In this edition of Five Minutes with MBE, we speak to Chris Lennon, Actuarial Manager at MBE Consulting, about the Prudential Regulation Authority’s proposed changes to Funded Reinsurance, what they mean for insurers, and why regulatory change doesn’t always require organisations to rebuild their actuarial processes from scratch.

The Funded Reinsurance Reset

For those who haven’t read the consultation yet, what is the PRA actually proposing and why is it important?

The PRA is concerned that the treatment of FundedRe under Solvency UK does not adequately reflect the risks these arrangements pose to insurers/cedants. In addition, it believes the treatment is inconsistent with that of economically-similar assets.

As a result, a new approach has been proposed to determine the Counterparty Default Adjustment (CDA) on FundedRe transactions. As well as reducing the value of the reinsurance asset on insurers’ Solvency UK balance sheets, the capital held for these transactions is expected to increase to an average of 10% of the underlying liabilities.

The new CDA will be calculated similarly to Fundamental Spreads on corporate bonds – this will be based on the duration of the reinsurance cashflows, the financial strength of the reinsurer, and the characteristics of the FundedRe collateral arrangement.

These concerns and proposals are of particular importance, as the use of FundedRe in the UK continues to grow in line with the Bulk Purchase Annuities (BPA) market, which currently transacts £40-50bn annually. Of this, around 15% is ceded via FundedRe.

Beyond Capital Requirements

Most of the discussion has focused on higher capital requirements. Why do you believe organisations should also be thinking about the operational implications?

If introduced, these proposals will only apply to FundedRe deals completing after 30th September 2026. As such, operational impacts need to be considered if an insurer intends to continue to, or wishes to assess the viability of continuing to, engage in new FundedRe transactions beyond this point.

The current Solvency UK approach to FundedRe is much more principles-based than the more prescriptive proposals above, meaning there will likely be some operational overhead involved for insurers to apply this new methodology.

As we will discuss further below, these overheads are expected to be minimal, regardless of what approach insurers have taken on past FundedRe deals.

The Rebuild Myth

Many insurers immediately assume regulatory change means new systems and new models. Is that really the case here?

Over the last 10 years, regulatory updates have become synonymous with operational complexity – unsurprising, given the level of changes brought about by Solvency II and IFRS17.

However, as all good Actuaries know, correlation is not causation, and these proposals serve as proof. In the PRA’s own words, “the proposals have been designed to leverage firms’ existing calculations and analysis.”

So in this case, there is no need to build bespoke, stand-alone procedures to calculate the new CDA. If anything, insurers may find that leaning more on their existing processes serves as a catalyst to review and modernise historic operating models.

Evolution, Not Revolution

If these proposals are implemented, what practical challenges do you expect insurers to face over the next 12–18 months?

If accepted, these proposals will have an implementation date of 1st July 2027.

While insurers’ CDA calculations for FundedRe will need to be updated to comply with the new rules, the data and modelling techniques required already exist either publicly, or within insurers’ existing processes.

For instance, financial strength ratings for reinsurers are published regularly, and are likely already monitored by insurers. Further, applying the CDA is akin to applying the Matching Adjustment to annuity cashflows, which BPA insurers will already have processes in place for.

Given the increased materiality of the CDA under these proposals, it is likely to come under increased scrutiny from stakeholders. Insurers will therefore have to consider whether any additional Reporting & MI might be required.

Prepare, Don’t Panic

For insurers planning future BPA transactions, what should they be doing now to prepare?

Insurers will firstly be keen to quantify the impact of these changes on the balance sheet and solvency positions of new BPA transactions, if they were to continue with their current FundedRe strategy.

BPA insurers can then compare and contrast this with the effects of other risk management and investment opportunities, including alternative reinsurance structures and more direct investments.

If following this assessment, an insurer decides they may continue to use FundedRe for future BPA deals, then their Pricing assumptions will need to be revised to reflect the increased capital requirements of FundedRe.

