Richard Martin

The Price of Time: Affluence and Longevity Modelling

In this article, we delve into these insights and explore the implications for pension funds navigating the intricate landscape of longevity risk assessment.

The unique angle brought by considering council tax bands adds depth to our understanding of the intricate relationship between financial well-being and life expectancy. It has long been attested that affluence has a strong influence on both morbidity and mortality of individuals. This exploration indicates that council tax bands capture some of these effects. Individuals who are registered as dying in residential properties with higher tax bands tend to achieve older age brackets more frequently than their counterparts in lower bands, prompting contemplation and quantification of the multifaceted factors influencing our journey through time.

While conventional models often focus on geographic factors down to the ward level, the council tax approach, which can be offered at a postcode sector level, offers a more discriminating view. This departure from the norm provides access to the localized dynamics that contribute to variations in life expectancy, providing a richer context for understanding the nuanced impact of affluence on longevity.

As an example, the table above presents a comprehensive breakdown of cumulative probabilities of death based on both age bands and council tax bands in the city of Manchester. Similar tables can be constructed for a variety of different geographies that can then be straightforwardly imported into existing longevity models structures.

Age Bands (Column A)
The table categorizes individuals into age bands of 5 years each, allowing for a detailed examination of mortality rates across different life stages.

Mortality Rates by Council Tax Bands (Columns B-F)
Columns B through F break down the cumulative percentage of total deaths for each age band based on the council tax band of the residence.

e.g. Column B represents council tax band A (the least affluent) and Column F for council tax bands E and above (the most affluent).

Odds Ratio (Column H-I)
Column H provides the odds ratio for individuals in a specific age band living in a property with council tax band A compared to those in council tax bands E and above.

For example, the odds ratio for the age band 60-64 years is 379. This suggests that individuals in this age group residing in a residential property with council tax band A have significantly higher odds of mortality compared to those in the same age group living in residential properties with council tax bands E and above. Almost 4 times more likely in this case.

In summary, this cumulative probability Table highlights the interplay between age and affluence as indicated by different council tax bands. It allows readers to discern patterns and make informed amendments of mortality risks across various demographic segments.
The extended use of data that discriminates lifestyles that impact upon longevity, enables: 

  1. More robust assessment of longevity risk. 
  2. Increased accuracy of fund valuations.
  3. Estimation of bespoke risks on lower value funds.
  4. Reduced reliance on default risk premiums.

For pension funds navigating the terrain of longevity risk during valuation, these findings offer a valuable lens through which to assess and manage risk.

While the statistics used within this piece are of the Manchester area only MM have a complete view across the UK, which can be a key variable in helping to improve longevity calculations.

Please contact MM. for more information on how you can assess and use this information

Securing Tomorrow – A Whitepaper on Member Data Quality and Pension De-Risking

In an era marked by evolving demographics, and ever-shifting regulatory frameworks, the world of pension management faces unprecedented challenges. For both organizations and individuals, securing financial stability during retirement has become an increasingly complex and multifaceted undertaking.

This is particularly true for schemes tasked with managing pension plans, who must navigate the intricate labyrinth of pension de-risking strategies to safeguard their members’ financial futures.

Traditional defined benefit pension plans, once seen as the gold standard for retirement, now face significant challenges in an ever-changing economic and regulatory environment. It is within this landscape of uncertainty and transformation that pension buy-ins and buyouts have emerged as increasingly popular strategies for securing those financial futures of scheme members.

The concept of pension buy-ins and buyouts is rooted in the imperative of reducing pension scheme risks and liabilities, while simultaneously enhancing the financial stability and predictability of retirement benefits. These strategies involve transferring the responsibility of paying out pension benefits from the sponsoring organization to insurance companies, thereby offloading the risks associated with investment performance, longevity, and market fluctuations.

Who are MM?

MM. was established in 2006 as one of the original adopters of the UK Government’s DDRI licence, providing mortality screening solutions to pension funds. We have grown and developed as a data services organisation providing products like MM. Spouse, MM. Beneficiary & MM. Residence which are business critical to our clients responsible for ongoing data management and data preparation for end-game or pension dashboards.

