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Lost in the Inbox: Missing member emails in Pension Scheme Data

In a report prepared for The Pension Regulator by OMB research to gain a greater understanding into pension scheme administration and the challenges faced by Administrators.
The data section of the survey focused on large administrators (100,000+ memberships) and threw up some interesting results.

Whilst the vast majority of these administrators were confident in the accuracy of the data they hold, there were significant issues around historical gaps and lack of email and phone numbers.

These gaps can have a significant impact on a scheme’s ability to contact their members and the figures from the survey are concerning.

All large administrators were asked if they were ‘Confident in accuracy of data for at least 75% of memberships’ for both active and deferred members.

Only 56% of schemes were confident in the accuracy of their active members email addresses, this figure dropping alarmingly to 11% for deferred members.

Mobile phone numbers represent another significant challenge to administrators with only 38% of them confident in the accuracy of >75% of their Active Members. Again the figure is, expectedly, far lower for deferred members at 13%.

These figures represent a fundamental challenge in an administrator’s ability to contact a scheme’s members outside of costly mailouts.

The report also highlights issues with regards to pension administrators’ ‘Dashboard Readiness’ ahead of the Pensions Dashboards Program’s rollout. A quarter surveyed expressing that ‘availability of data, accuracy of data and inability to fill historical data gaps’ being just some of challenges the success of the program will have. Accurate and complete member email addresses will be critical when matching members with multiple pots and reducing the number of partial matches.

MM, are a government-licensed data services company, supplying the pension & insurance industry. Our innovative solutions address this critical gap in member information with a new source of over 50m opt-in email addresses.

Our Email Append Service is designed to solve the very problem outlined in this report by OMB Research. This service seamlessly integrates email addresses into existing data sets ensuring administrators can comply with regulations and reduce operational costs.

OMB Research. (2022). Survey of pension scheme administrators 2020 to 2021 [Online]. Prepared for The Pension Regulator.

Mortality Rates and Deprivation (IMD) in England 

MM are a government approved DDRI licence holder and proprietary data owner, receiving all registered UK deaths on a weekly basis. The month of death, age at time of death and inferred gender can be aggregated by postcode or output area which are more granular than what are made available by the office of National Statistics (ONS). This means that MM can provide actuaries with more flexible, detailed and timely mortality statistics, with the ability to append different variables representing population experience and help improve longevity models

MM has delved into the intricate relationship between average age at death and the Index of Multiple Deprivation (IMD) in England. The IMD, a comprehensive measure of relative deprivation, assesses 39 indicators across seven distinct domains, providing a nuanced understanding of socio-economic conditions at the Lower-layer Super Output Area (LSOA) level.  

Our study seeks to unravel the impact of deprivation on life expectancy as a surrogate for experience data internally available to actuaries when building longevity models.   
 
Deprivation in England is gauged at the LSOA level, the second smallest geographical area in the UK (Output area being the smallest). Each LSOA, housing between 400 and 1,200 households and a resident population of 1,000 to 3,000 individuals, receives a score based on a combination of indicators. These scores are then ranked and divided into deciles, ranging from 1 (most deprived) to 10 (least deprived). 

Our research reveals a striking correlation between deprivation and life expectancy, underscoring the profound impact of socio-economic conditions on the longevity of individuals. The table below summarizes mortality rates for men, women, and overall populations across the ten deciles: 

This table demonstrates the overall deprivation decile, however MM can be segmented by other available deciles including Income, Employment, health, skills, housing environment and more. The same applies to other available datasets such as Urbanicity code, Council Tax, Health or Income data or third party datasets such as Experian Mosaic.

The data demonstrates that the overall age at death in the 1st decile (most deprived) is 74, meaning 7 years less than their counterparts in the top decile. The disparity is even more pronounced for men, with an 8-year difference (71-79) between the 1st and 10th deciles, compared to a 6-year range (77-83) for women. 

In conclusion, our research underscores the profound impact of deprivation on age at death, which shows the importance of experience data, assessed with appropriate estimates of the prevalence of this experience in the general population outside the schemes member data. 

The disparity in longevity based on the level of deprivation in the geographical area, is a stark reminder of the complex interplay between socio-economic factors and health outcomes. In future additions to our research, we will delve into the seven distinct domains used to calculate the IMD scores, providing a greater understanding of the factors contributing to these disparities.  

MM provides month of death, age and gender aggregated at a geographical level and is easy to implement into existing mortality models. When used alongside scheme experience data this will increase the accuracy and reliability of longevity models and pricing calculations. 

As is well understood longevity models require both infomation about deaths and also the exposed populations to properly estimate life expectancy. MM. also provide these alongside the deceased estimates.

Stay tuned as we unravel the intricate web of deprivation and its implications on health and well-being. 

The Pensions Regulator publishes their General Code of Practice

In a significant development for pension scheme governance, The Pensions Regulator (TPR) has recently unveiled its long-awaited 2024 General Code of Practice. Published on January 10th and due to come into force in March, this comprehensive code consolidates ten of the regulator’s existing codes, providing updates introducing new requirements to improve pension scheme governance.