As well as the operational considerations outlined above, insurers should also review their current pipeline of BPA transactions, to determine which deals would be likely to fall in or out-of-scope of the proposed changes. In some cases, they may choose to accelerate a deal timeline and utilise FundedRe under the current Solvency UK rules.

That being said, the PRA itself has stated that it “expects the volume of new funded reinsurance arrangements to be transacted before 30th September 2026 to be consistent with firms’ existing plans”.

Final Thoughts

If there’s one message you’d like insurers to take away from your article, what would it be?

By design, the PRA’s proposed changes to the treatment of FundedRe should not require BPA insurers to build new processes from the ground up. However, it does present an opportunity to re-assess and enhance existing systems which will now be more heavily relied upon.

MBE Consulting helps insurers implement regulatory change by building on existing actuarial capabilities — not by creating unnecessary complexity.

Whether you’re assessing the impact of the PRA’s proposed Funded Reinsurance changes, reviewing your actuarial operating model or looking to leverage your existing models and processes more effectively, the MBE Consulting team can help.

5 Minutes With is a new MBE Consulting content series sharing practical perspectives from our people on the issues shaping insurance, actuarial, finance and transformation teams.

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Introduction

In April 2026, the Prudential Regulation Authority (PRA) published a consultation paper on proposed changes to the Solvency UK treatment of Funded Reinsurance (FundedRe).

Much has been written about how the proposals would significantly impact the capital positions of these transactions, whose volumes are increasing within the Bulk Purchase Annuity (BPA) market, and the need for insurers to consider alternative risk management strategies.

This article delves deeper to consider the operational implications of the proposals. For insurers continuing to use FundedRe, there are numerous opportunities to leverage existing models and processes. No stand-alone processes need to be built, and any changes required should not be a burden for BPA writers.

Why Is the PRA Proposing These Changes?

One of the PRA’s key concerns with the current treatment of FundedRe under Solvency UK is that it does not adequately reflect the underlying risks—particularly the counterparty default risk posed by reinsurers.

In addition, the current treatment is considered inconsistent with economically similar assets, such as directly held corporate bonds.

Another concern is that the existing framework is principles-based, which can lead to inconsistencies in how capital is calculated across the industry. As a result, the PRA is proposing a move towards a more prescriptive, rules-based approach.

What Are the Proposed Changes?

Alongside introducing a formal definition of Funded Reinsurance into the PRA Rulebook, the consultation proposes a new methodology for calculating the Counterparty Default Adjustment (CDA).

The CDA measures the expected losses should a reinsurer default and reduces the value of the reinsurance asset on an insurer’s Solvency UK balance sheet.

Under the proposal, the CDA would equal the Fundamental Spread for corporate bonds that match the reinsurance cashflows in both:

• Maturity / duration
• Credit Quality Step (CQS)

Expected Market Impact

For an average BPA FundedRe transaction, the PRA estimates:

• Around a 7% reduction in Solvency UK balance sheet value.
• Capital requirements increasing from 2–4% of underlying annuity liabilities to approximately 10%.

As these proposals are intended to apply to transactions occurring after 30 September 2026, some insurers may choose to accelerate planned transactions.

More broadly, as BPA insurers reassess their capital management strategies and investigate alternatives to FundedRe, it is reasonable to expect that FundedRe volumes within the UK BPA market could decline should the proposals be implemented.

What Does This Mean for Existing Processes and Models?

The PRA itself notes that:

“Implementation costs are expected to be low…the proposals have been designed to leverage firms’ existing calculations and analysis.”

Industry-wide implementation costs are estimated to remain below £500,000 annually (annualised over ten years), assuming current FundedRe volumes continue.

Rather than building entirely new processes, insurers can leverage existing capabilities across several operational areas.

Where Existing Capabilities Can Be Leveraged

Monitoring Reinsurer IFS Ratings

This information is already readily available and is likely already monitored by BPA insurers as part of their existing risk management framework.

