Market oversight: Pensions dashboards
Pensions dashboards will let members see their pensions, including the State Pension, in one place online. To make this possible, around 2,600 occupational pension schemes with over 100 active and/or deferred members, and all providers of personal and stakeholder pension schemes, must connect to a central digital architecture being built by the Money and Pensions Service (MaPS) by 31 October 2026. We regulate the compliance of occupational pension schemes, and the Financial Conduct Authority (FCA) regulates personal pension scheme providers.
Over 1,000 occupational pension schemes, covering millions of records, are already connected. But connection is only part of the story. Schemes must be able to find dashboard users in their records and provide recent, accurate information about pension values. This relies on high quality data.
Trustees and scheme managers should treat data as an important strategic asset. High quality data is not just needed for dashboards. It is fundamental to delivering good outcomes for members.
Although around 2,600 occupational pension schemes are in scope for dashboard requirements, membership is highly concentrated: just 51 schemes hold around 80% of all the records that will appear on dashboards.
In August 2025, we contacted these large schemes to understand how well prepared they were. While significant progress has been made to improve data quality for dashboards, readiness is uneven. Schemes had a range of data controls in place, though some were more sophisticated than others. Preparations for ensuring they had the right personal data to find dashboard users in their records (‘matching’) were generally more mature than for value data. For many schemes, further work is needed to embed data quality, monitoring and assurance into business-as-usual processes.
This report summarises what we found, highlights best practice, and shares lessons for the wider pensions sector. Best practice insights have also been incorporated into updated dashboards guidance.
Published: 23 April 2026
Our approach
We identified schemes with more than 100,000 active and deferred members using annual scheme return data, excluding schemes close to wind-up.
We issued a questionnaire to these schemes and followed up with meetings where needed to explore their responses. We looked at:
- how schemes manage data quality day to day
- preparations for matching data (used to find dashboards users in their records)
- preparations for value data (used to return accurate and recent pension values to members through dashboards)
Each scheme received individual feedback with specific actions to take and examples of best practice as appropriate. Relevant good practice has also been included in our updated dashboards guidance, published alongside this report.
The findings are also helping us assess the risk that members may not see a complete or accurate picture of their pensions, which will inform the government’s decision on when to launch the MoneyHelper dashboard.
Findings
Governance
These large schemes had internal teams supporting trustees and scheme managers. Most teams had a named individual responsible for data quality, and some had specialist data quality teams. Data quality was typically (but not always) reported regularly – often quarterly – to trustees.
Most schemes provided information to us promptly and gave us access to the relevant experts, including the administrator. In a small number of cases, we had to request information repeatedly, or information was delayed due to schemes’ internal sign-off processes. Where schemes do not provide us with information voluntarily, we can issue notices requiring it, but we did not need to use this power at this time.
Schemes generally engaged positively with our feedback. Several took swift action to address issues we raised, resolving these within weeks.
Data controls
All schemes held data electronically and provided us with common and scheme-specific data scores where requested in the scheme return. Most schemes, when assessing their data, considered not just whether data was present, but also whether it met certain rules (was valid) and was consistent with other data. The more advanced schemes had built the functionality to look at the age of the data (when it was last updated or verified) as a factor in assessing data quality.
Schemes should assess member data from different dimensions to ensure that it is of sufficient quality and determine if improvements are needed. Just checking that each field has data is not sufficient.
Several schemes had the functionality to report on data quality in real time, running data tests daily. Some reports had sophisticated functionality, such as the ability to filter data by member type, or to show key scheme events and changes as context when looking at trends over time. However, in some cases, while reports were available, they were not regularly reviewed or acted on.
Nearly all schemes received employer data electronically at least monthly, although a small number received defined benefit (DB) contribution and salary details annually. Schemes with multiple employers generally had controls to ensure employer data quality, typically through validation checks built into data interfaces. These varied in sophistication, from basic format checks (such as confirming National Insurance numbers (NINOs) are the correct length) to more advanced rules (such as blocking invalid letter-number combinations not permitted by HMRC).