One key aspect that demands attention is the Administration: Information Handling aspect, especially in terms of Record Keeping.

The new code places a heightened emphasis on the need for robust record-keeping practices within pension schemes. Effective record keeping is crucial for ensuring compliance with regulatory requirements, facilitating transparency, and supporting the overall governance of pension schemes.

This article explores how MM, a leading service provider in the financial and administrative sector, can assist pension schemes in meeting the stringent requirements outlined in the code.

1. Maintaining Robust Scheme Records and Data Monitoring:

Governing bodies are now required to have a comprehensive set of measures in place to maintain scheme records. This includes robust data monitoring and improvement processes. MM offers a suite of solutions designed to streamline these efforts and ensure compliance with TPR’s standards.

2. Demonstrating Processes for Accurate and Up-to-Date Records:

MM helps schemes demonstrate that they operate processes ensuring accurate and up-to-date records. Through innovative technologies and tailored solutions, MM ensures that pension schemes have the capabilities to run seamlessly and efficiently.

3. Error Identification and Rectification:

Prompt identification and rectification of errors in scheme records are crucial for compliance. MM facilitates efficient error resolution, ensuring that any discrepancies are identified and corrected as soon as possible.

4. Common Data for Administrators:

MM recognise the importance of common data for administrators and provides solutions, such as MM Spouse and Beneficiary, to ensure that beneficiaries are accurately accounted for. This feature is pivotal in maintaining accurate and comprehensive member information.

5. Collaboration with Administrators for Scheme-Specific Data:

Working collaboratively with administrators is a key aspect highlighted in the code. MM facilitates this collaboration by providing tools like MM Existence, which aids in identifying, recording, validating, and correcting scheme-specific data. This ensures that data accuracy is prioritized and maintained.

7. Complete and Accurate Record Maintenance:

MM’s solutions align with TPR’s requirements for maintaining complete and accurate records, encompassing both common and scheme-specific data. This not only meets regulatory standards but also provides a foundation for efficient scheme administration.

8. Processes for Monitoring and Reviewing Scheme Data:

MM supports governing bodies with processes for ongoing monitoring and periodic reviews of scheme data. This includes data improvement prioritization for members approaching the benefit-drawing stage, as well as scheduled tracing and existence exercises to validate member data.

9. Comprehensive Solutions from MM:

To address the requirements outlined in the code, MM offers a suite of solutions, including MM Spouse and Beneficiary, MM Existence, and MM Residence. These solutions collectively ensure accurate member data, validate information, and maintain correct contact details.

As pension schemes navigate the complexities of the new General Code of Practice, MM stands as a reliable partner in ensuring compliance with the Record Keeping and Data Monitoring standards set by The Pensions Regulator.

By leveraging MM’s innovative solutions, pension schemes can not only meet regulatory requirements but also enhance their overall efficiency in managing member data and scheme records.

Contact us to discover how MM can empower your pension scheme with cutting-edge solutions tailored to meet these evolving regulatory challenges.

MM Spouse: Marital Status Predictor & Member Spouse Tracing 

If the scheme is unaware of their member’s marital status and associated Spouse in, this can lead to the risk of unexpected and potentially unknown liabilities, particularly for DB schemes.  

MM Spouse is a proprietary solution built on a foundation of billions of historic records and data different data sources from other options in the market. This approach is resulting in consistent uplift in traced spouses compared to LexisNexis De-Risking Solutions, particularly when the member is gone-away from the address on file. 

In order to replicate current pricing models, MM have maintained similar categories to traditional market options. 

Married 
Living with Family 
Single 
Living in Long-Term Relationship 
Widowed 
Unknown 
Shared Living 

As well as proven uplift in matches spouses MM also offer additional data which is valuable to schemes and insurers as a standard with our marital status predictor. 

Gone-away indicator  Locate and update contact information for ‘gone away’ individuals are essential components of responsible scheme management. This is particularly important for a write-out campaign during a risk transfer. 
Separation Indicator Help protect the rights of all parties involved. Keeping track of divorce or legal separation ensures compliance and supports the individuals.  
Deceased flag Maintaining up-to-date records on the deceased status of plan members is essential for effective and responsible administration. MM are a DDRI license holder. 

Along with billions of historical records of UK residents, MM Spouse leverages actual marriage data, increasing confidence and improving results for our clients. 

Our Secure File Transfer (sFTP) product ensures that all files are securely transferred and returned to our clients within 24hours in accordance with MM’s accredited ISO 27001 information security management system.

During recent data tests, MM. Spouse was found to deliver, in comparison to other providers:

  • 8% uplift in matched spouses  
  • 10% reduction unmatched members
  • Over 40% increase in members matched as living in ‘Living in long-term relationship’

The risk of increased liabilities can be a major concern. That’s why our service is specifically designed to identify spouse and cohabitee information, which can be useful in identifying possible beneficiaries. Providing increased certainty over your liabilities. 

Contact MM. today to see how we can help your scheme unlock the undeclared marital status of your members.

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.