Make sure you capture the data dividend @ClubVita #datadividend - - PowerPoint PPT Presentation

make sure you capture the data dividend
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Make sure you capture the data dividend @ClubVita #datadividend - - PowerPoint PPT Presentation

Thank you for joining us the webinar will start shortly Thinking about a buy out in 2020? Make sure you capture the data dividend @ClubVita #datadividend January 30 th , 2020 linkedin.com/company/club-vita 12 noon ET Club Vita LLP is an


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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.

January 30th, 2020 12 noon ET

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

Thinking about a buy out in 2020? Make sure you capture the data dividend

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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

Douglas Anderson Matt McDaniel Nate Luepke Bobby Gentry @ClubVita #datadividend linkedin.com/company/club-vita

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What do the results of an auction process look like?

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Advisor’s perspective on variation in prices

Deal pricing varies based on many factors, including:

  • Transaction size
  • Benefit size
  • Plan design
  • Seasonality

But, insurer’s view of longevity is a key driver, and may help explain outlier results both on low (93%) and high (105%) end

Premium relative to “market” liability*

Two thirds

  • f deals

between 98% and 103%

*Source; Mercer, US: Data based on 64 US retiree-only deals from May 2016 to October 2019. Market liability defined using Mercer Yield Curve and most recent SOA mortality tables with collar adjustment.

  • Demographics
  • State of issue
  • Investment portfolio
  • Insurer capacity
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So, how do you increase the likelihood of getting a price at the bottom of that range?

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Three sources of gains

Cleaner data More data fields Longer back history

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Deep cleansing: What about tracing missing participants?

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Three reasons why

  • 1. Reduce
  • utgoings

(improve cash flow) +

+

  • 2. Shorter

life expectancy (reduce reserves)

  • 3. More

underwriting confidence (lower risk premium)

=

$$$m benefits to bottom line

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How much can ZIP codes change pricing?

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Society of Actuaries tables

Zooming in on diversity

Club Vita 5-digit ZIP code model Club Vita 9-digit ZIP code model

  • High/low pension

amount

  • Blue/white collar

One Rating factors Factors used at one time

  • 5-digit ZIP code
  • Pension amount
  • Blue/white collar

All

  • 9-digit ZIP code
  • Pension amount
  • Blue/white collar

All Range of healthy male life expectancy at age 65 2.7 years

  • Pri-2012

5.4 years*

7.6 years

Club Vita 2020+ model

9.6 years?

  • Adds salary

amount as affluence measure All

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Impact of adopting a ZIP code longevity model

Impact of moving from RP06 to 9 digit ZIP US VitaCurves (both MP18 improvements)

Increasing plan size (benefits in payment)

Source: Club Vita, Zooming in on ZIP codes

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With ZIP codes ….

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* Small benefit retirement annuity purchase Source: Club Vita; for a sample large US plan, the effect on deciles of liabilities of moving from plan’s assumed accounting assumption to individual VitaCurves for baseline longevity

Split of liability by pension amount Largest pensions Smallest pensions

  • 8%
  • 6%
  • 4%
  • 2%

0% 2% 4% 6% 8%

Individual assumptions vs 'average' assumptions

Quantifying the health bias in SBRAP*

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What happens if we use salary instead of annual pension?

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  • 5%
  • 4%
  • 3%
  • 2%
  • 1%

0% 1% 2% 3%

Overpriced Underpriced

Changes in pricing or reserves from adding salary as a rating factor

(Based on 115 different pension scheme portfolios of pensioners and dependants)

Increasing portfolio size Loss of 1/3 of profits? £63m loss (0.6%) in £11bn business written £67bn missed business? Portfolios which could be written at average reduced premium of 1.4% <£200m Increasing portfolio size Loss of 1/3 of profits? £63m loss (0.6%) in £11bn business written £67bn missed business? Portfolios which could be written at average reduced premium of 1.4%

No impact

Adding final salary as a covariate

Introducing final salaries changed portfolio valuation by +/-2%. We now get salaries in 70% of UK records.

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Do participant options tell us something about the type of people they are?

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Proxies for marital status

+ 1.3 years

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Male Pensioners

Joint life Single life

Plan A Plan B Plan C

. . . . .

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Female Pensioners

Joint life Single life

Plan 1 Plan 2 Plan 3

. . . . .

+ 1 year Retirees who opt into a joint-life (with a contingent survivor pension) live longer Mix varies considerably between plans

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Are people taking lump sums random?

In UK, Club Vita’s non- pensioner (deferred vested) data has enabled more competitive pricing for deferred annuities.

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More back history

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We can quickly see how far back is reliable …

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12 13 14 15 16 17 18 19 20 21 22 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Life Expectancy at age 65 (men)

Comfortable Making-Do Hard-Pressed

Finessing trend assumptions

Narrowing of socio- economic gap Strong, stable improvements for all Resilience of higher socio-economics

~ higher incomes ~ live in most deprived areas ~ everyone else

By end 2020, Club Vita will provide socio-economic improvements for US retirees

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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 Conservatively, 2% or $10m on $500m

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Any questions?

Douglas Anderson Matt McDaniel Nate Luepke Bobby Gentry @ClubVita #datadividend linkedin.com/company/club-vita