!

Question 90

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

Answer 90

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!

Question 91

Describe the purpose of graduation

Answer 91

• 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

Question 92

List properties of a preferred graduation method

Answer 92

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

Question 93

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

Answer 93

• 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

Question 94

Describe the 4 steps of the graduation process

Answer 94

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

Question 95

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

Answer 95

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

Question 96

Describe common interpolation methods for table development

Answer 96

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

Question 97

Describe 3 methods for extending rates in a table

Answer 97

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

Question 98

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

Answer 98

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

Question 99

List the 5 componencts of enforcement reviews

Answer 99

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

Question 100

Describe 4 considerations for projecting future rates

Answer 100

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

Question 101

3 different types of projections of future rates

Answer 101

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

Question 102

Describe items to consider when assessing financial impact of a table

Answer 102

• 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

Question 103

Describe 5 ways to create additional tables from final table rates

Answer 103

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

Question 104

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

Answer 104

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)

Question 105

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

Answer 105

• 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

Question 106

Describe limited fluctuation credibility method

Answer 106

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

Question 107

Describe properties of Bühlmann Empirical Bayesian Method

Answer 107

• 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

Question 108

Compare LFM and Buhlmann credibility methods

Answer 108

• 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

Question 109

What are advantages of traditional approach to experience studies

Answer 109

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

Question 110

List 7 key steps in predictive analytics project

Answer 110

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

Question 111

List the key steps in a predictive analytics project:

• Data Collection and Validation (most critical step)

Answer 111

• clean data and consider variables that may not be obvious

Question 112

List the key steps in a predictive analytics project:

• Model Building

Answer 112

• multiplicative or additive
• statistical tests, business knowledge, and interactions to include

Question 113

List the key steps in a predictive analytics project:

• Model Validation

Answer 113

• compare A/E ratios
• use hold-out samples

Question 114

List the key steps in a predictive analytics project:

• Initial Factor Analysis

Answer 114

• do univariate analysis
• then remove highly correlated variables

Question 115

List the key steps in a predictive analytics project:

• Final Calibration

Answer 115

• refit model using all data and make final adjustments

Question 116

List the key steps in a predictive analytics project:

• Implementation

Answer 116

• use model results to set assumptions, do underwriting, etc.

Question 117

List the key steps in a predictive analytics project:

• Project Scope

Answer 117

• (target variable, LOB, timeline, resources)
• Assemble team of technical and business experts

Question 118

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

Answer 118

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

Question 119

List general advantages of predictive analytics for experience studies

Answer 119

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

Question 120

List new business valuation advantages of predictive analytics for experience studies

Answer 120

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

Question 121

Disadvantages of predictive analytics for experience studies

Answer 121

• significant expertise required
• risks: misinterpret data and flawed models

Question 122

Limitations of predictive analytics (and traditional) in experience studies

Answer 122

• 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

Question 123

List 4 main steps for building a predictive model

Answer 123

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

Question 124

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

Answer 124

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

Question 125

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

Answer 125

• 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

Question 126

List 4 main steps to prepare data for a predictive model

Answer 126

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

Question 127

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

• Generate variables

Answer 127

• synthetic vs. disease-state

Question 128

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

• Exploratory Data Analysis

Answer 128

• distributional vs. univariate analysis

Question 129

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

• Variable Transformation

Answer 129

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

Question 130

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

• Partition Data Into 3 Sets

Answer 130

1. Train
2. Validation
3. Test

Question 131

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

Answer 131

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

Question 132

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

• Anti-selection concerns

Answer 132

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)

Question 133

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

• Legal and ethical concerns

Answer 133

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

Question 134

Explain why credit data is useful for mortality prediction

Answer 134

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

Question 135

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

Answer 135

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

Question 136

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

Answer 136

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

Question 137

List criteria for using credit data for UW purposes

Answer 137

• 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

Question 138

List the credit attributes that influence the TrueRisk Life score

Answer 138

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

Question 139

Describe the data used to develop the TrueRisk Life model

• Credit archive data were divided into 3 groups

Answer 139

• 44M: training data used to create model w/ multivariate logistic regression
• 30M: test/validation data to prevent overfitting
• 18M: holdout data for mortality validation

Question 140

Describe the data used to develop the TrueRisk Life model

• Data sources

Answer 140

• 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)

Question 141

Characteristics needed for final variables in predictive analysis

Answer 141

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

Question 142

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

Answer 142

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

Question 143

How does age affect a TrueRisk Life score?

