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willistowerswatson.com
Use of Predictive Analytics in U.S.Life & Annuity Market
SEAC
November 15, 2018
John Fenton
© 2018 Willis Towers Watson. All rights reserved.
willistowerswatson.com
Items Addressed in this Presentation
2© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.
V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Overview of current use of predictive analytics in the life and annuity industry
As part of this, we will share a few results from a recent WTW survey
Focus on using predictive analytics to set experience assumptions
Case study involving use of predictive analytics in the pension mortality area
Use of predictive analytics in accelerated underwriting market
A few thoughts on developing staffing structure
Future emerging uses, including in determining customer value and inforce
management
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Overview
© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.3
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Where has predictive analytics (PA) been used so far in the life and annuity market
and where might it be utilized further
Not intended as exhaustive list
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Analyzing Experience and
Setting Assumptions
Underwriting Aid
Sales and Marketing
Inforce Management/
Customer Profitability
Claims Analytics
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Results from WTW Survey show use of PA varies materially
5© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.
V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Percent of Companies Using Predictive Analytics
Now vs. Within 2 Years
Amount of Premium Over $3 billion $1 billion - $3 billion less than $1 billion
Line of Business Now
In two
years Now
In two
years Now
In two
years
Individual life insurance 70% 90% 50% 75% 53% 89%
Group insurance 71% 100% 67% 100% 23% 46%
Retail individual annuities 71% 71% 13% 38% 18% 32%
Institutional annuities 50% 83% 50% 75% 11% 33%
Individual health 40% 80% 20% 40% 15% 38%
Base: Those who sell or have in-force business on the books (n varies).
Source: Willis Towers Watson 2018/2019 Life Insurance Predictive Analytics Survey: Today and in the Future
<20% 20%-39% 40%-59% 60%-79% 80%+
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Predictive modeling will continue to improve quantification of cost in experience analysis
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
This is done by more granularly assessing the impact of policyholder characteristics that may be difficult to quantify
using traditional analysis. Examples of factors include:
Face amount band
Risk class
Application questions
This may significantly increase the number of factors used in the pricing of new products and granularity of pricing
differentials (reducing risk to anti-selection)
Also allows for greater understanding of interaction of factors
Mortality Morbidity Loss Ratio Lapse Rate
Distribution channel
Prescription drug history
Wear-off
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Use of PA for mortality and morbidity is most prominent in annuities,
but significant growth is expected in individual life insurance
7© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.
V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
55%
40%
28%
23%
19%
36%
33%
36%
52%
37%
9%
27%
36%
25%
44%
Institutional annuities n = 11
Retail individual annuities n = 15
Individual health n = 11
Individual life insurance n = 40
Group insurance n = 16
Experience analysis: mortality/morbidity
Currently use Plan to use within two years Do not use and have no plans to use
Base: Those currently using or planning to use predictive analytics in at least one line of business (n varies).
Source: Willis Towers Watson 2018/2019 Life Insurance Predictive Analytics Survey: Today and in the Future
Now
In two
years Growth
55% 91% 65%
40% 73% 83%
28% 64% 129%
23% 75% 226%
19% 56% 195%
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Pension Mortality Case Study
© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.8
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The U.S. PRT market has grown significantly since 2013
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
1,500
0
5,000
10,000
15,000
20,000
25,000
30,000
Avg1991-2011
2012 2013 2014 2015 2016 2017
Annuity Purchase Transaction History – 1991 to 2017
1991 – 2011 Source: Estimated LIMRA
2012 – 2017 Source: Willis Towers Watson Pension Risk Transfer Survey; includes history for 16 insurance companies through December 31, 2017
PRT business consists of insurers buying out
(selected) liabilities of U.S. private defined
benefit plans
While these figures are significant, this is still
a small portion of total DB market of
approximately $2.5 - $3.0 Trillion
Larger players have a reasonable amount of
mortality data; many new entrants don’t
36,033
3,848
8,700
13,775 13,992
23,301
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In order to help assess the appropriate mortality assumptions, Willis Towers Watson
(WTW) developed the WTW Pension Mortality Analysis (“PMA”)
10© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.
V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Covers mortality data for 27 WTW retirement/benefit plans
Based on nearly 4 million life years of exposure and approximately 135K deaths
Covers non-disabled retired lives and their beneficiaries
Data for period of 2009-2013
Utilizes PA techniques which we believe offers greater insights into mortality relative to standard industry
tables (e.g., RP 2014) – more details on subsequent page
Designed to reflect base mortality; use historical mortality improvement (HMI) to bring up to today’s date
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Our PA includes the following – most of which is typically provided in census included
with RFQ
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Attained Age
Gender
Recipient Type
Collar
Benefit Type
Industry
Monthly Benefit Amount
Primary
Factors
Gender and Recipient type – varies by attained age – can be viewed as forming base tables
Collar and Benefit Type – varies by attained age
Industry – varies by attained age
Benefit Amount and Industry Applicable on certain industries on monthly benefit amounts below $1,000
Benefit Amount – varies by attained age
Interactions
We have also developed a supplemental zip code model – based on subset of data
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Here is a sample of our predictive modeling findings
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Impact of Monthly Benefit Amount on Mortalityby Attained Age
ExposureMonthly_Benefit_Amount (< 1000)Monthly_Benefit_Amount (>= 2000 AND < 2250)Monthly_Benefit_Amount (>=3500)
Impact of Collar on Mortalityby Attained Age
Exposure Collar (Blue) Collar (White) Collar (Mixed)
Impact of Benefit Type on Mortality - Blue CollarDuration
Exposure - Certain
Exposure - Joint
Exposure - Single
Benefit_Type
(Certain)
Benefit_Type (Joint
Survivor)
Benefit_Type (Single
Life)
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Lessons learned from PMA
Beneficiary mortality is higher than participant mortality
Varies by gender and time since beneficiary
Joint + Survivor mortality is lower than Single Life mortality
Proxy for marital status
Mortality varies by industry but not as much as some other factors
The relationship on benefit type (e.g., Joint + Survivor, Single Life) and collar varies by type of collar
Benefit amount, particularly above $1000 a month, has a significant impact
Differences in factors generally come together at advanced ages
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Accelerated Underwriting
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What is accelerated underwriting?