Many schemes and administrators had dedicated employer relationship teams, which provided education to employers on data to provide to the scheme and worked with them to resolve issues. The most advanced were able to analyse their data by employer and provide employer scorecards.
Most schemes reviewed employer data after submission to identify or prevent issues. A small minority placed full responsibility on employers or assumed the data was correct.
Schemes should have processes in place to enable employers to provide timely and accurate data, electronically wherever possible. Validation checks should be built into the process – see maintaining data quality.
Temporary NINOs were commonly accepted for new members. More proactive schemes regularly reviewed these and worked with employers or members to replace them with permanent numbers.
Regular and targeted member engagement will assist you in maintaining good quality data. You should ensure that there are simple and accessible routes for your members to update their information easily. You should seek opportunities to obtain the latest member information, such as when responding to incoming queries.
Many schemes encouraged members to review their data through portals or apps. Fewer had strong controls to validate member-submitted data, though some used postcode lookups or bank verification to reduce these risks.
Where issues were identified, most schemes carried out root-cause analysis and amended their systems and processes to prevent the same issues from recurring. Some did this informally, while a smaller number had structured, ongoing data improvement plans reporting to the trustees or scheme manager.
You should put improvement plans in place to address issues. Plans should clearly set out the steps you are taking to improve your scheme data and have a clear timeline.
Tracing exercises for deferred members, especially those nearing retirement, were common, as was mortality screening for pensioner members, with some operating this weekly.
Some schemes took a highly proactive approach to improving their data, commissioning external third parties to assess and fill gaps, or capturing additional data points like email addresses and mobile numbers.
Most schemes had multiple data quality controls in place, though we identified areas for improvement for many of these. The more proactive schemes had set out their controls, and the work they are doing to maintain and improve data, in a data strategy. Some schemes reviewed or audited their controls annually, but many did not.
Trustees and scheme managers have a legal duty to ensure that schemes have governance and internal controls in place to monitor and improve data. You should ensure that your administrators have adequate controls in place to maintain good quality data. You must monitor the effectiveness of your controls.
It is good practice to formally capture your strategic approach to member data in a data management strategy. You can find out more in our guidance on maintaining data quality.
It is a requirement for governing bodies to maintain agreed, consistent, and fair policies for situations where data cannot be corrected. See the data monitoring and improvement module of the code of practice.
Overall, schemes have stepped up activity to improve data quality in recent years, particularly in preparation for dashboards. It is important trustees and scheme managers ensure data improvement, monitoring and assurance become part of ongoing business‑as‑usual activity.
Matching
Matching is the process schemes use to determine whether a dashboards user corresponds to a record they hold, and whether they can safely return information to that individual. Schemes can return a ‘full match’ (where they are confident that the user is their member) or a ‘possible match’ (where they need some more information from the user to confirm that they are a member).
You should decide what data to use to match members to their pensions based on your scheme’s data quality and availability. You must keep a record of your matching policy for at least six years from the end of the scheme year in which the decision is made.
Nearly all schemes had defined how they would identify matches. Those who had yet to decide on their matching criteria were schemes that had not yet connected and were awaiting input from their administrator or connection provider. Most schemes used the Pension Administration Standards Association’s guidance on matching to help them decide on their matching policy.
NINO, date of birth and last name were the most commonly used fields for full matches, with first name and postcode also used by some. A small number were exploring ‘fuzzy matching’ or ‘Levenshtein matching’ to deal with minor differences in records and whether they can be considered a reasonable match (for example the surname Smyth vs Smith, where the NINO and date of birth match).
If a ‘possible match’ is returned, members have 30 days to contact the scheme and provide additional information to confirm the match. Schemes planned to use the same data to verify a member’s identity that they use as part of their current processes. Once a match is confirmed, data would usually be refreshed overnight, so most members would have updated information available within 24 hours. However, if a member is still in employment, any details they provide through the possible matching process will usually be overwritten by employer data, unless the member also changes their details with the employer. Only one scheme was developing the ability to identify the most recent data, though many schemes explicitly signposted members to their employers to help avoid this issue.
