Session 7: The Merging of Actuarial Science and Data ScienceThis topic will cover how to integrate...
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2018 Predictive Analytics Symposium Session 07: The Merging of Actuarial Science and Data Science SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer
Transcript of Session 7: The Merging of Actuarial Science and Data ScienceThis topic will cover how to integrate...
2018 Predictive Analytics Symposium
Session 07: The Merging of Actuarial Science and Data Science
SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer
Merging Actuarial and Data ScienceSeptember 20, 2018Julia Romero FSA, CERA
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Common ground isn’t enough
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The right tool for the job
- Levels of collaboration between actuarial and predictive modeling workstreams
- Generating data science products that are actuarially relevant
- Creating actuarial frameworks that can utilize data science effectively
Roadmap
Why working together matters
Independent Intersecting Integrated
New
Business
Inforce
Traditional Approach
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Example:
Underwriting
model Mortality
Challenges Benefits
Traditional Approach
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● Useful when approaches are prescribed by
regulation
● Conform to traditional governance and
benchmarking approaches
● Traditional performance metrics are
straightforward
● Disconnect between predictive and actuarial
models
● Cohort / cell based approach uses data
inefficiently
● Risk of falling behind standard practice
Intersection approach
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Example:
Closed Projection
System
Agent Based Model
● Compress over important predictive facets
● May require substantial use of actuarial
judgement
● Manual processing increases model risk
● Difficult to calculate traditional metrics
Challenges Benefits
Intersection Approach
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● Leverage predictive analytics and data science
● Low or moderate modeling burden
● Enforces sanity checking
Integrated approach
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Strategy utilized at Haven Life
Example:
Closed Projection
System
Agent Based Model
● Limited opportunity to modify predictive model
results
● Substantial investment to build and maintain
● May be perceived as a “black box”
Challenges Benefits
Integrated approach
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● Fully utilizes predictive modeling
● Meaningful interaction between predictive
models
● Real time analysis of assumption-design
dynamics
● Direct connection between profitability and
strategy
Building relevant predictive models
Independent Intersecting Integrated
New
Business
Inforce
Eyes on the prize
Iteration is good
Partners are important
Never underestimate the data
Build models about things that matter
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Situation
- Your projection system only
accommodates tabular assumptions
- The point of an ABM is to capture
behavioral dynamics and feedbacks
Intersection Case Study: ABM for VAs
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How do you use your agent
based simulation model
(ABM) to set a new lapse
assumption for inforce
business?
Agent Based Simulation Models
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Images from SOA Product Matters Issue 89 “Using Agent based modeling to understand policyholder behaviors”. Lombardi, Louis; Paich, Mark; and Rao, Anand (2014)
Square peg - round hole
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Sample ABM results Projection assumption table
How would we do it?
“Opening” actuarial processes
Independent Intersecting Integrated
New
Business
Inforce
Design flexible foundations and interfaces
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Situation
- Random forest that outputs the
likelihood of a premium payment for a
policy in a given period
- Current assumption is a static table
with some interest rate dynamics
Integration Case Study: Random forest for UL
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How can you leverage a
random forest model of
premium funding behavior in
a universal life pricing model?
PROPRIETARY & CONFIDENTIAL
Random forest models
How would we do it?
Questions?
Thank you