Answer 143

• 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

Question 144

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

Answer 144

• 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

Question 145

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

Answer 145

• 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

Question 146

Review the FCRA Regulations on the back of this card

Answer 146

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

Question 147

Describe the LexisNexis Risk Classifier methodology

Answer 147

• 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

Question 148

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

• Mortality risk within age, gender, and duration

Answer 148

• 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

Question 149

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

• Public records

Answer 149

• 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

Question 150

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

• Wealth

Answer 150

• Risk Classifier scores generally increase with wealth

Question 151

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

• General population mortality risk

Answer 151

• 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+)

Question 152

List 4 possible methods for ending a mortality table

Answer 152

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

Question 153

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

• Less-Than-One Method

Answer 153

• Choose an ultimate age
• No changes to other qx ’s
• At ultimate age, qx may be 1.000

Question 154

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

• Pattern Method

Answer 154

• qx ’s continue normally until they reach 1.000
• Age where qx = 1.000 is ultimate age

Question 155

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

• Blended Method

Answer 155

• Choose ultimate age
• Grade qx ’s to 1.000 at ultimate age
• i.e. “blend” the qx ’s to 1.000

Question 156

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

• Forced Method

Answer 156

• Choose ultimate age
• Set ultimate qx = 1.000 at ultimate age
• No changes to other qx ’s

Question 157

Describe difficulties when ending the mortality table

Answer 157

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

Question 158

Describe slope considerations when ending the mortality table

Answer 158

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

Question 159

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

Answer 159

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

Question 160

Explain the importance of understanding policyholder behavior

Answer 160

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

Question 161

How does policyholder behavior affect many areas of insurance operations?

Answer 161

• 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

Question 162

List 5 challenges in understanding policyholder behavior

Answer 162

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

Question 163

Describe 5 challenges in understanding policyholder behavior:

• Human decision making is not fully understood

Answer 163

• (but improving)
• Behavioral economics, Big Data, computing power, dynamic modeling

Question 164

Describe 5 challenges in understanding policyholder behavior:

• Data challenges

Answer 164

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

Question 165

Describe 5 challenges in understanding policyholder behavior:

• Curse of knowledge

Answer 165

• Significant gap between policyholder and actuarial product views

Question 166

Describe 5 challenges in understanding policyholder behavior:

• Information asymmetry

Answer 166

• Insurers have incomplete information about policyholder
• Policyholders have incomplete understanding of products

Question 167

Describe 5 challenges in understanding policyholder behavior:

• Lack of time

Answer 167

Time is spent preparing data, not analyzing it

Question 168

Describe how data sources for policyholder behavior assumptions vary

Answer 168

• 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

Question 169

Describe dynamic policyholder behavior assumptions

Answer 169

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

Question 170

List common dynamic policyholder behavior assumptions

Answer 170

• 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

Question 171

Describe static policyholder behavior assumptions

Answer 171

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

Question 172

Define financial efficiency

Answer 172

how optimally policyholders maximize their contract values

Question 173

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

Answer 173

• 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

Question 174

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

Answer 174

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

Question 175

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

Answer 175

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

Question 176

Top policyholder behavior modeling challenge

Answer 176

Data availability and credibility

Question 177

Describe policyholder behavior modeling techniques

Answer 177

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

Question 178

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

• modeling

Answer 178

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

Question 179

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

• Data collection, analysis, and assumption setting

Answer 179

• 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

Question 180

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

• Governance Process Steps

Answer 180

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

Question 181

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

• Validation

Answer 181

• Establish formal process
• Document changes
• “Model Sherriff”