A new approach to underwriting individual life policies
Historic approaches often criticized as taking too much time, too invasive
Replicates full underwriting with fewer requirements
Underwrites a policy without the need for blood work or attending physician statements
Data from outside sources is used to augment the application
Based on PA to help determine key drivers of mortality
Underwriting process may be fully or mostly automated with little input from underwriters
Models are developed to handle new data and assign risk class scores
Underwriters review model decisions or make decisions on complex cases
15© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.
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Use of PA for underwriting is highest in individual life, but is growing in other lines
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52%
44%
27%
25%
40%
19%
46%
31%
8%
37%
27%
44%
Individual life insurance n = 40
Group insurance (case level underwriting) n = 16
Individual health n = 11
Group insurance (medical underwriting) n = 16
Underwriting
Currently use Plan to use within two years Do not use and have no plans to use
Base: Those currently using or planning to use predictive analytics in at least one line of business (n varies).
Source: Willis Towers Watson 2018/2019 Life Insurance Predictive Analytics Survey: Today and in the Future
Now
In two
years Growth
52% 92% 77%
44% 63% 43%
27% 73% 170%
25% 56% 124%
willistowerswatson.com
The use of data in life insurance underwriting is changing rapidly
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
MIB: results from prior
insurance applications
MVR: motor vehicle records,
citations, crash data
Fraud: Identify check
Consumer records: court
cases, bankruptcy
Policyholder characteristics: age, gender,
marital status, tenure etc.
Policy level: face amount,
risk class, product etc.
Geo-demographic data
Credit attributes
Social Media
Electronic health records
Wearables
Facial analytics
Pharmacy: flags for
medication use, dosage,
frequency
Lab test: cholesterol, drug
testing
Attending Physician Statement:
current conditions, historical
record
Insurance Company data Public and Pooled data
3rd Party, Medical 3rd Party, Misc.
Modeling
Data
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U.S. experience adopting accelerated underwriting
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
Market trends
In the U.S. we see major carriers utilizing accelerated underwriting programs and mid-sized carriers are
following their leads
Increasingly becoming table stakes to play
Larger companies may develop their own in-house modeling expertise and mid size are more likely to rely
on reinsurers
They typically find that initial mortality uncertainty does not negate the increased sales, placement rates,
and expense savings
Characteristics of good program
Use several data sources
Reflexive application
Triaging applicants
Program evolves over time
Maintain same RC structure
Use of random holdouts
Considers sentinel effect
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Staffing Structure
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In developing PA capability, an important question is the role of data scientists vs.
domain experts
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
%Data science Domain experts
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Do you believe actuaries or data scientists are more equipped to handle PA at your
company, based on the following characteristics?
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97%
97%
7%
13%
13%
20%
20%
63%
30%
23%
10%
10%
3%
3%
30%
57%
64%
70%
53% 17%
Income statement focus
Insurance knowledge
Technical and software skills
Education and training
Mathematical and statistical aptitude
Problem solving skills
Communication skills
Actuary Data Scientist Both Neither
Base: Those currently using predictive analytics in at least one line of business (n = 30).
Source: Willis Towers Watson 2018/2019 Life Insurance Predictive Analytics Survey: Today and in the Future
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We suggest it’s domain expertise that helps decide
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Data science
Domain experts
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Future Emerging Uses
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We expect to see PA being used much more broadly in the future
Expanded use of areas mentioned earlier
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Analyzing Experience and
Setting Assumptions
Underwriting Aid
Sales and Marketing
Inforce Management/
Customer Profitability
Claims Analytics
willistowerswatson.com
One area we believe is particularly promising is in assessing drivers of customer
profitability
Applications on both new sales and inforce management
Focus below on inforce management
Premise: Understand the factors that drive customer profitability, examine what actions can be
undertaken to improve customer profitability
Suggest that developing and utilizing appropriate metric is critical
Suggest an EV type: i.e., PV of future profits
Needs to be measured at a fairly granular level
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Overview of what this might look like on a VA inforce block
Block: VA business written over past 10 years with guaranteed benefits – richness of which varies over time. Sold
through multiple distribution outlets
Business segments to analyze:
Issue age
Duration
Gender
Policy size
Which segments are adding most value
What actions might you consider taking
For your higher value customers
Route them to more qualified (i.e., better) customer service reps
Send them personalized messages
Offer additional products at discounted price
Have a company person visit them once a year (for very best)
Allow some exceptions to administrative rules
For your lower value customers – chase them out the door
For either
Exchange program
Change policy elements
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Richness of XB
Time of first withdrawal
Amount of withdrawal(s)
Life circumstances
Geographical
Distribution outlet
Fund mix
Service frequency
Existing company customer
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Contact
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V:\_Towers Watson Internal\RCS\People\Sands, Ann-Marie\POWER\SEAC-Predictive Analytics-2018.pptx
John Fenton, FSA, MAAA
Senior Director
5 Concourse Parkway, Floor 18Atlanta, GA 30328
T +1 678 684 0555E [email protected]