predictive analytics and accelerated underwriting survey ...€¦ · predictive analytics and...
TRANSCRIPT
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Predictive AnalyticsandAccelerated Underwriting Survey Results
Al Klein
October 6, 2017 IAA Mortality Working Group
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Agenda
Background
Results
2
Predictive analytics
Accelerated Underwriting
Concluding thoughts
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Background
▪Survey was conducted in June/July 2016 of US companies
▪We initially had fewer responses than we wanted so we called companies we knew had implemented programs and asked them to participate▪Response to this follow up was good and we believe most of the companies that had a program when we conducted the survey participated
▪Responses were received from both direct companies and reinsurers who helped implement programs
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Background (cont’d)
▪Goal was to learn about company practices on three timely issues:
▪Predictive analytics
▪Accelerated underwriting
▪“The use of tools such as a predictive model to waive requirements such as fluids and a paramedical exam on a fully underwritten product for qualifying applicants without charging a higher premium”
▪Enhanced underwriting – Insufficient response
▪“The use of supplemental information (e.g., criminal history, credit rating, prescription histories) and a predictive model to refine the underwriting process for a simplified issue product”
▪Avoided questions on proprietary information to maximize participation
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Background (cont’d)
▪Started both sections of the survey with a large question to establish what was:
▪Implemented
▪Being worked on
▪Not worked on or considered
▪Focus of all subsequent questions was on the programs implemented by the respondents
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Caveats
▪Original survey is out-of-date as additional companies have implemented new programs
▪However, I believe the information is still good and useful for both those with programs and those considering new programs
▪ I will be covering results at high level
▪Please find complete survey at:
▪https://www.soa.org/experience-studies/2017/predictive-analytics-underwriting/
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Predictive AnalyticsSurvey Results
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Predictive Analytics
Implementation Choices
Implemented
Working on and
Plan to implement within 1 year
Plan to implement within 1-2 years
Plan to implement longer than 2
years
Not sure if will implement
Not currently working on but
Considering it
Considered it and/or worked on it
but decided not to do it
Not considering it
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Quick Summary of 2015 PA Results
34 companies responded to the survey
26 of these companies implemented one or more
PA programs
9
117 PA programs were implemented
Two companies implemented the most PA programs
(12 each), others implemented 1-10 programs
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Predictive Analytics – Marketing
Marketing Programs
Program ImplementedWorking
on
Not working
on but
considering
Not working
on and not
considering
Total
Customer more likely to buy 12 6 6 5 29
Cross selling 10 2 7 9 28
Target market determination 9 8 4 7 28
Up selling 9 3 7 10 29
Customer less likely lapse 7 9 5 8 29
Customer health profile 5 5 11 7 28
Agent selection/hiring 4 6 4 9 23
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Predictive Analytics – Underwriting
Underwriting Programs
Program ImplementedWorking
on
Not working
on but
considering
Not working on
and not
considering
Total
U/w risk class 12 9 7 5 33
Deciding on u/w
requirements9 11 8 2 30
Stretch criteria for
selecting u/w class5 4 10 13 32
Business decisions 1 2 4 18 25
Table shave 1 1 1 21 24
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Predictive Analytics – Post-Issue Mgmt
Post-Issue Management Programs
Program ImplementedWorking
on
Not working
on but
considering
Not working
on and not
considering
Total
In force mgmt. – pre-lapse 7 6 7 9 29
Targeted conversion 5 2 7 12 26
For term, post-level premium
term conservation mgmt.2 7 8 11 28
Agent monitoring/mgmt. 2 6 11 7 26
In force mgmt. – post-lapse 2 5 8 11 26
In force mgmt. – Other
customer interaction1 2 4 8 15
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Other types of PA programs that have been implemented
13
1Marketing (4) – Attract new reinsurance business, Prospecting models, Identifying prospects, UL vs. Term Prospecting
2
3
Underwriting (2) – implemented another type of underwriting PA program, Working on something
Post-issue Management (8) – Implemented another, working on other, or considering another type of post-issue management program (6), Ongoing claim study, Considering for business considerations
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Sources/types of data used to develop PA Models
14
1
Vendor
(17)
2
Financial
(16)
3
Lifestyle
(13)
4
Application
(12)
5
Internalexperience
(12)
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Individuals/Areas involved in developing PA models
15
1Marketing – Internal Actuary, Marketing, Data scientist/statistician
2
3
Underwriting – Internal Actuary, Internal Underwriter, Marketing
Post-issue Management – Marketing, Data scientist/statistician, Internal Actuary
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Other Interesting findings
▪Most PA programs were implemented within the last few years, but some PA marketing programs were implemented earlier
▪Most PA programs were implemented as a pilot and many of the underwriting and post-issue management programs remain as a pilot
▪Most PA programs impacted only 0-10% of the overall business and none impacted more than 75%
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Top Obstacles in Developing PA Models
17
1
Data Sources
(20)
2
Agent Buy-in
(13)
3
Internal User
Buy-in
(13)
4
Implement-ation
(12)
5
Designing/ Building
the Model
(12)
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Accelerated UnderwritingSurvey Results
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Accelerated Underwriting
Accelerated Underwriting (AU) Programs
ImplementedWorking
on
Not working
on but
considering
Not working on
and not
considering
Total
10 12 1 3 26
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Accelerated Underwriting Program Limits
▪Maximum issue age ranged from 35 to 85 and most common was 60
▪Maximum face amounts ranged from $100K to $3M, with most common $1M
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Accelerated Underwriting Decision-making
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Data sources used for AU decision-making
22
1
MIB Checking Service
(7)
2
MVR
(7)
3
Rx History
(7)
4
Application
(6)
5
Lifestyle
& MIB IAI
(5 each)
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Most important data sources for Accelerated Underwriting decision-making
23
1
Rx History
(6)
2
Application
(6)
3
MVR
(5)
4
MIB Checking Service
(4)
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Data sources used for Risk Class decision-making
24
1
MVR
(7)
2
Rx History
(7)
3
Application
(6)
4
MIB Checking Service
(6)
5
Financial
(5)
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Most important data sources used for Risk Class decision-making
25
1
Rx History
(7)
2
MVR
(6)
3
Application
(5)
4
MIB Checking Service
(5)
5
Personal History Report
(4)
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Individuals/Areas involved in developing Accelerated Underwriting programs
26
1 Internal Underwriter (all 8)
2
3
Internal Actuary (7)
Internal Marketing (4)
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Other Interesting findings
▪5 of 9 accelerated underwriting programs were implemented as a pilot program and one remains as a pilot program
▪4 of 9 companies randomly check some applicants to test their assumptions and/or model
▪4 of 8 use predictive analytics in the decision-making process for AU programs
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Other Interesting findings (cont’d)
▪7 of 8 indicated time to issue decreased
▪6 of 8 indicated they were not sure if mortality changed since implementation of the AU program
▪7 of 8 plan to expand their AU programs
▪4 of 8 indicated that their reinsurers participated in the AU program
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Biggest challenges encountered in developing AU programs
29
1 Data sources (4)
2
3
Justifying cost/benefit analysis (4)
Implementation (3)
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Concluding thoughts
Both PA and AU programs are growing at a rapid pace and I expect that to continue over the next several years.
I also expect to see new methodologies and hybrid approaches emerge over this same time period.
I believe this is a great time to be a PA actuary and to offer creative and constructive solutions.
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