Co-presented with Louis Lombardi at LOMA Chief Actuaries Meeting in Chicago on May 18, 2015. The presentation reviews the traditional experience studies conducted by Actuaries and details how advanced analytic techniques, including behavioral simulation, can be used in actuarial sturdies

1. www.pwc.com
A discussion on how actuaries can
use advanced analytical
techniques to modernize their
experience studies processes
May 18, 2015

2. 2
Agenda
This presentation will cover four
topics:
1. Understanding our customers
2. Types of studies
3. Data management
4. Tools
Given the amount of time for this
discussion, we will only be able to
touch on the highlights.
Types of studies
Understanding our
customers
Data management
Tools
This presentation will help you develop a better understanding of your policyholders and show
you how to use advanced analytical techniques to modernize your experience studies processes.

3. PwC
Understanding our customer
3
In this section, we will discuss how we can
develop a better understanding of our
customers (i.e., policyholders). The key
point is to view the policyholder as a
member of the household, making choices
based on their life situation.
Types of studies
Understanding our
customers
Data management
Tools

4. PwC
Life insurance ownership
4
Source: LIMRA
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1960 1976 1984 1992 1998 2004 2010
Ownership of individual
life insurance reaches a
50 year low.
Ownership of individual life insurance reached a 50 year low in 2010, leaving a significant
number of households underinsured.

5. PwC
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
1980 1990 2000 2010
U.S. Ten Year Treasury Rates
Addressing a challenge
5
How will policyholders behave when interests
rates rise?
A significant challenge confronting the actuarial profession is how policyholders will behave
under different environments.

6. PwC
Understanding the policyholder
6
Life Events
• Getting married
• Buying a house
• Having a child
• Retiring
Income Statement
• Salary
• Expenses
1. Nondiscretionary
2. Discretionary
3. Health costs
Balance Sheet
• Assets
1. Home
2. Financial assets
• Liabilities
1. Mortgage
2. Personal debt
Choices
• Rational
• Behavioral
1. Mental accounting
2. Joint decision
making
3. Financial literacy
It is important that we view the policyholder not as a male age 40 nonsmoker, but as a member
of the household, making choices based on their life situation.

7. PwC 7
Dependents Single & ‘Rich’ Growing Family Pre-Retiree Retiree New Generation
Liability Creation
Asset Transfer
Asset Creation Asset Creation
Asset Protection
Asset Preservation
Asset Depletion
PolicyholderLife-CycleStagesLifeEventsAdvice
Asset Cycle
• Paying off student loans
• Starting a career
• Getting married
• Buying a home
• Having or adopting children
• Paying tuition bills
• Caring for parents
• Planning for retirement
• Withdrawal money for retirement
• Paying for health care
• Creating a legacy
Understanding life events and choices
Life events change the individual’s understanding of themselves and their relationship to others
and to the environment.

8. PwC
Hispanic: Hector
“My family is the most important thing
in my life. If these products will protect
my family and help me save for my
children’s college tuition, I would be
interested in purchasing them.”
Demographics and Characteristics
Origin: Spanish ethnicity
Attitudes: Busy lifestyle, choose investments
plan to utilize fully, prioritize children’s future,
multi-generational support
Influencers: Advice from friends and family,
trusted advisors
Perception: Life insurance is too expensive
Source: LIMRA and PwC analysis
Channel Preferences
• Independent Agent: credible and
established sources; price conscious so prefers
ability to compare prices across products
• Captive Agent : agents that represent
carriers with strong Hispanic value
propositions
• Bank – one-stop shop for financial needs
• IBD: may be able to provide multiple low cost
financial solutions to address multiple needs
How can we help?
• Simple and affordable products that can be
modified over time as people age
• Juvenile life insurance for children with
conversion to permanent option at designated
ages designated
• Living benefits
• Options: short term disability, education,
family healthcare, final expense, guarantee
riders for minimum return or TL return of
premium guaranteed, event trigger
How Can we Reach Them?
• Bi-lingual agents that understand unique
Hispanic culture and Hispanic family needs
• Proactive channels and directed marketing
What are their needs?
Financial Concerns:
• Less disposable income because of the need to
support extended family
• Protection due to single breadwinner family
structure
• Adequate resources to support education of
children, protection from sudden death,
• Financially responsible for family members
• Products with minimal fees, maximum use of
benefits, and premium return
• Inexpensive plans that provide protection and
income opportunities
Risks:
• Risk averse due to pressure to support family
8

9. PwC 9
Age
Level Premium
Premium after
Level Period
Mortality
Rate
Jump triggers action
Time
Searching Threshold
Action Threshold
1
2
3
4
5
6

10. PwC
Types of studies
10
In this section, we will discuss the
different types of studies actuaries are
currently performing. It will also discuss
the type of studies actuaries can create in
the future using advanced analytical
techniques.
Types of studies
Understanding our
customers
Data management
Tools

11. PwC
Types of experience studies
11
Experience studies can be grouped into six major categories to reflect the type of information
they will provide.
Foundational
Aspirational
Types
of
Studies
Termination
Studies
1
Selection & Utilization
Studies
2
Policy Cash Flow
Studies
3
Economic
Studies
4
Demographic
Studies
5
Advanced
Studies
6