MM. has built an experienced professional services team who help clients resolve issues around loss of contact with members. This team also help our clients manage data quality problems found during the Pension Dashboard roll-out or those going through a risk transfer process.

MM. are dedicated to Information Security and are ISO 27001 certified.

What is the purpose of this whitepaper?

This white paper details the average member-data quality standards across scheme databases analysed over the past 6 months. MM. are linking scheme member data to sources of truth to highlight the quality of the data provided and potential improvements that can be achieved.

The purpose of this report is to highlight the typical data quality problems faced when preparing for endgame and the impact this may have on both valuations and write-out’s during a buy-in/buyout.

A background of Bulk Annuities sector

To date there have been 103 transactions in 2023 which total over £23bn of risk transfer between a scheme and an insurer. The calculation of future liabilities and the accuracy of longevity models for the member and their dependents are vital when valuing the scheme.

A typical defined benefit scheme often relies heavily on the data provided by the member at the point of joining the scheme. As time elapses data degrades at an exponential rate and the scheme becomes unaware of the relationship status and location of its members. This has a major impact on the longevity predictions and liability calculations of the scheme and its members.

Further to the impact on fund value, the degradation of data also has a major impact on address quality and therefor member communication. As a scheme is legally required to mail out certain documents on a yearly basis as well as needing to write-out to members during a Buy-in or Buy Out to inform of the changes, the quality and currency of the members’ address is critical.

What is MM. Spouse Append?

MM. Spouse identifies whether a member is still alive, their marital status and if the current address is up-to date. The service categorises members’ relationship status into the below categories and appends the given spouse’s personal information.

If a member and their beneficiaries are entitled to future payments this will have a significant impact on calculating future liabilities. The accuracy of this calculation is of paramount importance to ensure value for money is being achieved for both parties.

The socio-demographic indicators of a member such as their age, where they live, their profession, whether they are married and the number of persons living in the house has a material impact on longevity calculations for both the member and their spouse. MM. Spouse will append these details to a member record in a format easily interpreted.

What is the average data quality and completeness of deferred members?

MM. Have collated results from client data over the past 6 months to give an overview of the scale of data quality problems found within a typical scheme member database.

MM. Spouse categorises a member’s relationship status into one of 7 categories, as detailed below.

Append StatusDefinitions%
Living in Long-Term RelationshipMember matched to a person for a continued period at the
same address
11%
Living with
Family
Member has not been matched
as married but evidence of living
at the address with other family members
7%
MarriedMember is married and a spouse
has been identified
36%
Shared LivingMember is found to be residing
at the address with non-family members
5%
SingleMember found but not matched
to anyone at the address
6%
UnknownMember was not found at the
given address at any point in time
16%
WidowedMember matched to a potential spouse who is deceased20%

Address Quality

Of the member records that form this sample, only 59.33% of the records were verified according to PAF without any corrections.

39.95% of the address records required some level of correction ranging from spelling errors to incomplete or incorrect data.

Definition% of Total
Address verified59.33%
Address verified but some corrections required37.64%
Address verified with some correction, looking
wider that just the specified postcode
1.49%
Address verified to Street Level, the property was not on PAF, but a unique match to the street identified on a single postcode0.53%
International Address0.50%
Address verified from a postcode which was substituted due to a Royal Mail
recoding and now matches the PAF
0.30%
No Match Found – Unable to match the record0.21%
Ambiguous Postcode Match – Matched record to Street Level but cannot determine Postcode. Multiple possibilities returned.0.01%

How many members are ‘Living as Stated’?

Most DB member data was collected a long time ago meaning many members will have moved home and not informed the scheme. A change in address will impact the efficiency of write out communication, impact the longevity calculations if the member has changed socio-demographically and increase operational cost to communicate with the member.

