Predictive Analytics for Life Insurance: How Data
and Advanced Analytics are Changing the
Business of Life Insurance Seminar
May 23, 2012
Session 1 – Overview of Predictive Analytics for
Life Insurance
Presenter
Doug Welch
2012 Society of Actuaries Conference –
“Predictive Analytics for Life Insurance”
Doug Welch – Deloitte Consulting
Deloitte Consulting LLP
May 2012
Emerging Marketplace Significance
# 1 National bestseller … Moneyball: The Art of Winning an Unfair Game – Michael Lewis, 2005
Recent Business Publications
Visibility into analytics can assist business leaders in making decisions more accurately, objectively and economically – a rapidly developing consensus in business, education, law, medicine and even professional sports. There has been significant recent attention to the increasing use of analytics across disciplines:
y g ,
NY Times Bestseller… Freakonomics: A Rogue Economist Explores the Hidden Side of Everything – Steven D. Levitt, Stephen J. Dubner, 2009
Competing on Analytics: The New Science of Winning – Thomas H. Davenport, Jeanne G. Harris, 2007
Super Crunchers: Why Thinking-By-Numbers is the New Way To Be Smart – Ian Ayres, 2008
The Numerati– Stephen Baker, 2008
Business Week, “Managing by the numbers: How IBM improves productivity by tracking employees’ every
move”,Stephen Baker, September 8, 2008
Many insurers may be missing the opportunity to drive value through analytics, and those who explore these capabilities could gain a competitive advantage.
The Economist, “Data , data, everywhere – A special report on managing information”,February 27, 2010
Algorithmic Solutions Development and Deployment
While an insurance-based Algorithmic Solution approach has many applications, today we will focus on New Business Application Triage. Below is a high-level overview of the process. Using advanced statistical analysis, organizations are better able to segment cohorts of people or policy holders.
Analytical Solution
N t diti l
Potential Data Sources Segmentation Analysis
New Business Application Triage
Data aggregation and data cleansing Analytical Solution
Evaluate variables for correlation to target variable Develop Algorithmic Solution Score applicants Traditional internal data sources Non-traditional data sources Lifestyle Data Financial Data Synthetic Variables Household Data Consumer Data Medical Data Customer History Application
Data • Eliminate time-consuming and
physically invasive tests for certain applicants • Streamline application
review process • Improve ease of doing
business
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Important Note:
Data may be subject to various laws and regulations (e.g., FCRA, state privacy
laws). Deloitte Consulting does not provide legal advice. Clients must consult
their legal counsel to determine whether particular data may be obtained and / or
used in a particular context.
Possible Data for Life Insurance Algorithms
An insurance-based Algorithmic Solution approach starts with all of the traditional data that can be captured within the first 48 hours. This information can then generally be supplemented1with a variety of external datasets. This approach can assist in segmenting those who may otherwise appear indistinguishable from one another.
Data category
Traditional Underwriting
New Business Application Triage
Target Marketing / Cross-Selling Applications Application Basic demographics Medical history Family history Paramedical examination Fluids Height/weight Other medical/interview Telephone interview APS/medical records Treadmill Test EKG MIB (M di l I f ti
MIB (Medical Information Board data) Rx (prescription data) Driving record (MVR)
Traffic conviction history
Auto accident history
Existing policy data ?
Algorithmic Solutions – Business Applications
Analytics Enabled Underwriting
Simplified Life Insurance Cycle
Obtain / Retain Sales Force Design & Develop Products Market to / Identify Clients Assess Client Needs / Illustrate Submit & Process Application Underwrite Risk Assess Client Needs / Illustrate Submit & Process Application Underwrite Risk Manage and Service In-force Process Claims & Disburse Applying a New Business Application Triage Algorithmic Solution using application data, MIB, MVR, Rx and other 3rdparty data, together with underwriting rules established by the insurer…
Force Products Clients / Illustrate/ Illustrate ApplicationApplication force Disburse
Algorithmic Solutions-Enabled Triage Process
Algorithm Raw Score Application completed Tele-Interview completed if required) Insurer‘s Underwriting Rules
Medical tests not required Policy issued Processing time -several days ILLUSTRATIVE Expedited
- 4 - Copyright © 2012 Deloitte Development LLC. All rights reserved. MVR
3rd Party Marketing Additional Data
Sources: Obtain and analyze
medical test results
Policy issued or denied Processing time -several weeks MIB Rx Traditional
Algorithmic Solutions – Business Applications
Analytics Enabled Underwriting
Simplified Life Insurance Cycle
Obtain / Retain Sales Force Design & Develop Products Market to / Identify Clients Assess Client Needs / Illustrate Submit & Process Application Underwrite Risk Assess Client Needs / Illustrate Submit & Process Application Underwrite Risk Manage and Service In-force Process Claims & Disburse … may provide results that are similar to fully underwritten decisions for a significant portion of the business – predominantly the higher scoring segments. The graph below is illustrative of results based on our experiences but actual results will vary.
