© 2014 Oliver Wyman

Guillaume Briere-Giroux, FSA, MAAA, CFA

Integrating Predictive Analytics in Assumption SettingImplementation and Integration in Financial Models

2014 Valuation Actuary Symposium

New York – August 26, 2014

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Agenda

I. How and where do predictive analytics impact assumption settingtoday?

II. Implications for assumption setting process

III. Challenges and solutions for financial modeling integration

IV. Key takeaways

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What is the impact of predictive analytics?B

usin

ess

valu

e

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

Enhancedexperience studies

Enhancedassumption setting

Enhancedmodel-baseddecisions

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Where do predictive analytics impact assumption setting today?Use of predictive modeling is increasingly widespread for experience studies

Product Surrenders /Lapses

Utilization / fundingpattern Mortality Morbidity

VA Living Benefits

FIA Living Benefits

Fixed Annuities

Universal Life

Term

Long Term Care

We also see greater use of predictive analytics in the context of M&A

Source: Oliver Wyman research

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Implications for assumption setting process

1 More attention is paid to more secondary internal variables

2 More opportunities to test external variables

3 More 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 settingbut 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?

This creates four cohorts to model, i.e., a “policyholder behavior scenarios” dimension

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Computational implications for modelingRun time optimization becomes a three dimensional problem

Can specify accuracy functions to find optimal accuracy for a given run time in order togenerate an efficient frontier

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Other considerations for modelingHow granular should the model become?

1 Materiality and certainty of dynamic

2 Materiality of business

3 Model purpose

4 Degree of buy in

5 Ability to implement and validate

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Implementation approach: compromising between transparency,flexibility, controllability and 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 implementation

ConsiderationsParallel 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 / sensitivitytesting

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

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

Sensitivity testing • Understand the potential impact from stress testing the assumptionparameters

Documentation • Documentation of rationale for key modeling decisions and anylimitations 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

3 Prioritize and make incremental improvements