predictive analytics use in life & annuity market 2018 1115... · amount of premium over $3...

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.


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.

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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


Overview

© 2018 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.3


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



Analyzing Experience and

Setting Assumptions

Underwriting Aid

Sales and Marketing

Inforce Management/

Customer Profitability

Claims Analytics


Results from WTW Survey show use of PA varies materially



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%+


Predictive modeling will continue to improve quantification of cost in experience analysis



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


Use of PA for mortality and morbidity is most prominent in annuities,

but significant growth is expected in individual life insurance



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).


Now

In two

years Growth

55% 91% 65%

40% 73% 83%

28% 64% 129%

23% 75% 226%

19% 56% 195%


Pension Mortality Case Study



The U.S. PRT market has grown significantly since 2013



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


In order to help assess the appropriate mortality assumptions, Willis Towers Watson

(WTW) developed the WTW Pension Mortality Analysis (“PMA”)



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


Our PA includes the following – most of which is typically provided in census included

with RFQ



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


Here is a sample of our predictive modeling findings



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)


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




Accelerated Underwriting



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




Use of PA for underwriting is highest in individual life, but is growing in other lines



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).


Now

In two

years Growth

52% 92% 77%

44% 63% 43%

27% 73% 170%

25% 56% 124%


The use of data in life insurance underwriting is changing rapidly



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


U.S. experience adopting accelerated underwriting



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


Staffing Structure



In developing PA capability, an important question is the role of data scientists vs.

domain experts



%Data science Domain experts


Do you believe actuaries or data scientists are more equipped to handle PA at your

company, based on the following characteristics?



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).



We suggest it’s domain expertise that helps decide



Data science

Domain experts


Future Emerging Uses



We expect to see PA being used much more broadly in the future

Expanded use of areas mentioned earlier



Analyzing Experience and

Setting Assumptions

Underwriting Aid

Sales and Marketing

Inforce Management/

Customer Profitability

Claims Analytics


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




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



Richness of XB

Time of first withdrawal

Amount of withdrawal(s)

Life circumstances

Geographical

Distribution outlet

Fund mix

Service frequency

Existing company customer


Contact



John Fenton, FSA, MAAA

Senior Director

5 Concourse Parkway, Floor 18Atlanta, GA 30328

T +1 678 684 0555E [email protected]

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