Heywood
The consequences of inaccurate data can be counted in increased costs and risks, to the scheme, but also to its members. Heywood chief strategy officer Chris Connelly says the business case for creating an ongoing data cleansing strategy can be calculated on savings made by avoiding unnecessary costs. “We help our clients to consider what the downside of bad data could be, in an effort to place a value on what good data means,” he says.
Improving the quality of member data should be seen not as an additional expense, but as an opportunity to save money. Take, for example, additional costs incurred when a scheme sends paper communications to the wrong address. Using an estimate of £4 for the cost of each letter posted, a scheme with 20,000 members and an average of 8.97% of incorrect addresses, every mailout will include 1,794 incorrectly addressed letters and waste £7,176. This level of paper wastage will also have a negative impact on a scheme’s environmental social and governance (ESG) record.
Heywood Pension Pulse data shows schemes are wasting on average £7,176 per mailing, per 20,000 members
As noted above, more resources will be wasted once the dashboards are operational, if data errors mean members can’t find a pension they think they have, or are presented with a “possible match”.
“The common theme with every possible match scenario is huge amounts of time being taken up, wasting lots of money,” says David Rich.
There is also the fact that if people engage with dashboards and have a poor experience, they may not use the system again: a terrible outcome for the pensions industry, and society in general.
The costs of inaccurate data could be particularly high for schemes seeking to consolidate or to complete buy-in or buy-out BPA transactions, because insurers base premiums on analysis of a scheme’s liabilities – or on their best estimate if truly accurate information is unavailable. They may also add on additional cost to reflect any perceived risk in inaccurate data.
“If insurers’ are unsure what the risk really is, they won’t underprice – they’ll go the other way,” says Rich. He suggests that in some cases an inaccurate portrayal of scheme liabilities could add millions to the BPA premium paid by even a small pension scheme.
Finally, there is the question of regulatory breaches. Rich notes that while The Pensions Regulator has been generally reluctant to issue fines in the past, public statements made in relation to the launch of the dashboards suggest this could change.
Rich says schemes should also consider risks associated with breaching other regulations such as GDPR, which could expose a scheme, provider or sponsoring employer to significant fines and reputational damage.
“Even in the best-case scenario, poor data quality means higher costs and wasted time,” says Heywood chief operating officer Louise Donohue. “It can mean the scheme makes overpayments. And the more time that is spent on correcting mistakes, the bigger work backlogs get and the worse administrative efficiency and member experiences become.
“Beyond that, there are regulatory risks and reputational risks, for the scheme and for the sponsoring employer. Ultimately there is a risk to the future stability of the scheme, because long-term funding and investment decisions are being made based on what is sometimes poor data.
“A good data cleansing strategy delivers multiple benefits,” Donohue concludes. “It reduces long term cost, reduces risks and improves the member experience.”
