UK Authority Finds Pensions’ Tech Upgrades Are Floundering

A new research report by the UK-based Pension Administration Standards Association (PASA) concluded that technological aspirations for pension schemes looking to automate their administrative tasks are being inhibited by poor data quality.

“The research conducted by PASA showed a confusing picture,” the report began, partly either perpetuated by a lack of understanding by pensions on the significance that data has on their plans for further administrative automation. While there was a clear appetite among pensions surveyed for new technologies, the ambitions were mitigated by the 90% of respondents who did not consider their data quality to be excellent.

The pensions are incentivized in large part to help further automate their self-service calculations, benefit statements, pay slips, and other transactions to reduce administrative costs and to improve their members’ experience and engagement statistics.

“There’s a clear willingness on the part of many pensions to improve the quality of their data…however, willingness is not enough. It takes time for schemes to integrate the technology some have on their wish lists.”

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The problem, the PASA found, is that data cleanse projects aimed at improving the quality and integrity of a pension’s data set, are often regarded as high cost/low value endeavors. The report said they believe managing data on an ongoing basis provides a lot of glossed-over value, enabling the implementation of advanced technology and greatly improve pension governance.

“Despite obvious gaps in knowledge and, by the schemes’ own admission, data, there’s an interesting anomaly apparent in the research. There’s a high demand for technology, but lower interest in high quality data,” the report said.

The report showed that while only a third of defined contribution pension schemes consider their data to live up to an “acceptable” standard, many noted that their data is poor and they will not address the matter soon. They said it’s not exactly high on their priorities list, and they lack enough capital to carry out their tasks.

The impact of poor-quality data are varied, and include reputational risk, incorrect member benefit calculations, delays to bulk activities such as valuation exercises, and real-time errors being reported when a user attempts to edit an existing record.