High data quality is essential for pension funds to ensure an honest and controlled business operation. It enables pension funds to provide participants with their rightful pension payments based on accurately determined premiums. In recent years, significant attention has been given to data quality in the pension sector. With the implementation of the Future Pensions Act (WTP), even more emphasis is placed on the quality of data in pension administration. Pension funds must meet the prescribed standards for data quality before the transition to the new pension system can take place.
Pension funds are currently busy preparing for the introduction of the new pension contract. This is in addition to their regular activities, requiring them to keep the shop open while renovating. It is clear that the period until 2027 will be a very busy one in the pension landscape.
Pension funds are currently busy preparing for the introduction of the new pension contract. This is in addition to their regular activities, requiring them to keep the shop open while renovating.
One of the challenges that pension funds face is demonstrating data quality. According to the Future Pensions Decree, pension funds must be able to demonstrate that data quality is ensured before the transition to the new pension system can take place. This is crucial for making balanced decisions about the transition and accurately calculating individual pension assets. The Data Quality Framework – Future Pensions Act and the Data Quality Good Practice support pension funds in this challenge. But how can pension funds apply these publications?
Although pension funds have outsourced the majority of their activities, such as pension and asset management, to execution organisations, they remain ultimately responsible for maintaining an integral and controlled business operation according to the Pension Act. The quality of data forms the foundation for this. It is the responsibility of pension funds to gain insight and form a judgment about the quality of the data, despite the outsourcing. Therefore, pension funds cannot solely rely on assurance statements from outsourcing relationships, such as the ISAE 3402 Type 2 and ISAE 3000 statements.
Since the Quinto-P investigations in 2012, pension funds have already dedicated significant attention to data quality. Data quality presents a significant challenge, particularly because pension funds have been receiving and processing data for decades and often utilise legacy systems. Given that pension funds have been processing data for decades, there is a possibility that errors have occurred in the pension administration.
As a result, data has become a prominent topic on the governance agenda, prompting many pension funds to conduct data quality assessments in order to gain insight into potential discrepancies in their administration. The outcomes of these assessments provide a clear picture of the data quality of the pension fund and should be considered when following the Data Quality Framework and Good Practice guidelines. Consequently, the results of these assessments serve as the initial step to complying with the guidelines and demonstrating control over data quality.
The publication of the Data Quality Framework does not signify that pension funds need to start from scratch. The activities already undertaken serve as the foundation on which the remainder of the Framework can be applied. The goal for each pension fund is to gain timely insight into Data Quality and implement potential corrections in the administration before transitioning to the new system. The publications by the Pension Federation and DNB offer tools to achieve this insight.
We are here to assist in translating the complex guidelines surrounding data quality into a pragmatic and concrete approach for your pension fund
Data quality is a crucial element for pension funds, especially in the lead-up to the new pension system. With the transition to the new pension system and the publication of the Data Quality Framework and Good Practice guidelines, data quality has become even more important. This insight is necessary to navigate through the 4 mentioned phases and to carry out any necessary corrections and remedial actions. The deadlines are clear: all activities, including improving data quality, must be conducted at least 9 months prior to the transition to the new pension system. All of this is to ensure that the pension fund can make informed decisions about the transition.
With the transition to the new pension system and the publication of the Data Quality Framework and Good Practice guidelines, data quality has become even more important.
We assist various pension funds with the implementation of the Data Quality Framework and support a large number of clients with the challenges related to data. Projective Group boasts over 100 data experts who eagerly delve into topics such as data quality, data governance, master data management, and data architecture.
With over 25 years of experience in the pension sector, we provide substantial expertise in data and project management to numerous clients. We are eager to engage in discussions to exchange thoughts about data quality.
Established in 2006, Projective Group is a leading Financial Services change specialist. With deep expertise across practices in Data, Payments, Transformation and Risk & Compliance.
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