When the Level Term Period Ends:Experience from Conversions & Post-Level TermJeremy Lane, FSA, CERA, MAAAStephen Abrokwah, Ph.D., FSA, CERA, MAAA

2

Policyholder behavior. Why should we care?

Improves product design

Delivers customer value

Provides a competitive advantage

Drives financial performance

3

Understanding policyholder behavior

Historical approach

Complex and requires detailed analysis Historically it’s been largely ignored

Early approaches have been ineffective Industry is starting to see the importance

Policyholderbehavior in the industry

Advanced Traditional Insurance Data• historical funding, policy loans,

account performance, distribution channel, etc.

4

Going beyond traditional insurance data is critical to success and helps understand each individual

Basic Traditional Insurance Data• age, gender, duration, amount, risk

class, premium mode, etc.

Product Characteristics• optionality, guarantees,

illustrations, etc.

Non-insurance Data• buying behavior,

associations, financial profile, social/ family characteristics, etc.

Economic/Market Data• interest rates, GDP,

unemployment, new product offerings, etc.

Data on Lapsed Policies• characteristics that help

understand how changing behavior changes outcomes

Insights from non-traditional data are leading to new solutions

Deep dive on policyholder behavior

• Clear patterns emerged that we can’t see from reinsurance data

• Helped identify possible IF Solutions pilots

Mortality impact of policyholder behavior

• Valuable insights to better understand the impact of anti-selective lapses

• Surrenders have the best mortality

Detailed UW data leading to better risk selection

• Drives new UW solutions such as Lab Requirement Model and TrueRisk enhancement

5

“The premium payments were way way too high and unaffordable, when my job situation changed.”

Source: Decision Technology Behavioural Survey, March 2019 (n = 755)

What were your main reasons for lapsing your Universal Life insurance policy?

Key Themes % SampleCouldn't afford premiums / premiums increased 3 1 %

Significant life event (e .g. death in family, loss of job) 1 8 %

Wanted something be tte r (e .g. policy, inves tment) 8 %

No longer wanted / needed 7 %

Needed the money for another purpose 4 %

Thought it an unnecessary expense / not worth it 4 %

Dissatis fied with provider / policy 4 %

Other 2 3 %

“Not worth keeping payments up, had ample savings & left employment”

“My husband and I both lost our jobs at almost the same time...... We couldn't afford to continue all the insurance

policies.”

“After medical treatments, my health improved and I decided not to continue with the payments to allocate it

to other expenses.”

“There were other things that I wanted to invest my money in at the time, so I stopped the insurance and

diverted my money and efforts towards that endeavour”

Financial concerns main reasons for lapsingUL insurance policies

6

7

Conversions: Summary of key drivers

8

Availability of the conversion

option

Policyholder notification

and incentives

Agent incentives

Type of products

available for conversion

Secondary market

Captive vs. independent

agents

Conversions later in the level te rm period contain increased anti-selection

Mortality loads initially grade down quickly and then gradually decline9

50%

100%

150%

200%

250%

1-3 4-6 7-9 10+

Duration after conversion experience by duration at conversion group

Early

Mid

EOLT

Mortality loads by duration at conversion

Conversion mortality loads are higher at younger ages and high face amounts

10

Mortality loads by other key factors

11

Average conversion rate

Range of conversion rates for T10 by company

Conversion rates jump at the end of the level period and contain a wide variance by company driven by differences in practice

Conversion rates are higher for shorter duration plans and older ages

Higher rates of conversions are associated with lower levels of anti -selection

12

Conversion rates by term plan and issue age

Conversion rates are similar for T10 and T15 and T2 0 is not yet very credible

T2 0 results influenced by changing conversion options over time

13

Conversion rates by term plan and issue age

Conversion rates are higher at less healthy classes due to inability to purchase new product for a lower price

14

Conversion rates by risk class

Pricing for Conversions

Direct Cedent Include excess mortality in permanent product pricing

Include excess mortality in term pricing as part of cost of conversion

ReinsurerReinsurance treaties covers conversions in two major ways

Keep conversions in original treaty at point -in-scale YRT rates Coinsurance versus YRT treaty considerations

Cover conversions as part of perm treaty it’s converting to (point -in-scale) Reinsured versus non-reinsured plans considerations

Company practices strongly influence conversion results

Anti-selectionincreases by duration of conversion

A significant cost of the conversion option is driven by end of level period conversions

Key TakeawaysGood data is essential to study conversions

Variation among companies (agent vs broker)

17

Post-Level Term Experience

18

Understanding in force policyholder behavior is …

a significant opportunity with the potential to

have a real impact.

increasingly important given the

current market environment.

complex and requires us to go

beyond traditional thinking.

