Guest comment: Data considerations for GMP equalisation

Since the Lloyds judgement on GMP equalisation (GMPE) in 2018, the subject of data quality is still consistently cited as one of trustees’ top priorities for 2022. So, how have things moved on during the last few years and where does the industry sit in terms of thinking around data quality for GMPE?

In July 2020 the PASA GMPE working group issued data guidance, which included a Data Requirements Schedule detailing the specific data items typically needed for a GMPE project and identifying whether they’re essential or desirable to the project. Although information on this subject is available, the question is, to what extent have trustees engaged with reviewing and improving the data needed for GMPE? And what action needs to be taken now as we also consider the implications for data quality of the introduction on pensions dashboards?

We now know GMPE may require the creation of new database fields and can ultimately lead to adjustments to members’ benefits. This article explores some of the data issues to consider along your GMPE journey in the light of experience since the original working group’s data guidance was published.

Despite the inherent complexity of GMPE, there’s scope – in fact often a need – for pragmatism, in relation to scheme and member data. Scheme data needs to be good, but perhaps trying to achieve perfection can lead to costs outweighing the benefits (or indeed what’s possible). Particularly where some of the data items are needed solely for GMPE and won’t benefit the ongoing running of the scheme.

Many trustees having now embarked on their journey and the industry has experience to understand the critical data items needed to carry out equalisation and those which, if not available, can be solved through alternative approaches. Some of the data needs are driven by the calculation methodology chosen and scheme structure. Knowing the critical data items means cleanse activity can be assessed properly with informed decisions taken as to where to focus resources and costs in seeking to rectify data gaps and inaccuracies.

Data analysis and planning for GMPE

Below are some key considerations which trustees should include in data analysis and planning for GMPE.

· The combined knowledge of your advisers, together with your existing approach to record keeping, will be key to understanding what scheme and member data you have available. They will form the foundations of your GMPE data strategy. This should help trustees to identify any gaps or known issues in the quality, completeness, storage and use of data

· A data audit is a key first step in identifying data readiness for GMPE. The audit should be targeted towards database fields required for GMPE which will be different from traditional Common and Scheme Specific data testing. Often, administration systems don’t hold all key data items in just one place so multiple sources should be included where necessary. This may also reduce later cleanse work for other purposes. While trustees are looking at data for GMPE, it’s a perfect time to consider data quality more widely. High quality data is essential for running a scheme properly on a day-to-day basis and also for other projects, such as risk transfer and member options, as well as industry initiatives like pensions dashboards. This could be more cost-effective than having to revisit manual records more than once. Trustees should fully understand their scheme’s data and what needs to be done to improve it, and TPR recommends a specific ‘data improvement plan’

· By understanding the gaps and the criticality of any data issues, trustees will have far more certainty about how much time and effort any cleanse work will take and where they may need to make decisions

· Understanding complex individual cases and how GMPE calculation design will help to shape any cleanse work required and trustees advisers can help with this. It’s acknowledged across the industry that resources have come under increasing pressure over the last 12 months to factor in cleanse work alongside other regular (BAU) and project tasks. So understanding data needs and calculation design upfront is crucial. It’s imperative BAU work is not affected, and most administrators now have project teams who work with the BAU team but do most of the heavy lifting

· Ensuring a robust audit trail is created as trustees proceed through the GMPE journey is essential for delivering a successful GMPE project. Data analysis results should be recorded, and subsequent decisions taken should be clearly set out

Benefit construction (creating benefit tranches and opposite sex equivalents)

One of the key areas which needs to be addressed early in any GMPE project is the approach to be taken to benefit construction, where more detailed benefit information and tranching is required to undertake GMPE. Experience in the industry over the past few years has shown:

· Pension scheme records rarely include all the benefit tranches to support the calculations phase of GMPE

· A number of approaches can be adopted where benefit tranches need to be constructed. For example, when considering GMP opposite sex values, this could include applying a pro-rata, first principles, using validated Contracted Out Earnings (COEs) or NISPI Dual Calculation Service approach. The choice of which works for you will depend heavily on what data trustees have available and its quality/reliability

· One size doesn’t fit all members and schemes, and a flexible approach may be needed which may vary for different groups of members within a scheme. This may be influenced by a number of factors including data quality

Ultimately, there’s scope for flexibility and tactical, proportionate approaches which may reduce the data burden and it’s important not to let data be a barrier to GMPE. However, data is critical to a successful GMPE project and all reasonable steps should be undertaken to review and improve data before diving into the benefit calculations. Discussions and decisions (in principle at least) don’t always need to be paused while data projects are progressing. But it’s important to remember GMPE projects should be overlaid with quality governance and a robust audit trail of decisions and active engagement. Collaboration between pension scheme trustees, managers, administrators and other advisers is critical.

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