Nearly all schemes had discussed their approach to possible matching with administrators or suppliers, although few had fully finalised this at the time of our engagement. Many schemes were concerned about having enough operational capacity to manage possible matches. Some are exploring web-based solutions or artificial intelligence (AI) to help resolve possible matches, while others plan to recruit additional staff. These capacity concerns are consistent with the findings from our previous report on the administration market and emphasise the need for sustainable resourcing, operational resilience and early planning between governing bodies and administrators.
You have a duty to resolve possible matches if a saver contacts you within 30 days of using the dashboard. This includes during user testing.
You should work with your administrator or connection provider to ensure that they have the right processes and resources in place to resolve possible matches.
Most schemes had a formally documented matching policy, and several schemes shared these with us. A small number were unclear or did not cover all the areas we would expect. The strongest policies clearly explained the factors considered when setting matching criteria (including data quality), how possible matches would be resolved, the reports they would receive from administrators or connection providers, and how the policy would be reviewed – including review frequency and sign-off arrangements.
You need to ensure that your data is of sufficient quality to allow you to meet your duties to find members. You may wish to take proactive steps to reduce data gaps or issues.
To ensure that you continue to effectively match over time, you must ensure that you maintain the quality of your data on an ongoing basis. You can find out more in our guidance on data quality.
Most schemes had commissioned or received bespoke assessments of the quality of their matching data. Addresses were often identified as a weakness, although few schemes planned to rely on them for matching. Many schemes were actively working to improve their data, for example by tracing deferred members, resolving temporary NINOs with employers, and collecting additional contact details such as email addresses and mobile numbers. Relatively few schemes had clear plans for maintaining the quality of matching data over time.
Some schemes with external additional voluntary contributions (AVC) providers were working to reconcile personal data held by the AVC providers and main administrators to ensure a consistent matching experience. The most proactive schemes had already completed this work and planned to repeat it annually.
It is good practice to reconcile the data held by different providers to ensure consistent outcomes for members.
You should review your matching criteria on a regular basis and refine these to reflect changes such as data quality improvements or learnings from your providers’ reports.
You should have a clear mechanism for approving changes to your matching criteria.
Most schemes confirmed they would receive reports from their administrator or connection provider showing the number of ‘full’ and ‘possible’ matches. Some had access to this information in real-time. Many schemes had also considered how they would review the effectiveness of their matching criteria, commonly planning annual reviews, with more frequent checks during user testing or shortly after dashboards go live.
Values
All schemes had started preparing value data (the information shown to members through dashboards about the value of their pensions). However, for many schemes, this work was less advanced than their preparation for matching, and few had received a specific readiness report from their administrator or connection provider.
You need to ensure your data is of sufficient quality to allow you to meet your duties. This includes both the data you need to find members in your records, and the data you need to provide them with accurate information about the value of their pensions.
Recency
Schemes must provide value data calculated within the last 12 to 13 months. If recent values are not available, schemes must provide this within three days (for defined contribution (DC) benefits) or 10 days (for all other benefits).
In determining values to return, you should consider alignment with other communications. For example if you return information which is different from that issued to the member in their annual benefit statement, you should consider how best to explain this difference to members.
DC schemes already provide annual benefit statements, as do public service schemes for active members. Some DB and hybrid schemes also issue annual statements. Almost all such schemes intended to use these existing values for dashboards, allowing them to meet their duties efficiently and provide consistent information to members. Only exceptionally were schemes planning to provide real-time information to members through dashboards, and these were considering how best to communicate the difference to members.
Where schemes did not have recent value information for their members (typically for deferred members), most planned to revalue most of these benefits ahead of dashboards going live and then on an annual basis. Typically a small number of complex cases (such as those involving pension sharing orders) would still need to be calculated on demand. The more prepared schemes had already discussed and agreed the processes for calculating these with their administrators and sought assurance that the statutory timescales could be met.
