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A discussion on how actuaries can use advanced analytical techniques to modernize their experience studies processes
May 18, 2015
2
Agenda
This presentation will cover four topics:
1.Understanding our customers
2.Types of studies3.Data management4.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.
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Understanding our customer
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
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Life insurance ownership
Source: LIMRA
1960 1976 1984 1992 1998 2004 20100%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
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.
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19801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
U.S. Ten Year Treasury Rates
Addressing a challenge
How will policyholders behave when interests rates rise?
A significant challenge confronting the actuarial profession is how policyholders will behave under different environments.
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Understanding the policyholder
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. Home2. Financial assets
• Liabilities1. Mortgage2. Personal debt
Choices• Rational• Behavioral
1. Mental accounting2. Joint decision
making3. 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.
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Dependents Single & ‘Rich’ Growing Family Pre-Retiree Retiree New Generation
Liability Creation
Asset Transfer
Asset Creation Asset Creation
Asset Protection
Asset Preservation
Asset Depletion
Pol
icyh
olde
r Life
-Cyc
le S
tage
sLi
fe E
vent
sA
dvic
e
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 choicesLife events change the individual’s understanding of themselves and their relationship to others and to the environment.
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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 CharacteristicsOrigin: Spanish ethnicityAttitudes: Busy lifestyle, choose investments plan to utilize fully, prioritize children’s future, multi-generational supportInfluencers: Advice from friends and family, trusted advisorsPerception: 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
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Age
Level Premium
Premium after Level Period
Mortality Rate
Jump triggers action
Time
Searching Threshold
Action Threshold
1
2
3
4
5
6
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Types of studies
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
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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
Typesof
Studies
TerminationStudies
1
Selection & UtilizationStudies
2
Policy Cash FlowStudies
3
EconomicStudies
4
DemographicStudies
5
AdvancedStudies
6
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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
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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 14 160
2
4
6
8
10
12
Set A
A relatively “normal’ fit
2 4 6 8 10 12 14 160123456789
10
Set B
An obvious non-linear relationship missed by the line fitting
2 4 6 8 10 12 14 1602468
101214
Set C
A clear outlier that should be investigated before accepting the fitted regression line
6 8 10 12 14 16 18 2002468
101214
Set D
A linear regression line is probably not appropriate here
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Predictive analytical techniquesStatistical regression techniques can be used to incorporate more variables, increasing predicative capabilities and helping to overcome credibility issues.
StatisticalTechniques
14
Linear Regression 𝑳𝒂𝒑𝒔𝒆=𝜶+𝜷𝟏 ∙𝑷𝒓𝒐𝒅𝒖𝒄𝒕+𝜷𝟐 ∙𝑮𝒆𝒏𝒅𝒆𝒓 +𝜷𝟑 ∙ 𝑨𝒈𝒆+⋯
Generalized Linear Models
𝑳𝒂𝒑𝒔𝒆=𝒈(𝜶+𝜷𝟏 ∙𝑷𝒓𝒐𝒅𝒖𝒄𝒕+𝜷𝟐 ∙𝑮𝒆𝒏𝒅𝒆𝒓 ⋯)
SymbolicRegression 𝑳𝒂𝒑𝒔𝒆=𝜶+𝜷𝟏 ∙𝑷𝒓𝒐𝒅𝒖𝒄𝒕𝑫𝒖𝒓 +𝜷𝟐 ∙
𝑮𝒆𝒏𝒅𝒆𝒓 𝟐
𝒕𝒂𝒏−𝟏¿¿
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Trees and ForestsTree methods are widely used as alternatives to linear modeling, especially when modeling large data sets.
Partial Withdrawal
s
% Max
Yes
2% Lapse Rate
No
9.5% Lapse Rate
>125%
6.2% Lapse Rate
<75% 75% to 125%
1.3% Lapse Rate
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Cluster AnalysisClustering is an unsupervised learning method that seeks to find related groups of observations within a dataset
Secure
Stressed
Fragile
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Drawbacks of predictive analytics
“[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).
19801981198319851987198819901992199419951997199920012002200420062008200920112013
-2.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
U.S. Ten Year Treasury Rates
How will policyholders behave when interests rates rise?
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Simulation Model Structure
Synthetic Policyholder Population
Projected Product
Attributes
Projected PolicyholderAttributes
Competitive Factors
Economic Factors
Policyholder Factors
ProjectedLapse
Behavior
Parameters(For ‘what-if’ analysis)
Model ‘Agents’
OutputsSimulation Model
Withdrawal Frequency
Annuitization
PolicyholderBehaviors
Withdrawal Amount
Lapse
Products
Economic Environment
Advisors&
Company
Policyholders
External Data
Views & Calibration
ProjectedWithdrawal
BehaviorScenario
Combination
Inputs
18
Assumptions&
Scenarios
The model includes a range of components that simulate important factors relevant to policyholder decision-making
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1
5
2
4 6
3
Advanced studies: modeling decision process around employment
RetiredUnemployed
Employed
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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
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1 2 3
4 5 6
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1 2 3
4 5 6
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Data management
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
PwC
Future state conceptual design of data, calculation and reporting processes
24
5Analytical & Reporting Processes
Analytical Tools
• Standard reports• Queries• Report writers• Dashboards• Analytics• Visualization
Data Aggregations
Met
adat
a La
yer
Governance and Controls6
Internal Extracts
Policy Data
Fund Data
Financial Transactions
. . .
External Extracts
Financial Data
Economic Data
DemographicData
. . .
Extr
act
Pro
cess
es
1Source SystemsInternal
Sources
Policy Administration
Claim Systems
. . .
External Sources
Federal Reserve
Census Bureau
. . .
Extr
act,
Tra
nsfo
rm a
nd L
oad
Proc
ess
(ETL
)
2
Controls
Operational Data Store
Data Storage
ETL Process
Data Warehouse
Policy Data
Transactions
Study Results
. . .
3
ETL
Proc
ess
4Experience Studies Calculation Engines
Calculations
Actuarial Software
StatisticalPackages
Input
Output
Data Staging
PwC 25
Combining internal and external data
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
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Demographic studies: Household income statement
26
Source: SBI and PwC analysis
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Demographic studies: Household balance sheet
27
Source: SBI and PwC analysis
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Demographic studies: Employment
28
Source: SBI and PwC analysis
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Demographic studies: Financial health
29
Source: SBI and PwC analysis
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Explaining lapse behavior
30
Simulating customer behavior under multiple scenarios can help insurers develop a more holisticunderstanding 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.
Secure Fragile Stressed0%
10%
20%
30%
40%
50%
60%
Financial Security
All ages0.0%
3.0%
6.0%
9.0%
12.0%
15.0%
Under75%
75%to
<100%
100%to
<110%
110%to
<125%
125%to
<150%
150%or more
VA Lapse Rates
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Tools
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
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.
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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
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Symbolic Regression
Symbolic RegressionTraditional 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
Thank you.
This publication has been prepared for general guidance on matters of interest only, and does not constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is given as to the accuracy or completeness of the information contained in this publication, and, to the extent permitted by law, PwC, its members, employees and agents do not accept or assume any liability, responsibility or duty of care for any consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it.
© 2015. All rights reserved. In this document, “PwC” refers to a member firm of PricewaterhouseCoopers International Limited, each member firm of which is a separate legal entity.