Chris Stehno Big Data and Analytics’ dramatic impacts in the Life Insurance Industry

Agenda Big Data Life Insurance Specific Examples of Predictive Analytics New Business Application Triage Underwriting

Inforce Management Risk Based Marketing Risk Based Retention

Distribution Recruiting and Retention Agent / Consumer Matching

Big Data is in the News From the dawn of civilization until 2003, humankind generated 5 exabytes of data. Now we produce 5 exabytes every two days, and the pace is increasing. Eric Schmidt, Executive Chairman, Google

Every century a new technology – steam power, electricity, atomic energy or microprocessors – has swept away the old world with a vision for a new one. Today, we seem to be entering the age of big data. Michael Cohen, Author, Speaker, Broadcaster

We’ll see this as a the time in history when the world’s information was transformed from an inert, passive state and put into a unified system that brings the information alive and lives on forever. Michael Nielsen, Data Scientist, Writer, Programmer

Big Data on Consumers

Reads two e-books per month

Subscribes to multiple health

magazines

Attends yoga class twice a week

Frequently purchases fruits and

vegetables from grocery store

Collects collectible plates

Likes country music

Listens to books on tape

Subscribes to Diabetes Monthly

magazine

Frequently purchases discounted

gift certificates for fast food from

deal-of-the-day websites

Orders plus-size clothing

Gambles at casinos

Reads about astrology

Owns a video game system

Jane Joe

Future Disruptors - Telematics The current market for usage-based driving is around $1 billion in annual premiums, mostly generated by Progressive.1

One million of Progressive’s nearly nine million auto insurance customers use Snapshot, which has logged over six billion miles of driving data from over one million trips per day.1

Progressive’s “Snapshot” collects large volumes of driving data with a device that policyholders can install

This includes information on:

Mileage

Speed and

Driving habits, such as how often you drive after midnight

This data is transmitted directly to Progressive and analyzed to provide discounts to individuals with safer driving habits.

P&C insurers such as Allstate have started to provide similar solutions.

1Wall Street Journal, “Auto Insurers Bank on Big Data to Drive New Business’

Similarly, “Quantified Self” applications such as Fitbit and Nike+ Fuel Band allow customers to monitor and share lifestyle/health data such as: Weight and Body Measurements Heart Rate Blood Glucose Blood Pressure Activity Such data can be transmitted directly to the life insurance, and impact:

Cost of insurance

Understanding of increased/decreased health risk for the individual

Underwriting decision

Future Disruptors: Life & Health

Future Disruptors - Blue Button

The next big disrupter in insurance market place, that will lower underwriting costs and significantly reduce the processing speed.

Applications of Predictive Analytics

Sample Applications for Life

Risk-Based Marketing to P&C Customers

Application Triage

Proactive Retention Management

Growth

Operations

Underwriting Algorithms and Automated Systems

Advisor Retention

Advisor Recruitment

Distribution

Lifestyle Based Analytics (LBA)

Disease State Algorithms

Using only third-party data algorithms have been built to provide insights into individuals afflicted with 20 plus lifestyle diseases (e.g. diabetes, female cancer, tobacco related cancer, cardiovascular, depression, etc.) which impact morbidity. Additionally over 1 million paramedical exams have been used to identify individuals who are at extreme risk or have a condition that has not been otherwise detected or diagnosed.

3rd Party Data Types Disease State Algorithms

Survey Data

– Self-reported information collected over the last 18 mos

– Contains many lifestyle elements

Observed Data:

– Basic individual and household demographics • Age, sex, number and ages of children, marital status • Occupation categories, education level

– Financial information • Income level, net worth, savings and investments • Home value, mortgage value

– Lifestyle data • Activity — running, golf, tennis, biking, hiking, soccer • Inactivity — TV, mail-order, computers, video games,

casino gambling • Diet, weight-loss, exercise, cooking, gardening, health

foods, pets

Small Area Characteristics:

Disease State Model Demo

Health Risk Score Demo

New Business Process Application Triage

Application Triage Process Example of Process

Algorithm Raw Score

Application completed

Tele-Interview completed if

required)

MVR

3rd Party Marketing

Additional Data Sources:

Insurer‘s Underwriting

Rules

Obtain and analyze medical test results

Policy issued or denied Processing time - several

weeks

Medical tests not required Policy issued Processing time - several

days

MIB

Rx

ILLUSTRATIVE

Expedited

Traditional

Sample Data

We review the broadest set of variables possible to determine what data elements add meaningful insights to the algorithm.

