Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA

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Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting

Moderator:

David Wang, FSA, FIA, MAAA

Presenters:

Guillaume Briere-Giroux, FSA, MAAA Eileen Sheila Burns, FSA, MAAA

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Presented by

Eileen S. Burns, FSA, MAAA

Milliman

October 27, 2014

Integrating Predictive Modeling in

Assumption Setting

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What makes predictive modeling different?

 Results

– More granularity – Quantifiable goodness-of-fit

 Foundations

– Different tools – More data prep

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BUILDING A SOLID

FOUNDATION

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Human and physical capital

 Decision #1: Project staff

– Actuary – Statistician

 Supporting staff

 Decision #2: Program

– Comfort – Flexibility – Processing efficiency – Cost

 Computing resources

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Exploratory data analysis

 Programmer, statistician, and actuary

 Data visualization tools

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How is each field distributed? Is it consistent over time?

Which variables are related to the target variable, and how?

Data visualization – on raw and prepped data

Policy value Policy value Category Category Time Time

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Example 1: Gender

Problem: Missing

extreme number of

genders, male and

female counts

unexpected

Solution: Code

problem referencing

secondary insured

by mistake (gender2

vs. gender field)

MALE FEMALE NA

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Example 2: Distribution channel

Problem: Large

number of distribution

channels missing

Solution: No

change. Discovered

issue by looking

across time. Fix is

only available for

policies that don’t

lapse, and would

distort experience.

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Example 2: Distribution channel

Problem: Large

number of distribution

channels missing

Solution: No

change. Discovered

issue by looking

across time. Fix is

only available for

policies that don’t

lapse, and would

distort experience.

08 Q2 08 Q3 09 Q1 08 Q4 09 Q2 09 Q3 09 Q4 10 Q1 10 Q2 A B C NA

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Example 3: Policy status records

08 Q2 08 Q3 09 Q1 08 Q4 09 Q2 09 Q3 09 Q4 10 Q1 10 Q2

Problem: No deaths

in any Q4

Solution: No

change. A data issue

with no feasible

solution. Only affects

scaling and only for a

small percentage of

policies.

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Example 4: Rider prevalence

08 Q2 08 Q3 09 Q1 08 Q4 09 Q2 09 Q3 09 Q4 10 Q1 10 Q2

Problem: no other

rider features seen in

one quarter.

Solution: Analyzed

prior and subsequent

quarters to identify

policies with riders.

Necessary to avoid

modeling policies

with riders as no-rider

policies.

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Example 5: Assessing policy values

Death benefit

Log distribution

Log death benefit

Relative distribution DB/AV % of dist <= 1.00 25% 1.01 - 1.25 50% 1.26 - 2.00 10% 2.01 – 5.00 5% 5.01 – 9.00 5% 9.01+ 5%

Problem: What is a

reasonable

distribution?

Solution: Check log

distribution, and

check relative to

similarly-scaled field.

Led to recognizing

some $4$ benefits,

and some DB coded

as increase over AV.

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Example Recap

 Response variable

– Policy status

 Factor variables

– Rider prevalence – Gender – Distribution channel

 Numeric variables

– Policy value distributions

 Correlations

 Other issues

– Missing entire files

– Files with misleading names (e.g. wrong valuation date) – Inconsistent feature

descriptions (e.g. reset/ratchet) – “unreliable” fields

Takeaway: every variable that is part of a typical

experience analysis has required specific attention to ensure consistency and model validity, as have each of the additional 50+ fields.

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Thank you!

Eileen S. Burns, FSA, MAAA Milliman

eileen.burns at milliman.com 206-504-5955

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Predictive Modeling Case Studies

Liz Olson, FSA, MAAA

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Background

• I am not:

– A statistician

– A model builder

– Responsible for experience studies

• I am:

– An actuary

– Responsible for assumption governance

– Someone who knew there was power in statistics, but spent years

before finding the right statistician to pull it off

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Experience Studies Where We’ve Used Predictive Modeling

• Variable Deferred Annuity Lapses

• Deferred Annuity Mortality

 Fixed Deferred Annuity Lapses

 VUL Life Insurance Mortality

 Life Insurance Renewal Premiums

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Fixed Deferred Annuity Lapses

