AI is everywhere in pensions commentary, but in pension risk transfer it still raises a fair question. Where does it genuinely add value today, and where is it still theoretical?
In this article, Heywood’s Director of Pension Risk Transfer, Kelvin Wilson, looks at where AI and machine learning are already improving outcomes across real PRT transactions, and where the limits still are.
In the context of pension risk transfer, artificial intelligence is not about replacing actuarial judgement or trustee decision-making. Its real value lies in reducing friction across data-heavy, time-sensitive stages of a transaction. Machine learning, in particular, allows large volumes of historic and operational data to be processed, validated, and translated at a speed and consistency that manual approaches struggle to match.
Across the PRT value chain, delays and costs tend to accumulate at points where data is incomplete, inconsistent, or difficult to interpret. Four areas stand out where AI is already beginning to make a practical difference: assembling liability information at scheme level, broking and pricing support for advisers, transaction implementation for trustees, and risk settlement for insurers.
Accurate assembly of member and benefit data underpins every pension risk transfer transaction. Before a scheme can engage the market with confidence, trustees, administrators, and advisers must bring together multiple data sources, often spanning decades, and ensure they are complete, consistent, and fit for insurer scrutiny.
This is where AI is starting to change the pace of preparation. Machine learning models can support automated data validation, identify anomalies across large datasets, and accelerate the translation of historic benefit records into structured benefit specifications. AI-enabled tracing techniques can also improve the speed and success rate of locating deferred and pensioner members, reducing gaps that would otherwise surface later in the transaction process.
The practical impact is not just operational efficiency. Schemes that can assemble and evidence their liabilities earlier are better positioned to engage advisers and insurers sooner, respond more quickly to data queries, and reduce the risk of delays once pricing discussions begin. AI does not remove the need for human oversight, but it does remove much of the manual effort that traditionally slows schemes on the path to being genuinely transaction ready.
Broking and actuarial modelling sit at the commercial heart of pension risk transfer. Whether a scheme is considering a longevity swap or a bulk purchase annuity, the decision to transfer risk hinges on a clear assessment of cost versus risk reduction, informed by timing, market conditions, and scheme-specific objectives.
AI is beginning to add value by accelerating the analytical work that underpins these decisions. Machine learning can support faster recalculation of pricing scenarios, assess the impact of changing market conditions in near real time, and assist with the structured drafting of transaction documentation. This enables advisers to respond more quickly to market opportunities without compromising analytical rigour.
One area where this is already having a practical impact is in streamlined BPA transactions for smaller pension schemes, typically those with assets below £150m. These schemes have historically faced proportionately higher costs and longer timelines due to data complexity and limited standardisation. AI-enabled data translation and mapping now allow information from multiple schemes to be converted efficiently into insurer-specific templates, reducing manual effort and supporting more consistent pricing engagement.
As BPAs become a more common route to de-risking, particularly for smaller and mid-sized schemes, the ability to prepare, structure, and translate data efficiently is increasingly critical. Heywood supports bulk purchase annuity transactions by helping schemes and advisers reduce manual effort, improve data consistency, and move through pricing and implementation with greater confidence. More detail on our approach to supporting bulk purchase annuities is available on our Bulk Purchase Annuities page.
The result is not just faster execution. By lowering operational barriers and improving data consistency, AI is helping broaden access to the BPA market, allowing advisers and insurers to engage with a wider range of schemes on a more efficient and commercially viable basis.
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Completing a PRT transaction is an important milestone, but a BPA buy-in or longevity swap is rarely the end of the journey. In most cases, it marks the transition into a complex implementation phase that determines whether liabilities are ultimately settled, insured, or managed through run-on to self-sufficiency. This stage triggers extensive data analysis, recalculation, reconciliation, and exchange of information across multiple parties.
Application of technology to necessary areas of insurance payment reconciliation, guaranteed minimum pension (GMP) equalisation/rectification, data migrations and benefit translations are now a given. Where AI is adding incremental value is in reducing the operational drag that often follows a transaction. Machine learning can support automation at scale, standardise outputs across differing datasets, and tailor processes to scheme-specific requirements without introducing additional manual effort.
The practical benefit is greater consistency and fewer delays at a point where pressure on administrators, trustees, and insurers is typically highest. By reducing rework and exception handling, AI-supported processes help contain costs and support a more controlled transition from transaction execution to long-term liability management.
Whether a scheme is running on, settling liabilities through insurance, or transferring to a commercial superfund, effective management of assets and liabilities remains critical at the endgame destination. At the same time, pension member and policyholder engagement is increasingly recognised as a core component of good risk management, not an afterthought.
AI can support this by enhancing both modelling and communication. Machine learning techniques are able to supplement conventional actuarial asset liability management models by identifying complex, non-linear patterns in data and adapting more dynamically to changing market conditions. This reduces the need for repeated manual recalibration and allows stakeholders to focus on interpreting outcomes rather than maintaining models.
On the member side, AI is already being used to improve how information is delivered and understood. Personalised videos and targeted email communications can be aligned to an individual’s circumstances, helping explain outcomes, next steps, and entitlements more clearly. This improves understanding, reduces inbound queries, and supports smoother liability settlement processes.
Taken together, AI that complements existing pension technology represents a significant opportunity for the UK and global PRT market. The gains are not limited to efficiency. Better data handling, clearer communication, and more responsive modelling all contribute to stronger governance outcomes and improved experiences for members and policyholders alike.
At Heywood, we understand that the impact of AI in the pension risk transfer market varies depending on your role in the process. Whether you're a trustee managing governance, an adviser coordinating transactions, an insurer assessing liabilities, or a scheme sponsor preparing for buy-out, our solutions are tailored to meet your needs.
This article was last updated in December 2025.