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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

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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

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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

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Future state conceptual design of data, calculation and reporting processes

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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

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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

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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

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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

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Experience study tools

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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

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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.

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