You should work with your administrator to ensure that the right processes and resource are in place to meet the deadlines to return value information. It may be more efficient to put in place a process to revalue pensions annually where these are not already available.
As good practice you may want to identify how you will manage more complex scenarios such as members with partial or late retirement, hybrid benefits or AVC benefits managed by a different provider.
Accuracy
Accurate values depend on accurate inputs and robust calculation processes.
For DC schemes, we explored controls around investments and contributions. As part of the authorisation process, master trusts must operate robust systems and processes to ensure transactions are processed accurately and on time. This includes identifying missing contributions and reconciling bank accounts, contributions and investment units. They must report any significant events that affect their ability to meet authorisation requirements, which includes failures of these controls. They also commission annual assurance reports, which they share with us. We found no significant concerns with these arrangements.
You should understand the procedures and financial controls the administrator operates to ensure that financial transactions are processed promptly and accurately. You should annually review processes and systems for financial transactions, and identify opportunities to improve them. See the financial transactions module of the code of practice.
Many master trusts use automation to reduce error risk, including automatic contribution reminders, automated reporting of contribution or investment allocation errors, and automated benefit calculations and retirement processing.
Among DB and hybrid schemes, some had completed benefit audits, often as part of a derisking exercise, which increased confidence in benefit accuracy.
You must ensure that your scheme member data is of sufficient quality to enable you to provide your matched members with accurate, recent value data within the timescales set out in legislation. You should test the accuracy of the data items you need to calculate accurate values. Our scheme member data quality guidance provides further information on the actions you can take to conduct a robust assessment of your scheme member data quality.
You must ensure that calculations are undertaken and recorded correctly. You should assure yourself that appropriate controls are built into your administrator’s calculation processes. This may include testing the accuracy of a sample of calculations, building in additional checks for complex cases or even commissioning third parties to do end-to-end testing of your administrator’s processes.
‘Value unavailable’ codes
In some cases, schemes may not be able to provide value data to a member immediately and must instead return a code from a list specified by MaPS. Practice varied widely. Several schemes were unsure which codes they might need to use, while the most prepared had identified the relevant codes, understood how many members might be affected, and considered the operational steps required, including administrator training.
You should work with your administrator or connection provider to identify where you may need to use ‘unavailable’ codes. You should keep a clear record of your rationale for their use and the number of members impacted.
The use of the codes does not remove your duty to return information, and you will need to be able to demonstrate that you have an appropriate process in place to provide your members with the missing information in a timely manner.
MaPS is currently user testing the MoneyHelper dashboard. Connected schemes will need to calculate values on demand and be able to respond to member queries during this time, though volumes will be low. Schemes should use this period to review the effectiveness of their processes, investigate and address any issues identified, and adapt in light of user testing.
Next steps
You can find out more on preparing for dashboards in our updated guidance. We also provide checklists, which you can use to track the progress of your preparations, both before and after connecting.
These preparations should be seen not as a compliance exercise but as part of a wider journey of digital and data-driven transformation. High-quality, well-governed data underpins accurate administration, effective automation, cyber resilience and ultimately trust in the pensions system. Managing data quality is therefore not a one-off activity. Trustees must embed robust controls, regular assessment and clear accountability into their business‑as‑usual governance arrangements, working closely with administrators as strategic partners. You can find out more about maintaining good quality member data on an ongoing basis in our scheme member data guidance.
We will continue to place data and administration at the centre of our regulatory focus as pensions dashboards move from preparation into operation. We will shortly launch the next phase of our work, targeting the value data preparations of DB and hybrid schemes. We will gather further insights on how dashboards are working in practice from MaPS and industry, and use these insights to inform the discussions on the timing of launch of the MoneyHelper dashboard.
Ultimately, pensions dashboards are one part of our broader mission to raise standards across administration, strengthen collaboration between trustees and administrators, and build a more trusted, resilient pensions system that works for members not just today, but for the future.