12. PwC
Basic Format
12
The challenge is to develop both tabular and graphical displays of experience studies that are
intuitive and insightful.
Source: 2014 Post Level Term Lapse & Mortality Report, Society of Actuaries (2014)
Actual-to-Expected Mortality

13. PwC
Importance of visualization
13
Source: Anscombe, F. J., Graphs in Statistical Analysis, American Statistician (1973)
Our brain processes data in a visual format more easily and faster than tables of numbers.
0
2
4
6
8
10
12
0 5 10 15
Set A
A relatively “normal’ fit
0
2
4
6
8
10
12
0 5 10 15
Set B
An obvious non-linear
relationship missed by
the line fitting
0
2
4
6
8
10
12
14
0 5 10 15
Set C
A clear outlier that should be
investigated before accepting
the fitted regression line
0
2
4
6
8
10
12
14
0 5 10 15 20
Set D
A linear regression line is
probably not appropriate here

14. PwC
Predictive analytical techniques
Statistical regression techniques can be used to incorporate more variables, increasing
predicative capabilities and helping to overcome credibility issues.
Statistical
Techniques
14
Linear
Regression
𝑳𝒂𝒑𝒔𝒆 = 𝜶 + 𝜷 𝟏 ∙ 𝑷𝒓𝒐𝒅𝒖𝒄𝒕 + 𝜷 𝟐 ∙ 𝑮𝒆𝒏𝒅𝒆𝒓 + 𝜷 𝟑 ∙ 𝑨𝒈𝒆 + ⋯
Generalized
Linear Models
𝑳𝒂𝒑𝒔𝒆 = 𝒈(𝜶 + 𝜷 𝟏 ∙ 𝑷𝒓𝒐𝒅𝒖𝒄𝒕 + 𝜷 𝟐 ∙ 𝑮𝒆𝒏𝒅𝒆𝒓 ⋯ )
Symbolic
Regression
𝑳𝒂𝒑𝒔𝒆 = 𝜶 + 𝜷 𝟏 ∙ 𝑷𝒓𝒐𝒅𝒖𝒄𝒕 𝑫𝒖𝒓
+ 𝜷 𝟐 ∙
𝑮𝒆𝒏𝒅𝒆𝒓 𝟐
𝒕𝒂𝒏−𝟏( 𝜷 𝟑)
+ 𝜷 𝟒 ∙
𝟏
𝑨𝒈𝒆
+ ⋯

15. PwC
Trees and Forests
15
Tree methods are widely used as alternatives to linear modeling, especially when modeling large
data sets.
Partial
Withdrawals
% Max
Yes
2% Lapse Rate
No
9.5% Lapse Rate
>125%
6.2% Lapse Rate
<75% 75% to 125%
1.3% Lapse Rate

16. PwC
Cluster Analysis
16
Clustering is an unsupervised learning method that seeks to find related groups of observations
within a dataset
Secure
Stressed
Fragile

17. PwC
Drawbacks of predictive analytics
17
“[Predictive modeling] is designed to
rank individuals by their relative risk,
but not to adjust the absolute
measurement of risk when a broad
shift in the economic environment is
nigh.”
Eric Siegel, Predictive Analytics: The Power to Predict Who Will
Click, Buy, Lie or Die, Wiley (2013).
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
1980 1990 2000 2010
U.S. Ten Year Treasury Rates
How will policyholders
behave when interests rates
rise?

18. PwC
Simulation Model Structure
Synthetic
Policyholder
Population
Projected
Product
Attributes
Projected
Policyholder
Attributes
Competitive
Factors
Economic
Factors
Policyholder
Factors
Projected
Lapse
Behavior
Parameters
(For ‘what-if’ analysis)
Model
‘Agents’
Outputs
Simulation Model
Withdrawal
Frequency
Annuitization
Policyholder
Behaviors
Withdrawal
Amount
Lapse
Products
Economic
Environment
Advisors
&
Company
Policyholders
External
Data
Views &
Calibration
Projected
Withdrawal
Behavior
Scenario
Combination
Inputs
18
Assumptions
&
Scenarios
The model includes a range of components that simulate important factors relevant to
policyholder decision-making

19. PwC 19
1
5
2
4 6
3
Advanced studies: modeling decision process around employment
RetiredUnemployed
Employed

20. PwC 20
Individual Dormant
Need Cash
Use disposable
income
Partial VA
withdrawal
Consideration of
withdrawal
Cash need covered
Event
(i.e., health issue)
Full VA
withdrawal
Account
withdrawal
hierarchy
Cash need
Unfulfilled
Other accounts
(CD, mutual funds,
401k)
Cash need fulfilled
1
2
4
5
6
3
Advanced studies: modeling decision process around fulfilling a cash
need

21. PwC 21
1 2 3
4 5 6

22. PwC 22
1 2 3
4 5 6

23. PwC
Data management
23
In this section, we will discuss the data
management, calculating, and reporting
processes. The key point is: (1) to develop
a data management strategy that
integrates internal and external data
sources; and (2) to separate calculating
from data management and reporting,
wherever possible.
Types of studies
Understanding our
customers
Data management
Tools