Question 182

List benefits of utilizing behavioral economics in policyholder behavior assumptions

Answer 182

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

Question 183

List 5 decision shortcuts (heuristics)

Answer 183

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

Question 184

List and define 5 decision shortcuts (heuristics):

• Status quo bias

Answer 184

people tend to stick with prior choices

Question 185

List and define 5 decision shortcuts (heuristics):

• Framing

Answer 185

how choices are labeled can influence consumer decisions

Question 186

List and define 5 decision shortcuts (heuristics):

• Mental accounting

Answer 186

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

Question 187

List and define 5 decision shortcuts (heuristics):

• Reliance on defaults

Answer 187

tendency to rely on assumptions set in the past

Question 188

List and define 5 decision shortcuts (heuristics):

• Relative choices

Answer 188

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

Question 189

List 4 value assessments (PH behavior)

Answer 189

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

Question 190

List and define 4 value assessments:

• Hyperbolic discounting

Answer 190

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

Question 191

List and define 4 value assessments:

• Endowment effect

Answer 191

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

Question 192

List and define 4 value assessments:

• Anchoring

Answer 192

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

Question 193

List and define 4 value assessments:

• Love of free

Answer 193

people have a disproportionate love of free things

Question 194

List 6 emotional impacts

Answer 194

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

Question 195

List and define 6 emotional impacts

• Self-herding

Answer 195

future decisions may be made to justify past behaviors

Question 196

List and define 6 emotional impacts

• Hot and cold states

Answer 196

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

Question 197

List and define 6 emotional impacts

• Self-control facilitation

Answer 197

willingness to accept less freedom in order to facilitate saving

Question 198

List and define 6 emotional impacts

• Loss aversion

Answer 198

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

Question 199

List and define 6 emotional impacts

• Overconfidence

Answer 199

overestimating one’s ability to make correct choices

Question 200

List and define 6 emotional impacts

• Risk aversion

Answer 200

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

Question 201

Define the bandwagon effect (social impact)

Answer 201

people do what they see other people do

Question 202

List 4 behavioral economics principles that affect life insurance purchasing behaviors

Answer 202

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

Question 203

Describe behavioral economics principles that affect life insurance purchasing
behaviors

• Social Impacts

Answer 203

• “Masculine” societies buy more life insurance than “feminine” societies

Question 204

Describe behavioral economics principles that affect life insurance purchasing
behaviors

• Emotional Impacts

Answer 204

• 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

Question 205

Emotional Impacts - solutions for insurers

Answer 205

• 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”)

Question 206

Describe behavioral economics principles that affect life insurance purchasing
behaviors

• Value Assessment

Answer 206

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

Question 207

Describe behavioral economics principles that affect life insurance purchasing
behaviors

• Decision Shortcuts

Answer 207

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

Question 208

Describe behavioral economics principles that affect annuity purchasing behaviors:

• Decision Shortcuts

Answer 208

• 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

Question 209

Describe behavioral economics principles that affect annuity purchasing behaviors:

• Value Assessment

Answer 209

• 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

Question 210

Describe behavioral economics principles that affect annuity purchasing behaviors:

• Social Impacts

Answer 210

• need to minimize the association between profit and death

Question 211

Describe behavioral economics principles that affect annuity purchasing behaviors:

• Emotional Impacts

Answer 211

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

Question 212

Describe 2 actuarial academic lapse theories

Answer 212

• 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

Question 213

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

• Mental accounting

Answer 213

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

Question 214

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

• Relative choices

Answer 214

• 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

Question 215

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

Answer 215

• “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”

Question 216

Emotional component of life and annuity products

Answer 216

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

Question 217

How do emotional impacts impact policyholders’ decision to lapse?

• Hot and cold states

Answer 217

• Anxiety may influence lapse and withdrawal decisions
• Marketing and other communications could trigger hot states

Question 218

How do emotional impacts impact policyholders’ decision to lapse?

