predictive analytics and accelerated underwriting survey ...€¦ · predictive analytics and...
TRANSCRIPT
Predictive AnalyticsandAccelerated Underwriting Survey Results
Al Klein
October 6, 2017 IAA Mortality Working Group
Agenda
Background
Results
2
Predictive analytics
Accelerated Underwriting
Concluding thoughts
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
3
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
4
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
5
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▪Original survey is out-of-date as additional companies have implemented new programs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/
6
Predictive AnalyticsSurvey Results
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
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
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
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
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
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
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)
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
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%
16
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)
Accelerated UnderwritingSurvey Results
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
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
20
Accelerated Underwriting Decision-making
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)
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)
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)
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)
Individuals/Areas involved in developing Accelerated Underwriting programs
26
1 Internal Underwriter (all 8)
2
3
Internal Actuary (7)
Internal Marketing (4)
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
27
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
28
Biggest challenges encountered in developing AU programs
29
1 Data sources (4)
2
3
Justifying cost/benefit analysis (4)
Implementation (3)
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.
30