Calculating Reinsurance Cashflow Duration

Existing cashflow and asset management models can already determine the maturity and duration of reinsurance cashflows.

Assessing CQS Upward Notches

The applicable upward notches can generally be determined directly from the FundedRe contract and are unlikely to change over time.

Adequacy & Matching Adjustment Eligibility

These depend on how collateral is defined within the FundedRe agreement. Insurers and reinsurers typically agree on the collateral strategy in advance to balance investment returns against eligibility for upward notches.

Credit Enhancement

This notch is unlikely to be widely used, as reinsurers generally already possess strong IFS ratings and therefore have limited incentive to hold lower-return assets purely to improve their rating.

Extracting Fundamental Spreads

Most BPA insurers already have processes to extract and monitor Fundamental Spreads for Matching Adjustment calculations.

These same processes can be readily reused for the proposed CDA calculation.

Applying the CDA

The CDA effectively acts as an adjustment to the discount rate applied when valuing reinsurance cashflows.

This methodology already exists within insurer valuation models through the application of the Matching Adjustment to annuity cashflow discount rates, making it straightforward to extend for FundedRe.

Assumptions Management

Existing assumptions management solutions require only minimal extensions to capture:

• Reinsurer IFS Rating
• Which of the three upward notches apply
• The Fundamental Spread relevant to each FundedRe transaction

Reporting & Management Information

Most BPA insurers already monitor the existing CDA for FundedRe transactions.

Current reporting processes will require only minor updates to reference the revised calculations.

Given the expected increase in materiality, organisations may also wish to introduce more granular reporting to satisfy internal and regulatory stakeholder requirements.

Preparing, Not Panicking

By design, the PRA’s proposed changes to FundedRe treatment under Solvency UK should not be onerous for BPA insurers to implement.

For organisations that already have mature actuarial models, asset management processes and assumptions governance in place, the opportunity is not to build new systems—but to make better use of existing ones.

The proposals may introduce additional capital requirements, but operationally they present an opportunity to streamline existing capabilities rather than create unnecessary complexity.

How MBE Consulting Can Help

Whether you’re assessing the operational impact of the PRA’s proposals, planning for post-September 2026 transactions, or considering how existing actuarial processes can be leveraged more effectively, MBE Consulting can help.

By building on your current operating model, rather than creating new standalone processes. We help insurers implement regulatory change efficiently, pragmatically and with minimal disruption.

Get in touch to discuss how your organisation can prepare for the proposed changes while strengthening its actuarial operating model.

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However, with current trends in the industry seeing a move towards Cloud-based infrastructure, model pointing offers opportunities to increase efficiency and reduce running costs, whilst maintaining the accuracy of results.

Model Pointing – Past, Present & Future

Model Pointing is the process of compressing the details of multiple policyholders, with similar characteristics and expected future behaviour, into a single line before running their information through an actuarial model.

In the past, this was necessary as the hardware and models available did not have sufficient speed and processing power to run calculations on every individual policy. Over time these speeds have increased dramatically, allowing ever more complex runs to be completed, including those at a policyholder level, in acceptable timeframes. As such, over time there has been less emphasis placed on compressing policyholder data.

However, the advent of Cloud-based computing has placed renewed focus on the granularity of this data, since reducing data volumes can reduce costs in two main areas.

• Model run-times: In Cloud-based environments, charges are incurred based on model runtime. For large insurers, the annual cost of running these models can run into the tens of millions in operational expenditure.
• Results Analysis: there are two main methods for storing and analysing results from a Cloud-based model
  a) Results stored in the Cloud: users are charged for Cloud storage space. The more compressed policyholder data becomes, the smaller the results files will be, therefore reducing costs.
  b) Results downloaded from the Cloud to on-premise servers: the greater the granularity of the policyholder data, and therefore the results data, the longer this process will take. Users will be charged according to the time taken, and this can become a bottleneck in actuarial processes.

