chris stehno - canadian reinsurancecrconline.ca/2015_presentations/crc 2015 breakout 1 big data...
TRANSCRIPT
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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