Insurers may refuse to quote and provide pricing for buy-ins and buy-outs where the DB pension schemes’ data is of a poor quality, warns Hymans Robertson, as it releases its latest paper. The firm recommends a five-stage approach – including data audit, cleansing and ongoing management – that schemes should follow to ensure their data is up-to-date and fit for purpose.

Having a high-quality data standard in place as early as possible can benefit administrative processes, reduce risk and enhance member experience, as well as ensure alignment with compliance frameworks from bodies such as PASA. It’s also key for the upcoming Pensions Dashboards, which will impose new levels of expectations on scheme data quality and currency. All DB schemes will have their own data journey to review but this remains a key element of the wider excellence in endgame process (LINK to site).

Commenting on the benefits of good data for members and administrators in the risk transfer process, as well as for insurers, Scott Finnie, Head of Digital Strategy, Hymans Robertson says:

“Every DB pension scheme will have its own journey but taking a holistic approach to data improvement and working towards an ‘accurate all the time’ data set will benefit the scheme’s progression towards their chosen endgame. This will allow for a smoother transition, and increased flexibility as all end game options can be explored with the knowledge that the data is accurate and correct. Our five-stage approach, as outlined in our paper is key to getting data into this position, and provides insights to ensure that data monitoring is seen to be an ongoing task for DB schemes as they continue their journey to endgame – which, for most, will include compliance with the Pensions Dashboards requirements along the way.”

The leading pensions and financial services consultancy’s five-stage holistic approach is key to data improvement starting with a clear definition of the link between data and benefits. Auditing the data at a regular interval, stage two, with a data improvement plan as stage three ensures that executing the plan (stage 4) will be straightforward. It's important that regular updates are provided to stakeholders and progress is maintained at this fourth stage, ensuring that the data project momentum continues. The final stage is to maintain ongoing monitoring – seeing data as a key integral part of a scheme – rather than a one-off exercise.

Commenting on the importance of data for schemes, Scott continues:

“Ensuring your data is good quality might sound simple but we know that the reality isn’t always as so. Having correct data benefits DB schemes in several important ways. It cuts administrative processes, reduces risk and enhances the member experience – all of which are invaluable to the smoothness of a scheme’s endgame journey. For those on a buy-out journey however, data quality is an increasingly pivotal consideration for insurers when considering whether to quote for a transaction, and the pricing they offer. Investing in a considered, holistic data improvement plan will make the scheme more attractive to market – as well as offering reduced risk, Dashboards compliance, and improved member experience along the way.”