Benefits & Use-Cases of Model Pointing

By increasing the compression of the policyholder data i.e. performing a greater level of model pointing, this will result in a reduction in both run times and operating costs, particularly for Cloud-based models.

The level of compression performed can be varied according to the run purpose – common use-cases for model pointing include:

• Business Planning
• Capital Planning
• Duration Matching
• Investment Decisions

Model pointing can also be used in reserving & valuation exercises. For instance, the IFRS 17 regulations specify that individual policies can be aggregated into groups (“Units of Account”) according to product, profitability status and year of issue.

Can this be achieved whilst maintaining accuracy?

When model pointing, a balance must be struck between increasing efficiency, compression of the data and retaining the accuracy of the results. This is possible using the latest techniques, such as those described below. As an example, for a large North American life insurer MBE Consulting have been able to compress their existing model points by 40%, whilst retaining over 99% accuracy of total cashflows over a 75-year projection period.

Model Pointing Techniques

Alongside the well-established algorithmic approach to model pointing, new machine-learning techniques such as clustering have been used successfully in actuarial modelling in recent years.

Algorithmic Approach

This refers to a methodical, logical process that is followed to group model points. This approach relies on a finite sequence of well-defined, computer-implementable instructions.

Techniques which can be used to further improve the efficiency and accuracy of the algorithmic approach include:

• Intelligent grouping: This involves the re-engineering of informative features from the policy data that can be used to distinguish between different policyholder groups. This typically involves combining or transforming raw policy attributes to create a new feature that captures the underlying risk profiles or characteristics. An example would be to group smokers and non-smokers together, with the addition of a ‘proportion smoker’ field.
• Rationalising segment boundaries: combining non-material segments of a policyholder variable with material segments.

Such algorithms are predictable and repeatable but can lack flexibility and require deep understanding of the problem to implement effectively. In addition, these algorithms can result in under-grouping i.e. model points that are too narrowly defined, resulting in disparate groups with relatively few policies in each group. This can result in increased computational time and resource requirements without necessarily improving the accuracy of the modelling outcomes.

Clustering

This is a method used in data analysis and machine learning to organise a data set into “clusters”. Data within the same cluster is more similar to each other than to those in other clusters.

This approach is a form of unsupervised learning, as it does not rely on pre-labelled data to form the groups. Instead, it identifies patterns and similarities in the data to determine the grouping.

Clustering can therefore reveal natural groupings and patterns in the data that might not be apparent upfront, making it extremely flexible and useful in uncovering insights and enabling informed decision-making. It is also much less prone to under-grouping (see above).

However, choosing the parameters required for clustering introduces subjectivity and can affect the results significantly – finding the optimal parameters often requires trial and error. Furthermore, the resulting groupings may not be explainable using actuarial logic and thus can be difficult to justify to stakeholders and auditors.

Pseudo-Clustering

MBE Consulting have pioneered a pseudo-clustering technique, which combines the strengths of algorithms and clustering to maximise compression whilst maintaining accuracy and auditability.

After applying an algorithm, the under-grouped model points are then clustered together with other, more-compressed model points. New machine-learning techniques are used to identify the most influential characteristic of the portfolio of model points. These are then used by the algorithm to systematically allocate the under-grouped model points to the more-compressed model points.

This methodology offers the advantages of an algorithmic approach, whilst also removing under-grouping with minimal impacts on model accuracy. The clustering element is also fully controlled and repeatable, and users have the flexibility to quantify what they consider to be an under-grouped model point.

Contact Us

As the industry continues to move towards Cloud-based infrastructure, model pointing offers a wealth of benefits. At MBE Consulting we have experience of transforming the model pointing processes of multiple insurers worldwide. If you’re interested in how we can improve your efficiency and reduce costs, without impacting accuracy, contact us today.

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