How to capture the pension risk transfer data dividend - - PowerPoint PPT Presentation

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Jan 17, 2023 151 likes •380 views

Thank you for joining us the webinar will start shortly How to capture the pension risk transfer data dividend http://linkedin.com/company/club-vita/ February 26 th , 2020 12 noon EST @ClubVita #datadividend Club Vita LLP is an appointed

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Club Vita LLP is an appointed representative of Hymans Robertson LLP, which is authorised and regulated by the Financial Conduct Authority and Licensed by the Institute and Faculty of Actuaries for a range of investment business activities.

February 26th, 2020 12 noon EST

Thank you for joining us – the webinar will start shortly @ClubVita http://linkedin.com/company/club-vita/

How to capture the pension risk transfer data dividend

#datadividend

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Today’s aim Q: Why are we bothering to create better data? A: Because cleaner, more complete data will save you money, creating a high ROI. We would like to show you how …

Plan sponsor Advisor

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Today’s panel

Richard Brown Benoît Labrosse Tom Ault Paul Forestell

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What factors drive group annuity pricing?

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Group annuity pricing is driven by several factors – many of which plan sponsors have control over

▪ Transaction size ▪ Timing of transaction ▪ Tranching of transaction ▪ Data cleanliness and completeness ▪ Assuris coverage ▪ Transaction day process ▪ Insurers’ confidence a transaction will occur ▪ Investment portfolio ▪ Transparency of process

Plenty of control

▪ Underlying population, demographics and location ▪ Benefit provisions ▪ Insurers’ preferences ▪ Insurers’ assets

Little control

Source: Data extract from a sample of 30+ Morneau Shepell transactions over 2018 and 2019

Sheer luck

▪ Insurers not winning a specific deal ▪ Insurers’ not meeting their annual objectives

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So, how can better data help ensure plan sponsors receive the best possible price?

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Three sources of gains

Cleaner data More data fields Historical experience

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Deep cleaning: Do you really know who your members are?

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Key sources of membership uncertainty

Unlocated deferred vested (DV) members

Basic data (e.g., date of birth) likely more unreliable than regular DVs

Uncertainty regarding second lives

Unreliable form of pension data – could single lives actually be joint?
Have predeceased spouses been captured?

Unnotified deaths

Reduced by comprehensive administrative practices, including existence

checking programs

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Three reasons why

1. Reduce

future payments (improve cash flow) +

+

2. Shorter

life expectancy (reduces reserves)

3. More

underwriting confidence (lower risk premium)

=

$$$m benefits to bottom line

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What can your data tell you about the longevity of your members?

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Tapping into the wealth of data in administrative records

Age Gender Job type Union or non-union Industry Retiree vs. Survivor Pension Salary Postal code Province Marital status Health at retirement Education

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1.2 1.3 1.9 2.0 3.6 3.7 4.6 7.9 1.3 1.0 1.4 1.2 2.0 3.4 3.4 6.5

1 2 3 4 5 6 7 8 9

Pensioner vs survivor Joint life vs single life Non-manual vs manual High vs low pension band High vs low salary band Longest vs shortest lived longevity group Regular vs disabled pensioner Most granular pensioner model

Additional Y ears of Period Life Expectancy

Differences in period life expectancy at age 65 based on CV18 VitaCurves

Males Females

What does data tell us about longevity expectations?

Pen. Type

Form Occupation Pension Salary Postal Code

Ret. Health

All Required data fields

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Impact of capturing longevity factors

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Lowering risk premium through better longevity insights

Postal codes

Postal code has

become a standard PRT data field

An address search can

increase coverage and accuracy

Occupation

High-level occupation

information may be

f limited value
Objective member-

specific data is the most powerful

Salary

Much better

representation of affluence than pension

Salary data isn’t

always readily available but partial or related data can still be helpful

Retirement health

Disabled pensioners

experience much higher mortality

Captured via plan-

specific disability pension benefits and/or status (e.g., LTD) prior to retirement

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What can historical experience tell us?

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Assessing historical experience

500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 2010 2011 2012 2013 2014 2015 2016 2017 2018

Year Ending December 31st

Projected vs. reported historical pensioner/survivor headcounts

Male Survivor Female Survivor Male Pensioner Female Pensioner Reported Counts

0.1% 1.0% 10.0% 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018

Crude mortality rate (log scale)

Male mortality for your Plan compared to VitaBank

80-89 - VitaBank 80-89 - Your Plan 70-79 - VitaBank 70-79 - Your Plan 60-69 - VitaBank 60-69 - Your Plan

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Dos and don’ts of historical experience for PRT Dos

Offer high quality historical experience data regardless of plan size
Verify that all historical deaths (and other exits) have been captured and

reconcile to past plan membership statistics

Provide as much data on longevity factors as possible

Don’ts

Rely heavily on valuation data for historical mortality experience
The quality of valuation-sourced mortality data is often poor
Easy to miss deaths, particularly those soon after pension

commencement and for original pensioner of survivors

Provide historical experience that hasn’t been fully validated/assessed

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So, what’s the overall data dividend?

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Good data delivers several benefits

Cleanliness (extra) Covariates

+

Convenience for insurers Confidence Cost reduction for plan sponsors?

+

Competition Good data vs bad data could mean savings of 2% (or $10m on $500m)

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