What are we missing today that will seem obvious tomorrow?

Improve consumer value, retention, and profitability

19

Developed a better model

Investigated the drivers that caused lapsation

Developed an innovative solution

More accurate ly predict behavior

Improved cost to consumers, increased

retention, and improved profitability

Implemented solution with 2 5 + clients

Customer Events

Source: Decision Technology Behavioural Survey, March 2019 (n = 755); Bolder events are statistically significant (p < .05); Modelled values shown

Other provider got in contactNegative WOM

Agent recommended new product

Researched other provider

Saw other provider ad

Visited other provider's website

Read negative provider review

Missed premium payments

Lapsed other financial product

Changed payment method

Positive WOM

Bad customer service experience

Didn't understand provider comms

Changed payment frequency

Read other provider positive review

Discarded provider comms

Interest rates decreased

Experienced problems with …

Premium price increased

Failed to receive policy comms

Read positive provider review

Positive other provider WOM

Reduced death benefit

0%

10%

20%

30%

40%

50%

60%

0% 5% 10% 15% 20% 25% 30% 35% 40%

Laps

ers

Annual Event Frequency

“Missing payments that resulted in

cancellation.” “To shop around for others.”

“Premium kept going up and the coverage kept

going down.”

Changes in premium amounts and/or frequencyare key drivers of lapse

20

Model to more accurately determine impact of anti-selection

• Key observations of analysis– Very similar health across first 90% of the population

– The vast majority of the anti -selection is driven off the worst 5% which will continue to pay with premium jumps > 10X

– If the bottom 5% of people don't lapse at duration 10, it implies a high effectiveness on lapse (~80% effectiveness)

– Anti-selection is more a function of who doesn't lapse than who does

– Almost all lapses are anti -selective even if the primary reason they occur are not related to health

21

Swiss Re performs frequent PLT mortality and lapse experience studies based on seriatim data from our reinsurance business

Analysis focused on areas where we have premium information

T10 T15 T20Companies 35 19 5

Issue years 1990 -2007 1990 -2003 1992 -1997

Exposure years 2007 -2017 2007 -2017 2011-2017

PLT claims 3,891 870 98

Dur T+ lapses 675,776 240,636 53,260

22

Comprehensive research, credible results

Additional VariationsVary by theseparameters

No Other Variance

Premium Mode

Premium Jump Ratio

Risk Class & Premium Jump Ratio

No Variance 9Issue Age & Level Period 5 1 1 1

Level Period 5 1Issue Age & Risk Class 2 1

Risk Class 1Premium Jump Ratio 1 1

Source: 2013 SOA Industry lapse assumptions survey

2 3

Only a few companies vary assumptions by the primary driver –Premium jump ratio

Lapse rates by plan and premium jumps

24

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1 2 3 4 5 6 7 8-10 10+

Comparison of shock lapse rates for T10 , T15 , & T2 0 by premium jump

T10 T15 T20

*Note: There is very limited experience for T2 0

25

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

<50 50-59 60+ <50 50-59 60+ <50 50-59 60+ <50 50-59 60+

1.01x - 3x 3.01x - 5x 5.01x - 7x 7.01x +

T10 shock lapse rate by premium jump and attained age by amount

Premium jump

Lapse rates by premium jump and attained age

26

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Mon

thly

Qua

rterly

Sem

i-Ann

ually

Annu

ally

Mon

thly

Qua

rterly

Sem

i-Ann

ually

Annu

ally

Mon

thly

Qua

rterly

Sem

i-Ann

ually

Annu

ally

Mon

thly

Qua

rterly

Sem

i-Ann

ually

Annu

ally

1.01x - 3x 3.01x - 5x 5.01x - 7x 7.01x +

T10 shock lapse rate by premium jump and premium mode by amount

Premium jump

Lapse rates by premium jump and direct premium mode

27

-

50

100

150

200

250

300

350

400

0%

250%

500%

750%

1000%

1250%

1500%

1750%

2000%

2250%

2500%

1.01x -2x

2.01x -3x

3.01x -4x

4.01x -5x

5.01x -6x

6.01x -7x

7.01x -8x

8.01x -9x

9.01x -10x

10.01x -11x

11.01x -12x

12.01x -13x

13.01x -14x

14.01x -15x

15.01x +

Post Level Mortality (Dur 11 -12 ) as % 2 0 0 8 VBT Table (by number)

Swiss Re Reinsured Study Swiss Re Reinsured claims counts

*A/ E's are based on by number. A/ E's by amount will be approximate ly 4 to 5 % higher by amount

T10 mortality experience by premium jump

Pers

iste

ncy

(Bar

s)

