Longevity 12
Chicago 2016
Importance of High Quality Data for
Underwriting Pension Risk Transfer (PRT)
and Longevity Reinsurance Transactions
Thomas Jones
Prudential Financial, Inc.
Primary Competency:
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For financial professional or institutional plan sponsor use. Public Use Permitted.
Disclaimer
• Views expressed as part of this presentation are my own
and do NOT represent those of Prudential Financial, Inc.
• The numbers in this presentation are for illustration
purposes only. Reading into them may only yield
academic knowledge.
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For financial professional or institutional plan sponsor use. Public Use Permitted.
Agenda
1. Why Data is important for PRT and Longevity
Reinsurance
2. Background: What goes into setting Mortality
and Marital assumptions
3. Case Study I – Number of Deaths
4. Case Study II – Number of Exposures
5. Mortality Improvement Assumption
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For financial professional or institutional plan sponsor use. Public Use Permitted.
Why Data is Important for PRT and Longevity Reinsurance
The quality of Mortality and marital data is critical to accurately assess the
value of liabilities. For a PRT transaction, where there is a single premium
paid upfront, the assumption set applies for the next 30-50 years and hence
it becomes paramount to get accurate data.
Life expectancy off by 3
months for a 70 year old is
worth more the 1% in
liability amount.
• Insurance companies lose capital
• Expected profits are not realized and
stock price could fall
CF emergence shocked scenario
and baseline scenario
450.00
• Mutual insurance companies could see
their solvency ratios drop
400.00
350.00
300.00
Shocked Scenario
250.00
Baseline
200.00
• All firms with pension plans see their
funding ratios drop
150.00
100.00
50.00
-
1
11
21
31
41
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For financial professional or institutional plan sponsor use. Public Use Permitted.
Background I: What goes into setting
Mortality assumption
Base Mortality Table:
• Varies from one population to another
• Differences attributable to different working conditions,
geographical locations, level of benefits, industries, etc.
• Qx’s generated by age and gender in any given mortality
study are impacted by:
I.
II.
Number of deaths recorded in the experience period, and
Total amount of “exposures” for the given population
Mortality Improvement Assumption:
– Data available from sources such as Human mortality database
(HMD), Social Security Administration (SSA), Medicare (CMS),
Center for Disease Control (CDC)
– Does Mortality Improvement vary by population within a country
and even across countries in the long run?
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Background II: What goes into setting
Marital assumption
• Critical Assumption for Longevity Reinsurance transactions
• Spousal benefit is for the spouse as of the primary
participant’s date of death rather than the retirement date
• Presents tail risk in liability
• Most of the data sources on marital assumption do not
capture “young spousal risk”
• “Direct survey” data preferable to general data sources where
appropriate judgment needs to be employed
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Case Study 1: Company ABC – “Number of Deaths”
1. ABC Pension plan had ~20,000 participants – an average age of 75
2. Anomalies in the early years where the “rate of mortality” was low
3. Further data review led to a better pattern of deaths
Year over Year Rate of Mortality Comparison
5.0%
4.0%
3.0%
Original data provided
Post Data Revision
2.0%
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2
3
4
5
6
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Case Study 2: Company XYZ – “Exposures”
1. Exposures for a given population are determined by
when a participant “enters” the mortality study and
“exits” the mortality study
2. There are several issues that are common with the
exposures aspect:
– Lag in dependent/new retirees deaths
– Under-reporting of dependent deaths within same
year deaths
– Unknown entry dates
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For financial professional or institutional plan sponsor use. Public Use Permitted.
Mortality Improvement Assumption – Data Sources
Publicly available data sources
Privately available data source
Centre for Disease Control (CDC)
Medicare (CMS)
Social Security Administration (SSA)
Industry tables (MP2014 and 2015) used SSA
Human Mortality Database
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Mortality Improvement Assumption—Data Sources
and Results
• Below is a comparison of average improvements between
2000 and 2010 for various ages and data sources:
• While there is not as much differentiation between the data
sources in ages 65-75, we see differentiation emerge for ages
80 and beyond
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Important Disclosures
This document has been prepared for discussion purposes only. Prudential Financial, Inc. (PFI) does not provide legal, regulatory, or accounting advice. An institution
and its advisors should seek legal, regulatory, investment and/or accounting advice regarding the legal, regulatory, investment and/or accounting implications of any
of the strategies described herein. This information is provided with the understanding that the recipient will discuss the subject matter with its own legal counsel,
auditor and other advisors. This document does not constitute an offer or an agreement, or a solicitation of an offer or an agreement, to enter into any transaction
(including for the provision of any services).
Insurance and reinsurance products are issued by either Prudential Retirement Insurance and Annuity Company (PRIAC), of Hartford, Connecticut, or The Prudential
Insurance Company of America (PICA), of Newark, New Jersey. Both are wholly owned subsidiaries of PFI, and each company is solely responsible for its financial
condition and contractual obligations. PFI of the Unites States is not affiliated with Prudential plc, which is headquartered in the United Kingdom.
© 2016 Prudential Financial, Inc. and its related entities. Prudential, Prudential Retirement, the Prudential logo, the Rock symbol, and Bring Your Challenges are
service marks of Prudential Financial, Inc. and its related entities, registered in many jurisdictions worldwide. Prudential Retirement is a PFI business.
0297284-00001-00
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