• Examined Fixed Deferred Annuity lapses using predictive modeling for

the first time in 2013

• Some factors that lapse behavior varied by were:

– Attained Age

– Policy Size

– Effective Surrender Charge

– Difference between credited rate and market rate

– Qual vs. Non-Qual

– Guarantee Minimum Floor Rate

• These drivers also had different sensitivities depending on whether the

policy was in the surrender charge period, in the shock year, or in the post-shock years

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Fixed Deferred Annuity Lapses

• Typical graphs of dynamic parameters

• Predictive modeling allows for better delineation between base and dynamic lapses

Dyn am ic  Fa ct o r Policy Size

In CDSC Shock Post Shock

Smaller Larger Dyn

am ic  Fa ct o r Attained Age

In CDSC Shock Post Shock Younger Older

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Fixed Deferred Annuity Lapses

• Progression of Models – Base Lapses Only

5 La p se  Ra te

Actual Lapse Rates vs. Model Over Time

Actual Model: Base Lapses Only

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Fixed Deferred Annuity Lapses

• Progression of Models – Base and Some Dynamic Parameters

6 La p se  Ra te

Actual Lapse Rates vs. Model Over Time

Actual Model: Base + Some Parameters

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Fixed Deferred Annuity Lapses

• Progression of Models – Final Model

7 La p se  Ra te

Actual Lapse Rates vs. Model Over Time

Actual Model: Final

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Fixed Deferred Annuity Lapses

Lessons Learned

– Just because the model fits the past doesn’t mean it’s good for predicting

the future

– The model told us that for one product the difference between our credited

rate and the market rate didn’t matter for policy durations post shock

• The study had a few years of experience, but all in low rate environment

– Don’t rely on the model but rather use it to inform decision making

– Involve actuarial modelers early on to ensure the dynamic parameters are

implementable

– “Language barrier” between statisticians and actuaries

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VUL Life Insurance Mortality

• We worked with a consulting firm to analyze our VUL mortality

experience using predictive modeling

• In addition to analyzing mortality across the obvious dimensions (age,

gender, underwriting category, etc.) we were also able to gather data from our P&C business or from other sources

– Home size, number of people in household, automobiles

– Interests of individuals in the household (i.e. boating, skiing, scuba, golf, organic foods, interior design, video games…)

• Consultant was able to tease out two profiles with very different

mortality experience

– Profile A: More attributes lead to lower mortality

– Profile B: More attributes lead to higher mortality

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VUL Life Insurance Mortality

Customer Profile A Customer Profile B

0 0.5 1 1.5 0 1 2 3 4+ M o rt alit R e la ti vi ty Number of Attributes of Profile A Relative Mortality by Number of  Profile A Attributes 0 0.5 1 1.5 2 2.5 0 1 2 3 4+ Mo rt al it Re la ti vi ty Number of Attributes of Profile B Relative Mortality by Number of  Profile B Attributes 10

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Life Insurance Renewal Premiums

• New project to use predictive modeling to establish renewal premium

assumptions for ULSG and VUL products

• Could ultimately be used as a retention strategy to proactively notify

customers who the model suggests may have a higher likelihood of stopping or reducing their premiums

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Life Insurance Renewal Premiums

• Some companies may be setting this assumption in a very simplistic

manner (i.e. all policies make renewal premiums such that the aggregate matches expectations)

• This can lead to a sizeable error on many policies

12 Histogram of Actual vs. Predicted  Renewal Premium Error All PHs Pay Average No Error Large Error Large Error Po lic y  C o unt

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Life Insurance Renewal Premiums

• Our new approach using predictive modeling has further improved our

renewal premium assumption

13 Histogram of Actual vs. Predicted  Renewal Premium Error Predictive Modeling Bucketing Algorithm All PHs Pay Average No Error Large Error Large Error Po lic y  C o unt

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Other Possible Areas for Predictive Model Use

• Living Benefit Utilization

• Transfers between Fixed and Variable investments

• Life Insurance Surrenders

• Life Underwriting Decisions

• 401k, 457, 403b Case Lapses and Participant Deferrals

• Sales Distribution Tendencies

– Could possibly identify producer abuse such as STOLI or STOVA

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Data Quality

• ASOP 23 provides insights into what an actuary should do when using

data

– Review data for reasonableness and consistency

– Not required to audit the data

• Some steps that we’ve taken

– Compare to ledger sources (policy count, deaths, lapses, FY

premium, renewal premium, claims)