Category%Definition
Gone Away21%Evidence to suggest the member is no longer at the given address.
Member Deceased6%Member has been matched to the DDRI.
Potential Separation16%Of those members considered to be either ‘Married’ or ‘Living in Long Term’ Relationship’
evidence to suggest that they are now separated.

Implementation and Best Practices

As part of a risk transfer transaction, it is critical that both the scheme and insurer conduct a comprehensive data-cleansing exercise to ensure both parties are working with the most current and up to date data available.

Partied involved in a risk transfer will conduct a data cleansing exercise leading up to a transaction, which can be offered as a managed service.

Administrators or ISP’s responsible for ongoing data management or multiple bulk annuity transactions are required to implement ongoing processes to reduce risk and ensure high data quality standards.

A scheme should regularly clean member data to ensure efficient communication and compliance with regulations. When de-risking upto date member data is critical to both the scheme and insurer when calculating accurate liability calculations and valuations.

Does your organisation want to avoid sending client data to third parties?

Recent research shows that cybercrime is increasing with over 2,893 incidents reported by the ICO in a 3-month period during 2023

The most effective way to reduce the risk of member data being stolen by bad actors is to contain it within the scheme, insurer, or administrator’s environment.

As a result, MM. have designed our solutions to be delivered on premise giving full control and peace of mind to our clients. This solution negates the need to send sensitive client information to a third party while ensuring member data is up-to date and complete in line with the standards required to achieve accurate scheme valuation.

Leveraging MM’s historical data assets

In the world of pension scheme management, data accuracy and efficiency play a critical role. MM is a data owner of over 3 billion historical records, which along with government approved licenses is the foundation of all our proprietary solutions.

There are many options in the market who re-sell existing solutions, and while there can be benefits to this this MM uses new and innovative ways to trace members and spouses meaning a scheme has a better chance of tracing additional people when used instead or alongside other sources.

During recent tests MM. have proven to find between 10-15% additional member spouses which as described in MM’s previous blog: Marriage and Pensions: A Mis-Match Made in Data, can have a tangible impact on fund valuation and member communication.

The benefits of being a data owner are numerous:

  1. Data Ownership and Control: When a tracing provider owns their data, it gives the pension company more control and confidence in the accuracy and security of the data. They can have a better understanding of how the data is collected, maintained, and used.

  2. Different Coverage of the Population: Using new and untapped sources of data means that additional members or spouses can be traced. This is crucial for pension companies as they rely on complete data to make informed decisions about investments, payouts, and other financial matters.

  3. Data Security: Data security is a significant concern in the financial industry. A tracing provider that owns its data can implement stringent security measures to protect sensitive information. This helps the pension company maintain the trust of its clients and regulatory authorities.

  4. Flexibility and Customisation: With ownership of the data, the tracing provider can offer more flexibility and customisation options. They can adapt their solutions to the specific needs and requirements of the pension company, ensuring that the data and services align with the company’s goals.

  5. Compliance and Regulation: Pension companies are often subject to strict regulations and compliance requirements. Using a tracing provider who owns their data can make it easier to demonstrate compliance with data privacy and financial regulations.

  6. Reduced Risk of Data Dependency: Relying on third-party data providers may introduce the risk of data dependency, where the pension company is at the mercy of the provider’s data availability and pricing. Owning the data can reduce this risk.

  7. Long-term Reliability: Data is a critical asset for pension companies, and they need assurance that their tracing provider will be a reliable partner over the long term. A provider that owns their data may have a more stable and sustainable business model.

  8. Data Innovation: Tracing providers that own their data may have more freedom to innovate and develop new solutions, which can benefit pension companies in the long run.

  9. Cost Control: By owning the data, the tracing provider may have more control over the cost structure, potentially leading to more predictable and cost-effective pricing for the pension company.

MM. have invested in innovative ways to trace members which means a scheme has the best possible chance of tracing members. This article highlighted some of the reasons why MM. should be considered alongside existing member tracing services to ensure maximum coverage and therefor confidence in scheme data.