Oliver Wyman Integrating Predictive Analytics in Assumption Setting - 2014 SOA Annual Meeting & Exhibit

© 2014 Oliver Wyman

Guillaume Briere-Giroux, FSA, MAAA, CFA

Integrating Predictive Analytics in Assumption Setting Implementation and Integration in Financial Models

2014 SOA Annual Meeting & Exhibit

Orlando – October 27, 2014

© 2014 Oliver Wyman 1 1 © 2014 Oliver Wyman

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


How predictive analytics impact assumption setting?

Bu

sin

es

s v

alu

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

Enhanced

experience studies

Enhanced

assumption setting

Enhanced

model-based

decisions


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

Source: Oliver Wyman research


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


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


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


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

5 Ability to implement and validate


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


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


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

Economy & Finance

Oliver Wyman Integrating Predictive Analytics in Assumption Setting - 2014 SOA Annual Meeting & Exhibit