• Self-control facilitation

Answer 218

• Some European countries require higher CSVs
• Increasing policyholder options may not be in policyholders’ best interests

Question 219

How do emotional impacts impact policyholders’ decision to lapse?

• Loss aversion

Answer 219

• 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

Question 220

How do emotional impacts impact policyholders’ decision to lapse?

• Risk aversion

Answer 220

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

Question 221

How do social connections impact policyholders’ decision to lapse?

Answer 221

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

Question 222

How do decision shortcuts affect premium and funding levels?

• Reliance on defaults

Answer 222

• Policyholders. . .
  
  • May follow target premium illustrations
  • Tend to pay same premium as last period
• Many actuaries assume recent patterns experience will continue

Question 223

How do decision shortcuts affect premium and funding levels?

• Status quo bias

Answer 223

• Increases policyholder inertia and procrastination

Question 224

How do value assessments affect premium and funding levels?

• Anchoring

Answer 224

• Numbers in marketing materials may influence funding
• Avoid unintended anchoring in communications

Question 225

How do value assessments affect premium and funding levels?

• Hyperbolic discounting

Answer 225

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

Question 226

How does self-herding affect premium and funding levels?

Answer 226

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

Question 227

How do decision shortcuts affect the timing of withdrawals?

• Mental accounting

Answer 227

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

Question 228

How do decision shortcuts affect the timing of withdrawals?

• Reliance on defaults

Answer 228

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

Question 229

How doese overconfidence affect the timing of withdrawals?

Answer 229

• 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

Question 230

How do decision shortcuts affect the amount of withdrawals?

• Mental accounting

Answer 230

• 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

Question 231

How do decision shortcuts affect the amount of withdrawals?

• Reliance on defaults

Answer 231

• Withdrawals may be based on amounts endorsed by experts or social norms

Question 232

How do value assessments affect the amount of withdrawals?

• Anchoring

Answer 232

• Withdrawals may be influenced by irrelevant numbers shown to policyholders

Question 233

How do relative choices affect investment decisions and fund transfers?

Answer 233

• “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

Question 234

How does framing affect investment decisions and fund transfers?

Answer 234

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

Question 235

How does mental accounting affect investment decisions and fund transfers?

Answer 235

• Policyholders may allocate “old money” vs. new money

Question 236

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

Answer 236

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

Question 237

How does procrastination affect investment decisions and fund transfers?

Answer 237

• 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

Question 238

How does risk aversion impact investment decisions and fund transfers?

Answer 238

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

Question 239

How does loss aversion impact investment decisions and fund transfers?

Answer 239

• 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

Question 240

How does overconfidence impact investment decisions and fund transfers?

Answer 240

• 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

Question 241

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

Answer 241

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

Question 242

What social impacts affect investment decisions & fund transfers?

Answer 242

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

Question 243

How is annuitization affected by decision shortcuts?

• Reliance on defaults

Answer 243

• Annuitization usually requires an active decision (opt-in)
• Insurers could make annuitization a default option

Question 244

How is annuitization affected by decision shortcuts?

• Framing

Answer 244

• 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

Question 245

How is annuitization affected by emotional impacts?

• Risk aversion

Answer 245

Certain income streams create mental ease

Question 246

How is annuitization affected by emotional impacts?

• Loss aversion

Answer 246

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

Question 247

Describe drawbacks of the traditional actuarial approach to modeling policyholder behavior

Answer 247

• 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

Question 248

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

Answer 248

• 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

Question 249

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

Answer 249

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

Question 250

Describe possible approaches with the most potential for policyholder behavior modeling

Answer 250

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

Question 251

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

Answer 251

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

Question 252

Profitability test with zero lapse rate or under illustration rules

Answer 252

• 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

Question 253

Common features of lapse-supported insurance

Answer 253

• guaranteed level premiums
• low/zero CSV
• high late benefits

Question 254

What happens when lapse-supported insurance premiums are too low

Answer 254

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

Question 255

Where is the risk in lapse-supported insurance?

Answer 255

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

Question 256

What is the highest risk for lapse-supported insurance?

