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:

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.

Agenda Why Data is important for PRT and Longevity Reinsurance Background: What goes into setting Mortality and Marital assumptions Case Study I – Number of Deaths Case Study II – Number of Exposures Mortality Improvement Assumption

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 Mutual insurance companies could see their solvency ratios drop All firms with pension plans see their funding ratios drop

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: 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?

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

Case Study 1: Company ABC – “Number of Deaths” ABC Pension plan had ~20,000 participants – an average age of 75 Anomalies in the early years where the “rate of mortality” was low Further data review led to a better pattern of deaths

Case Study 2: Company XYZ – “Exposures” Exposures for a given population are determined by when a participant “enters” the mortality study and “exits” the mortality study 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

Mortality Improvement Assumption – Data Sources Publicly available data sources Centre for Disease Control (CDC) Social Security Administration (SSA) Industry tables (MP2014 and 2015) used SSA Human Mortality Database Privately available data source Medicare (CMS)

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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