Force Products Clients / Illustrate/ Illustrate ApplicationApplication force Disburse
Algorithmic Solutions vs. Traditional Underwriting Results
10X tal it y R a te
Continue to fully underwrite application
X
Mo
rt
Algorithm Expected Mortality Fully Underwritten Pricing Expected Mortality
Low Model Score High Model Score
Apply insurer’s underwriting rules to reduce requirements and processing time Pop.
Obtain / Retain Sales
Force
Algorithmic Solutions – Business Applications
Agent Success and Retention
An additional high-value area where analytics might provide competitive advantage is in the area of retaining and enabling agents.
Simplified Life Insurance Cycle
Design & Develop Products Market to / Identify Clients Assess Client Needs / Illustrate Submit & Process Application Underwrite Risk Manage and Service In-force Process Claims & Disburse Obtain / Retain
Sales ForceForce Products Clients / Illustrate Application force Disburse
Sales Force
rm agent
success
The analysis can be based upon internally available information:
‒ Not-in-Good-Order ‒ Field Underwriting ‒ Business Quality ‒ Requirements Turnaround ‒ Call Center Questions / Calls ‒ Cycle Times S l P tt Higher Scoring Sales Force – 60% Lower Scoring Sales Force – 40% 2.5 X more likely to meet company’s definition of a “Successful Agent”
Agent Analysis Illustrative Results
ILLUSTRATIVE
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Chance of
l
onger
t
e
Low Score High Score
Pop. Avg.
‒ Sales Patterns
Algorithms score weighted factors and can provide on-going monitoring for at-risk producers and enable more targeted coaching / assistance
Ironically, the most common obstacle is the failure to methodically capture key data
< 20% chance of meeting company’s definition of a “Successful Agent” Market to / Identify Clients Assess Client Needs / Illustrate
Algorithmic Solutions – Business Applications
Targeted Client Marketing
Algorithmic Solutions can also provide opportunities to better target solutions to consumers, including the common challenge of cross-selling to existing customers.
Simplified Life Insurance Cycle
Obtain / Retain Sales Force Design & Develop Products Submit & Process Application Underwrite Risk Manage and Service In-force Process Claims & Disburse Market to / Identify Clients Assess Client Needs / Illustrate Clients / Illustrate
Force Products Clients / Illustrate Application force Disburse
Mass-marketing via email
Customer response
Application packet via mail
Application and Underwriting 15% 25% 30% 35% 40% 45% D ec ile
Market to target customers to limit sunk costs down the road, underwriting declines and not-takens
25% 12% 8% 7% 6% 5% 4% 3% 3% 1% 8% 6% 5% 5% 5% 4% 4% 3% 11% 0% 5% 10% 15% 20% 25% 1 2 3 4 5 6 7 8 9 10 Pe rc en t of D Decline Substandard
Manage and Service
In-force
Algorithmic Solutions – Business Applications
In-Force Policyholder Analytics
Some companies could enhance the management of their substantial in-force block, where analytics are typically focused on business losses and lapses rather than actually improving the business.
Simplified Life Insurance Cycle
Obtain / Retain Sales Force Design & Develop Products Market to / Identify Clients Assess Client Needs / Illustrate Submit & Process Application Underwrite Risk Process Claims & Disburse Manage and Service In-force force
Force Products Clients / Illustrate Application force Disburse
Inforce business Non-traditional data appended Retention Algorithm Focus retention rerouces on the healthy customers High Score Algorithm Score Continue current retention Low Score High Score
- 8 - Copyright © 2012 Deloitte Development LLC. All rights reserved. Health Risk Algorithm Algorithm Score Continue current retention processes most likely to surrender Low Score Spend fewer retention resources where they will have the least effect processes
Algorithmic Solutions Outlook
The life insurance industry has historically leveragedsome form of analytics in various areas, including market research
The analytics “arms race” is intensifying, driven in part by financial pressures, consumer expectations, competitive actions and information availability competitive actions, and information availability The potential analytics transformation for life
insurance could be similar to what has been experienced in other industries including:
– Retail: Individual consumer analysis – Distribution: Logistics sophistication – Property & Casualty: Underwriting models – On-line Services: Ease of use analytics
Each life insurance organization will be faced with important questions and tough choices important questions and tough choices Retaining the analytics status quo is essentially a
competitive choice
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