Answer 256

• 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

Question 257

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

Answer 257

• 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

Question 258

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

Answer 258

• 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

Question 259

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

Answer 259

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

Question 260

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

Answer 260

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

Question 261

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

Answer 261

• Dynamic premium persistency assumptions
• Sensitivity testing

*few companies currently doing

Question 262

Describe a viatical settlement and the viatical market

Answer 262

• 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)

Question 263

Describe the traditional life settlement market

Answer 263

• 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

Question 264

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

Answer 264

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

Question 265

List 3 forms of premium financing life settlements

Answer 265

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

Question 266

Describe 3 forms of premium financing life settlements

• Stranger-Owned Life Insurance

Answer 266

• Works like charity-owned

Question 267

Describe 3 forms of premium financing life settlements

• Charity-Owned Life Insurance

Answer 267

• 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

Question 268

Describe 3 forms of premium financing life settlements

• Insured Borrows Money To Buy Policy

Answer 268

Wait 2 years, then policyholder can. . .

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

Question 269

What is another proposed solution to life settlements?

Answer 269

• Pay policyholders a higher CSV based on updated underwriting

Question 270

How have insurers have responded to life settlements?

Answer 270

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

Question 271

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

Answer 271

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

Question 272

How will life settlements impact pricing?

Answer 272

• 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

Question 273

Describe key variations in life expectancy data related to life settlements

Answer 273

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

Question 274

Difference between standard and impaired life expectancy

Answer 274

• difference falls with age
• difference has been falling overall with time

Question 275

Why standard and impaired LEs in life settlements have been converging

Answer 275

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

Question 276

Describe the typical types of life insurance involved in life settlements

Answer 276

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

Question 277

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

Answer 277

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

Question 278

Ways simplified life settlements are gaining popularity

Answer 278

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

Question 279

How is the expected IRR of a life settlement determined?

Answer 279

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

Question 280

average expected IRR of a life settlement

Answer 280

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

Question 281

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

Answer 281

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

Question 282

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

• H2: Higher retained DB ⇒ higher expected IRR

Answer 282

• “Continued demand hypothesis”
• Policyholders are willing to accept lower offers to retain more DB
• Lower price ⇒ higher IRR for investor

Question 283

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

• H1: Higher retained DB ⇒ lower expected IRR

Answer 283

• 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

Question 284

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

Answer 284

• 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!

Question 285

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

• HighNDB

Answer 285

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

Question 286

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

• LEest2, LEest3–4

Answer 286

• ß Value = -0.0055, -0.0044
• Interpretation: IRR is lower if have more than 1 LE estimate
  (supports H6)

Question 287

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

• SLS

Answer 287

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

Question 288

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

• Convexity

Answer 288

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

Question 289

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

• RDB/NDB

Answer 289

• ß Value = 0.11 to 0.14
• Interpretation: Presence of retained DBs increases IRR
  (supports H2, not H1)

Question 290

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

• MediumNDB

Answer 290

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

Question 291

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

Answer 291

• 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

Question 292

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

Answer 292

• 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

Question 293

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

Answer 293

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

Question 294

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

• Surrender rates

Answer 294

• 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)

Question 295

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

• Amount of withdrawal taken

Answer 295

• 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

Question 296

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

• Method of withdrawals

Answer 296

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

Question 297

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

• When withdrawals start

Answer 297

• 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)

Question 298

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

Answer 298

• 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

Question 299

Product design of VA GLBs

• GMWB

Answer 299

• 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

Question 300

Product design of VA GLBs

• GMAB

Answer 300

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

Question 301

Product design of VA GLBs

• GMIB

Answer 301

• 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

Question 302

Product design of VA GLBs

• GLWB

Answer 302

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

Question 303

Describe the purpose and scope of ASOP 54

Answer 303

• 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

Question 304

Describe initial pricing considerations under ASOP 54

• Relevant Characteristics of the Product

Answer 304

• 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

Question 305

Describe initial pricing considerations under ASOP 54

• Criteria of the Actuary’s Principal

Answer 305

• 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)

Question 306

Describe considerations for selecting profitability metrics under ASOP 54

Answer 306

• 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

Question 307

List model development and selection considerations under ASOP 54

Answer 307

• 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

Question 308

List model development and selection considerations under ASOP 54

• Time horizon

Answer 308

• sufficient to capture profitability and risks

Question 309

List model development and selection considerations under ASOP 54

• Granularity

Answer 309

• include necessary detail (time intervals, assumptions, etc.)