24. PwC
Future state conceptual design of data, calculation and reporting
processes
24
5
Analytical & Reporting Processes
Analytical Tools
• Standard reports
• Queries
• Report writers
• Dashboards
• Analytics
• Visualization
Data Aggregations
MetadataLayer
Governance and Controls
6
Internal
Extracts
Policy Data
Fund Data
Financial
Transactions
...
External
Extracts
Financial Data
Economic Data
Demographic
Data
...
ExtractProcesses
1
Source Systems
Internal Sources
Policy
Administration
Claim Systems
...
External Sources
Federal Reserve
Census Bureau
...
Extract,TransformandLoadProcess(ETL)
2
Controls
Operational Data
Store
Data Storage
ETL Process
Data Warehouse
Policy Data
Transactions
Study Results
...
3
ETLProcess
4
Experience Studies
Calculation Engines
Calculations
Actuarial
Software
Statistical
Packages
Input
Output
Data Staging

25. PwC
Combining internal and external data
25
Policyholder data
• Millions of policyholders
• 10’s of variables
Narrow & Deep
Datasets
+
Household Data
• 4,000-5,000 households
• 100’s of variables
Broad & Shallow
Data
=
Synthetic household data
• Thousands of households
• 100’s of variables
Synthetic
Population
Using various statistical techniques, internal data can be combined with external data to give a
more complete view of the policyholder.
Advanced statistical technics

26. PwC
Demographic studies: Household income statement
26
Source: SBI and PwC analysis

27. PwC
Demographic studies: Household balance sheet
27
Source: SBI and PwC analysis

28. PwC
Demographic studies: Employment
28
Source: SBI and PwC analysis

29. PwC
Demographic studies: Financial health
29
Source: SBI and PwC analysis

30. PwC
Explaining lapse behavior
30
Simulating customer behavior under multiple scenarios can help insurers develop a more holistic
understanding of the choices policyholders make.
You will discover that certain customer
behaviors that seem “irrational” may actually
reflect your relatively limited view of customers’
personal circumstances.
For example, classifying a customer’s actions as
“irrational” because he surrenders a variable
annuity contract that was deeply “in-the-money”
may be inaccurate.
The customer may have needed the cash
surrender value to make mortgage payments or
cover a large, unexpected medical expense.
0%
10%
20%
30%
40%
50%
60%
Secure Fragile Stressed
Financial Security
Under
75%
75%
to
<100%
100%
to
<110%
110%
to
<125%
125%
to
<150%
150%
or more
0.0%
3.0%
6.0%
9.0%
12.0%
15.0%
All ages
VA Lapse Rates

31. PwC
Tools
31
In this section, we will discuss the types of
tools needed to perform experience
studies using advanced analytical
techniques.
Types of studies
Understanding our
customers
Data management
Tools

32. PwC
Experience study tools
32
R
 Software and programming language
 Free: open-source
 Widely used software that seems to be gaining popularity, particularly with the growing data science
community
 Non-commercial nature: concerns on quality, consistency, technical support and training
 Commercially supported versions also available, e.g. Revolution R
 Can be more difficult to scale for large data sets – R keeps all objects in memory
 Better hardware and ‘big data’ packages in R can handle larger data sets, but only for packages and
algorithms designed to do so (i.e. not all R functions)
 Pros: free, a large community of developers and users leading to significant development and available
packages for most methods. Commonly used in academia with many students graduating with
experience in R
SAS
 Software and programming language
 Commercial package – annual license
 Widely used software, one of the most popular commercial packages for
advanced analytics
 Technical support and training available from SAS
 Packages can support large data sets
 Pros: supports a wide-range of statistical and analytical methods proven
in many commercial environments.

33. PwC
Experience study tools
33
Python
 Programming language
 Free: open-source
 Combination of a general-purpose programming language that is also
easy to use for analytics
 Emphasizes readability: quick and easy to learn
 Libraries of code for data processing and analytics that are fast-
developing and gaining popularity
 Suited for ‘big data’
 Pro: easy to learn programming language with growing analysis and
visualization packages
Tableau
 Data visualization and dashboard program
 Tableau Desktop, Reader, Server and Cloud options are
available
 Commercial Package – Tableau Desktop is $2K per seat
and annual maintenance
 Provides connections to numerous data sources
 Emphasizes data visualizations: quick and easy to
generate analytics, dashboards and advanced
visualizations

34. PwC
Symbolic Regression
34
Symbolic Regression
Traditional regression assumes a linear model
form (after any transformations to the data and
link functions). Such data transformations are
largely the domain of the user.
Symbolic regression uses brute computer
power with genetic algorithms to find the best
functional form the fits the equation to the
data.
Pros
 Can automatically find complicated
functional forms and relationships in data
 Reduces time spent specifying a model
Cons
 Can easily over-fit functional form
 Very computationally intensive
 Functional form not always intuitive
Symbolic regression uses genetic programming to “evolve” more accurate model functional
forms
Source: Eureqa Pro, Nutonian

35. Thank you.
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