Cum

ulat

ive

Prem

Ratio

("D

ots"

)

Same duration 15 premium

Higher persis tency

Source: Swiss Re 's Reinsurance study28

-

2.00

4.00

6.00

8.00

10.00

12.00

0%

20%

40%

60%

11 12 13 14 15

Pers

iste

ncy

Post level term persistency by duration

Graded incr. persistency Cliff incr. persistencyGraded incr. cumulative prem ratio Cliff incr. cumulative prem ratio

Understanding policyholder behaviorPersistency is path dependent

Post level termLoss ratio increases by premium jump

Post level termLoss ratio increases by premium jump

Sources: Swiss Re 's Reinsurance study2 9

-

500

1,000

1,500

2,000

2,500

0%

50%

100%

150%

200%

Prem Jump < 5 Prem Jump 5-10 Prem Jump 10+

Ratio of Death Benefits to Direct Premiums

Loss Ratio Number of Claims

-

2,000

4,000

6,000

8,000

10,000

12,000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Dol

lars

Policy Year

16.4x jump

Year 11: $5,575

30

Annual premium for a T10 age 45 male best preferred $500k policy*

Years 1-10: $340

*Hypothetical example

Understanding policyholder behaviorSevere premium jumps = severe lapsation issues

Sources: RGA, SCOR, Swiss Re, & 2014 SOA studies

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1.01x - 2x 2.01x - 3x 3.01x - 4x 4.01x - 5x 5.01x - 6x 6.01x - 7x 7.01x - 8x 8.01x - 10x 10.01x+

T10 shock lapse rate by premium jump ratio by amount

RGA SCOR Swiss Re SOA

30

Post Level Term - Understanding policyholder behavior Lapse rates correlate with premium jumps

• Understanding PHB is key to optimizing value on in force policies and it can have significant economic impact on future profitability

• PLT consultation to manage post level term premiums

• A lot of opportunity remains for improvement particularly on permanent policies

32

(250) (200) (150) (100)

(50) - 50

100 150 200 250

11 12 13 14 15 16 17 18 19 20

Cas

h Fl

ow (T

hous

ands

)

Net direct cash flows by duration for 1 billion of volume entering the PLT period

BeforeIntervention

AfterIntervention

– Led to increasing the in force value by $ 0 .5 0 to $ 1.0 0 per 10 0 0 for clients and re insurers

– Overall provides a better value for policyholders compared to exis ting product s tructure

Post Level TermUnderstanding and reacting to policyholder behavior

33

Overall provides a lower cost for policyholders compared to existing product structure.

Led to enhancing the in force value for direct writers and reinsurers

Understanding PHB is key to optimizing value on in-force policies and it can have significant economic impact on future profitability

PLT consultation to manage post level term premiums

Understanding policyholder behavior

A win-win -win solution

(250) (200) (150) (100)

(50) - 50

100 150 200 250

11 12 13 14 15 16 17 18 19 20

Before Intervention After Intervention

-

2

4

6

8

10

12

14

16

10 11 12 13 14 15 16 17 18 19 20

Annual premium for a T10 age 45 male best preferred $500k policy*

Original premiums Managed premiums

Net direct cash flows by duration for 1 billion of volume entering the PLT period*

*Hypothetical example

34

Understanding policyholder behavior

Solutions to improve retention

Communication

• Use Behavioral Economics to influence action

• Target those likely to lapse or surrender with specific recommendations

• Call center training

Engagement• Wearables• Genetic testing• Wellness options• Agent (re-)engagement

Incentives• Design a product for those

that lapse or surrender with reduced underwriting

• Product exchange• Offer alternative products to

better fit current goals• Programs to get

policyholders back on track after a missed premium

35

• Product design influences which solution works best

• Market/Environmental context is key

• Cost/ Benefit analysis is important for prioritization

• Data Collection and IT systems require flexibility

• Ensure partnership with Legal & Compliance

Considerations when implementing solutions

Pollingwww.Slido

# FBI

Your key take-away from this session?

©2019 Swiss Re. All rights reserved. You are not permitted to create any modifications or derivative works of this presentation or to use it for commercial or other public purposes without the prior written permission of Swiss Re.

The information and opinions contained in the presentation are provided as at the date of the presentation and are subject to change without notice. Although the information used was taken from reliable sources, Swiss Re does not accept any responsibility for the accuracy or comprehensiveness of the details given. All liability for the accuracy and completeness thereof or for any damage or loss resulting from the use of the information contained in this presentation is expressly excluded. Under no circumstances shall Swiss Re or its Group companies be liable for any financial or consequential loss relating to this presentation.

We’re smarter together