Question 310

List model development and selection considerations under ASOP 54

• Dynamic assumptions

Answer 310

• policyholder behavior, etc.

Question 311

List model development and selection considerations under ASOP 54

• Asset returns

Answer 311

• should be consistent with company practice

Question 312

List model development and selection considerations under ASOP 54

• Economic scenarios

Answer 312

• (MC or RW) – represent an appropriate range

Question 313

List model development and selection considerations under ASOP 54

• Accounting and actuarial bases

Answer 313

• statutory, GAAP, tax, etc.

Question 314

List model development and selection considerations under ASOP 54

• Risk capital framework

Answer 314

• reflect the framework used in practice

Question 315

List model development and selection considerations under ASOP 54

• Taxes

Answer 315

• model should be based on expected tax structure and related items

Question 316

List model development and selection considerations under ASOP 54

• Risk evaluation

Answer 316

• model should evaluate risks appropriately

Question 317

List model development and selection considerations under ASOP 54

• Risk mitigation

Answer 317

• reflect strategies that will support the product

Question 318

List model development and selection considerations under ASOP 54

• Model validation

Answer 318

• model should be sufficiently transparent

Question 319

Describe pricing assumption considerations under ASOP 54

Answer 319

• 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

Question 320

Specific assumption setting considerations under ASOP 54

Answer 320

• 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

Question 321

Describe risk evaluation considerations when performing profitability analysis under ASOP 54

Answer 321

• 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

Question 322

Describe governance and control considerations under ASOP 54

Answer 322

*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

Question 323

List actuarial communications required by ASOP 54

Answer 323

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

Question 324

Which ASOP is for general guidance on actuarial communications

Answer 324

ASOP 41

Question 325

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

• Effective Date

Answer 325

• actuarial work with an information date >= 12 months after this ASOP is adopted by the ASB

Question 326

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

• Scope

Answer 326

• 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

Question 327

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

• Purpose

Answer 327

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

Question 328

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

Answer 328

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

Question 329

List ways to assess reasonableness of assumptions

Answer 329

• 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

Question 330

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

Answer 330

• 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.)

Question 331

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

• Prescribed assumptions set by law

Answer 331

• disclose but don’t assess

Question 332

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

• Assumptions set by others

Answer 332

• assess reasonableness
• follow Precept 8 of the Code of Professional Conduct (don’t mislead!)

Question 333

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

• Reliance on others

Answer 333

• 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

Question 334

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

• Alternative assumptions

Answer 334

• Evaluate the alternative assumptions using sensitivity analysis

Question 335

What actuarial communications are required for assumption setting?

Answer 335

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

Question 336

List challenges associated with developing mortality assumptions

Answer 336

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

Question 337

Dukes MacDonald (D-M) method

Answer 337

• n% of reverters are fully select

Question 338

Becker-Kitsos (B-K)

Answer 338

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

Question 339

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

Answer 339

• 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

Question 340

Describe the following with respect to term mortality and lapses.

• Recommended Sensitivity Tests

Answer 340

• Vary total and excess lapse rates assumed
• Vary parameters in D-M and B-K approaches
• Assume no profits after level premium period

Question 341

Describe the following with respect to term mortality and lapses.

• Disadvantages of Not Offering Post-Level Term Coverage

Answer 341

• Loss of profit from high premiums paid by persisters
• Lower GAAP income
• Not as marketable to policyholders

Question 342

Describe the following with respect to term mortality and lapses.