Representative Data

Traditional Datasets

Age & Gender

Policy Type

Tobacco use

Medical history

Family history

Deloitte Disease State Models (e.g., hypertension, depression, alcohol, diabetes, tobacco)

Net worth / income

Education levels

Type of vehicle owned

Occupation

Housing (e.g., own/rent, size of home, yr built, mort)

Hobbies (e.g., fish, hunt, boat, garden, gamble)

Lifestyle (e.g., weight control, TV, donate to charity)

Exercise habits (e.g., walk for health, run, tennis, skiing, golf)

3rd Party Rx claims data

MIB

MVR

Non-Traditional Datasets

Agent factors (e.g., tenure, production)

Total policies with company – household

Total premium placed with company – household

Premium Payment Frequency

Family medical history

External data Internal data External data Internal data

Interim Algorithm Results Raw Algorithm Results (Modeled Results by Decile)

New Business Process Underwriting

Lift Comparison using FCRA data

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1 2 3 4 5 6 7 8 9 10

Perc

enta

ge S

PPRF

SPPRF Percentage by Decile

Triage Model FCRA Model

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

1 2 3 4 5 6 7 8 9 10Pe

rcen

tage

Dec

line

Decline Percentage by Decile

Triage Model FCRA Model

Strategic Vision for Life UW Total Number of Formal Applications Applications

• Lower underwriting costs and reduce product cost • Faster turnaround times

• Consistent standards • Detailed insights into business portfolio

Business Objectives

Automated Underwriting using

Methodology Predictive Modeling

Expected Output

75% Applications Auto Underwritten Real-time Goal

37.5% 37.5%

Risk Based Marketing and

Retention

The Challenge With In-force The challenge with in-force sales is that a “preferred” customer 10 years ago may not be “preferred” today

Dave was 35 when he first purchased his $500,000 life insurance policy He was young, healthy and was categorized into the best risk class during underwriting

Dave’s life stage has changed He now has a family with children and has the financial means to upgrade his policy

10 years later

• Dave’s risk profile has not changed

• His lifestyle has not changed and continues to be healthy

• Could be classified into similar risk class

• Good opportunity for cross-sell / upsell

Good risk: potential for simplified underwriting

• Dave’s risk profile has changed significantly

• His changed lifestyle led to multiple co-morbidities

• Should not be classified into best risk class

• Additional review or traditional underwriting needed for cross-sell

Bad risk: needs thorough traditional underwriting

-or-

Population Scoring Model

Pre-scoring the entire United States adult population (210 million lives), giving insurers the ability to identify the markets and individuals within those markets who are most likely to buy and most likely to qualify – driving to higher response and approval rates

Analytics powered by 150+ algorithms including: Disease state algorithms Lifestyle clusters Purchase behaviors Propensity to qualify for and buy life insurance

150 +

Access to over 25 terabytes of third-party data that provides individual-level lifestyle and purchasing habit insights across the entire United States population

25+ TB

Customer Insights

Database

Illustrative Deliverable

Policy Number Name Age Likely to

Buy Likely to Qualify

Priority for Targeted Sale

Product to be Offered

Existing Agent

Reason Code

869382 John Doe 54 Y N Medium Annuity John Smith Rx Indicator 459204 Steve Johnson 32 Y Y High Universal Life Mike Himebaugh Newly Married 476024 Mark James 36 N N Low N/A Roger Dames Financial/Bankruptcy 386492 Sue Clark 68 N Y Medium SPIA Steve Mapes Newly Retired 345710 Sally Irvin 29 N Y Medium Term Life Sally Nichols Recently Divorced 836803 Brian Wood 45 Y Y High Term Life Keith Ames New Child 248046 Ed Jones 46 N N Low N/A Jennifer Appel Poor Health

Full Traditional Data Set

Non-Traditional Third Party Data

‘Likely to Qualify’

Algorithm

‘Likely to Buy’

Algorithm

Algorithm Input Algorithm Output

Overview of Cross & Up-sell Model

Customer Lifetime Value Models Model Purpose Key Deliverables

1. Product Surrender Differentiate Surrender propensity for individual contracts

Model validation results Slices report & dashboard Results Presentation Modeling Documentation Univariate modeling dataset Data source integration code

2. Product Profitability (e.g. Annuities Income-taking Behavior)

Identify policyholders likely to exhibit behaviors with potential negative profitability impacts (e.g., non-RMD partial withdrawal, income rider utilization)

Income-taker model Mortality model Potential business application

3. Product Cross Sell Identify the next best offer to either retain the business or to move the current business into a profitable product

Separate model developments for profitable and unprofitable policyholders

4. Customer and Agent /Distribution Matching

Match current customers with the distribution channel and/or specific Agent to optimize conversion

Distribution Model Agent matching algorithm Time series model

Summary of Models

Execution Channel Development

Align the channels through which retention and cross sell tactics are delivered to match the value and risk of the target cohorts; over

time, achieve optimization for cost and effectiveness.

F2F (Agents)

$100 per interaction

Over the phone advice and outbound capability $15 per interaction

First Line of Defense (e.g., up-skilled call center, Mail/Email collateral,

statement inserts)

$1 – $5 per interaction

Execution Channel Development

Distribution

Chan

ce o

f bec

omin

g a

succ

essf

ul a

gent

Low Score High Score

Pop. Ave.

Agent Success Segmentation

• The model scores individuals from 1 to 10 with 1 being the lest likely to resemble a successful agent and 10 being the most likely.

• The model is then tested on a validation set of data and the results are presented in a lift curve as shown below.

• Candidates in the first couple of deciles have less than a 20% chance of becoming successful agents

• Candidates in the best deciles have almost a 60% chance of being successful.

Top Scoring Candidates – 30%

Lower Scoring Candidates – 70%

2.5X More Likely to be a

Successful Agent

Less than 20% Chance of Becoming a Successful Agent

Agent Model

Predictive Retention Model

Attrition Risk Profile

Employee data

Risk Score

Risk Drivers

Advisor/Customer Matching

Matching Algorithms

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