Buck has announced plans to develop an artificial intelligence (AI)-powered data management tool, after research revealed that data mining typically accounts for over 40 per cent of the data cleanse effort and currently consumes around half of a project timeline.

The new tool, which is expected to launch fully for clients next year, will aim to help pension schemes with their data cleansing requirements, ensuring the accuracy and integrity of member records.

Announcing the plans, Buck explained that whilst most data cleansing projects involve manual data mining, the new tool will look to leverage AI to evaluate vast volumes of unstructured data across multiple source types.

This is expected to enable schemes to implement automated, streamlined processes, reducing project timelines and costs.

Buck suggested that this is particularly valuable given the "extremely competitive" state pf the current buyout market, with its analysis suggesting that well-managed data cleanse vastly improves the attractiveness of a scheme to insurers and can lead to a saving of up to 4 per cent on buyout premiums.

More broadly, the tool is expected to help support schemes with their overarching data journey plans, particularly in terms of GMP equalisation, scheme digitalisation, and preparation for pensions dashboards.

Buck head of pensions administration, Lee Cook, stated: “We are confident that AI has the potential to reshape the U.K. pensions industry and deliver valuable benefits to our clients.

"The ability to rapidly and comprehensively analyse large volumes of data is a standout benefit of AI, and we’re committed to investing in privacy and security at every step as we explore this capability.

"By developing this support for our dedicated pension project teams, we are helping them to reach their ambitions in a way that prioritises cost and resource efficiency.

“Even if a buy-in or buyout goal is some way off, schemes can still greatly benefit from being prepared by having accurate and streamlined data processes in place.

"Prioritising data cleansing now can prove a huge relief when schemes eventually enter any insurance transaction process.”

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