– Cursory review of distributions (issue age, account value, face

amount, gender, underwriting category)

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Staffing Model

• Pure statisticians often don’t have the product background or

business intuition needed

• Actuaries often don’t have the statistical horsepower needed

• Finding someone with both of these is rare

• Leaders could staff by:

– Using pure statisticians to build the predictive models and

using actuaries to manage the projects and add insights based on their intuition and experiences, or

– Grow the statistical talent by sending actuaries through

extensive statistical training so they can run the predictive modeling project from start to finish

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Summary

• Seemingly endless possible uses of predictive modeling in

experience studies

• Think beyond just using traditional internal data

• Garbage In = Garbage Out

• Build the right team

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Guillaume Briere-Giroux, FSA, MAAA, CFA

Integrating Predictive Analytics in Assumption Setting

Implementation and Integration in Financial Models 2014 SOA Annual Meeting & Exhibit

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Agenda

I. How and where predictive analytics impact assumption setting?

II. Implications for assumption setting process

III. Challenges and solutions for financial modeling integration IV. Key takeaways

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How predictive analytics impact assumption setting?

B u si n ess v a lue

Data analytics literacy

Describe / monitor Analyze / understand Score / predict Decide / optimize / manage Descriptive analytics

(what happened and why?)

Predictive analytics

(what will happen?)

Prescriptive analytics

(what should we do?)

Scope of predictive modeling techniques Enhanced experience studies Enhanced assumption setting Enhanced model-based decisions

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Where predictive analytics impact assumption setting?

Use of predictive modeling is increasingly widespread for experience studies

Product Surrenders / Lapses

Utilization / funding

pattern Mortality Morbidity

VA Living Benefits

FIA Living Benefits

Fixed Annuities

Universal Life

Term

Long Term Care

We are also seeing greater use of predictive analytics in M&A

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Implications for assumption setting process

1

More attention is paid to more secondary internal variables

2

Increased opportunities to test external variables

3

Additional relationships to study and understand

4

More comprehensive ”data-driven” discussions

5

Better ability to put experience in context

In summary, using predictive analytics requires more resources dedicated to assumption setting but enables richer thinking around key assumptions

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Example: Integrated GLWB policyholder behavior cohorts

Cohort of GLWB Observed behavior

“Efficient” users • Utilize 100% of GLWB maximum income • Strong utilization “feature skew”

• Low lapse rate

• More efficient dynamic lapses

“Partial” users • Utilize less than 100% of GLWB maximum income • Weaker utilization skew

• Higher lapse rate than efficient users • Less efficient dynamic lapses

“Excess” users • Utilize more than 100% of GLWB maximum income • Very high lapse rates

• Least efficient dynamic lapses

“Waiting” users • Have not yet utilized • Low lapse rates

• Efficient dynamic lapses • Waiting for rollup?

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Computational implications for modeling

Run time optimization becomes a three dimensional problem

Can specify accuracy functions to determine optimal accuracy for a given run time and generate an efficient frontier

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Other considerations for modeling

How granular should the model become?

1

Materiality and certainty of dynamic

2

Materiality of business

3

Model purpose

4

Degree of buy in

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Model implementation approach

There is a compromise between transparency, flexibility, controls and system performance

Desirable property Parameterized formula Factor tables

Transparency

Flexibility (model form)

Flexibility (adjustments) Ease of control Auditability Computational performance High Low

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Best practices for model implementation

Area of focus Considerations

Parallel testing • New assumptions are more complex to code • Do single cell-testing with replicator (e.g., Excel)

• Excel replicator can also be used for extreme value testing / sensitivity testing

Internal data • Data definition between experience study and financial models must be consistent

External variables • Modeler must understand the sensitivity of projected behavior to external variables (how reliable are my scenarios?)

Sensitivity testing • Understand the potential impact from stress testing the assumption parameters

Documentation • Documentation of rationale for key modeling decisions and any limitations or simplifications

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Key takeaways

1

Think about the business and the environments

2

Think about the models and their end goal

Figure

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References

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