• Limitations of D-M and B-K

Answer 342

• No classification by underwriting class
• Ignores premium size in ART period
• Assumes 100% rational policyholder behavior

Question 343

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

Answer 343

• 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

Question 344

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

Answer 344

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

Question 345

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.”

Answer 345

• 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

Question 346

Describe how early post-level term experience has emerged

Answer 346

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

Question 347

Possible explanations for lower post-level term lapses

Answer 347

• 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)

Question 348

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

Answer 348

• 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

Question 349

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

• The Approach

Answer 349

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

Question 350

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

• Advantages

Answer 350

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

Question 351

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

• Disadvantages

Answer 351

• 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

Question 352

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

• The Approach

Answer 352

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

Question 353

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

• Advantages

Answer 353

• 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

Question 354

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

• Disadvantages

Answer 354

• 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?

Question 355

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

• The Approach

Answer 355

• 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)

Question 356

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

• Advantages

Answer 356

• 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

Question 357

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

• Disadvantages

Answer 357

• 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

Question 358

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

Answer 358

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

Question 359

Describe the Dukes-MacDonald selective lapsation model

Answer 359

• 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

Question 360

Give a formula for Conservation of Deaths.

Answer 360

q^norm = q^sel x w^eff + q^norm x (w^non-eff + w^base) + q^pers x (1 - w)

q^sel = select issue age mortality

q^norm ​= normal, point-in-scale mortality

q^pers ​= 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 - w^base

w^eff ​ = “effective reverters” = effectiveness rate x w^shock

w^non-eff ​ = non-effective reverters = w^shock - w^eff

Question 361

Describe important considerations for setting a post-level mortality assumption

Answer 361

• 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

Question 362

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

Answer 362

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

Question 363

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

Answer 363

• *Lower lapses for. . .**
  1. Older issue ages
  2. Preferred classes
  3. Non-smokers
  4. Joint products

Male lapses ≈ female lapses

Question 364

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

Answer 364

• 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

Question 365

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

Answer 365

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

Question 366

Describe challenges with term conversions

Answer 366

• Term conversions have a higher mortality cost due to anti-selection: q^c_{[x] + t} > q^s_{x + 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

Question 367

Describe solutions with term conversions

Answer 367

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

Question 368

Describe price and competitive pressures with term conversions

Answer 368

• 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?

Question 369

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

Answer 369

• 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)

Question 370

How do direct writers reflect conversions in pricing?

Answer 370

• 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

Question 371

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

Answer 371

• 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

Question 372

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

Answer 372

• 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

Question 373

Describe how to develop point-in-scale mortality rates

Answer 373

• 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

Question 374

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

Answer 374

• 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

Question 375

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

Answer 375

• 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)

Question 376

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

Answer 376

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

Question 377

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

Answer 377

• 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

Question 378

List 3 philosophies for covering the cost of conversions

Answer 378

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

Question 379

Describe philosophy covering the cost of conversions:

• Reflect extra conversion mortality in permanent policy pricing

Answer 379

• Might be appealing if insurer has mature experience
• Can make permanent mortality artificially high

Question 380

Describe philosophy covering the cost of conversions:

• Develop separate permanent premiums for conversions

Answer 380

• Avoids cross-subsidization but is administratively challenging
• Alternatively, could reinsure conversions using YRT PISM rates

Question 381

Describe philosophy covering the cost of conversions:

• Reflect conversion cost in term pricing

Answer 381

• (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”)

Question 382

Describe the two stage model for pricing term conversions

Answer 382

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

Question 383

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

Answer 383

For each year when a conversion is possible calculate:

Extra NSP for Conversions = K_x,r = PVFB^Converted - PVFB^{Standard WL}

= reserve for conversions at duration r

Question 384

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

Answer 384

A_x,r = _rp_x x e_x,r x K_x,r x v^r

Total PV of Expected excess mortality = Σ A_x,r

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

Question 385

Describe factors that cause the cost of conversions to vary

Answer 385

• 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)

Question 386

Describe the relationship between conversion period length and conversion cost

Answer 386

• 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