Introduction: What Comes After Implementation?

For much of the last decade, actuarial modernisation has been driven by technology transformation. Insurers have invested in new actuarial platforms, implemented IFRS 17, replaced legacy systems and introduced more sophisticated modelling environments.

These initiatives were necessary. They established the technology, models and reporting capabilities required to support increasingly complex actuarial functions.

But for many insurers, the next challenge is no longer simply getting technology in place. It is making those capabilities work together effectively.

A modern technology environment does not automatically create a modern actuarial operating model. Even with sophisticated platforms, actuarial functions can remain dependent on manual processes, fragmented data, disconnected models, repeated reconciliations and significant human intervention.

This raises a more important question for actuarial leaders: How effectively does the entire actuarial operating model work? The next phase of actuarial modernisation is about moving beyond implementation towards operational optimisation, end-to-end workflow orchestration and, ultimately, AI-enabled actuarial operations.

Phase 1: Modernisation Through Technology Implementation

The first phase of modernisation focused on establishing the necessary technology and compliance foundations.

For many insurers, the priorities included:

• Digitalising actuarial processes.
• Implementing IFRS 17.
• Replacing or upgrading legacy technology.
• Introducing modern modelling platforms.
• Improving reporting capabilities.
• Establishing new data structures.

The emphasis was on implementation: introducing the systems and capabilities needed to meet regulatory requirements and support the business.

For many organisations, this represented a substantial transformation. However, completing an implementation does not necessarily mean that the wider operating model has become efficient.

A system can perform exactly as intended while the processes surrounding it remain fragmented. Data may still require manual transformation, model outputs may need to be transferred between systems, and teams may continue to spend considerable time reconciling results.

This is the distinction between implementing technology and modernising the way the actuarial function operates.

Implementation asks: Does the technology work?
Modernisation asks: Does the operating model work?

The next phase begins when the focus shifts from what has been implemented to how effectively those capabilities are used.

Phase 2: Modernisation Through Operational Optimisation

The second phase is about improving the performance of the entire actuarial operating model.

Rather than focusing solely on individual platforms or processes, the objective is to reduce operational friction, improve connectivity and create more efficient end-to-end workflows.

Four priorities underpin this progression.

1.  Optimise
Identify and remove unnecessary steps, duplication and inefficiencies across the actuarial lifecycle.

Before automating a process, organisations need to understand whether the process itself is effective. Automating an inefficient workflow can simply make an inefficient process run faster.

2.  Automate
Reduce repetitive manual activity and improve consistency through automation.

This can include routine calculations, data preparation, recurring processing activities and other tasks that follow established rules.

Automation creates value, but its impact is limited when the wider workflow remains disconnected.

3.  Integrate
Connect data, models, systems and downstream processes so that information can move more effectively across the actuarial function.

Integration reduces the need for repeated data transfers, manual reconciliations and duplicated transformation activities. It also helps create a more consistent flow of information between actuarial, finance and reporting teams.

4.  Orchestrate
Move beyond automating individual tasks towards coordinating the end-to-end actuarial workflow.

An orchestrated process connects the activities that make up the actuarial lifecycle, from data receipt and validation through model execution, output analysis, exception management, human review and reporting.

The objective is not simply to make individual tasks faster. It is to make the entire operating model work more effectively.

This progression can be summarised as:
Optimise → Automate → Integrate → Orchestrate

The distinction matters. Automating a single process may save minutes or hours. Orchestrating the end-to-end process can change how the actuarial function operates.

The Hidden Cost of Fragmentation

One of the most persistent challenges in actuarial modernisation is fragmentation between otherwise capable systems, models and processes.

Consider a typical actuarial workflow.

Data originates in one system and is transformed through another process. A model consumes that data and produces outputs. Those outputs are exported, checked and reconciled. Finance receives results, reporting teams assemble information, and exceptions are identified for further investigation.

Finally, the actuary reviews the information and applies professional judgement.

Each individual component may work effectively. Yet the overall workflow can still be inefficient.

Fragmentation between data, models, finance, reporting, the general ledger (GL), scenario analysis and governance can create several operational challenges:

• Repeated hand-offs:
  Information moves between teams and systems, requiring additional coordination.
• Manual reconciliation:
  Teams spend time checking and resolving differences between outputs.
• Duplicated transformation:
  Data is processed more than once across disconnected workflows.
• Disconnected controls:
  Validation and governance activities may be spread across different stages of the process.
• Delayed insight:
  Information takes longer to move through the lifecycle and reach decision-makers.
• Reduced actuarial capacity:
  Skilled professionals spend more time on production and repetitive execution, leaving less capacity for interpretation and analysis.

These costs are not always visible when individual systems are assessed in isolation. A model may run efficiently, a reporting platform may function correctly, and a data process may meet its requirements, while the hand-offs between them continue to consume time and resources.

This is why modernisation cannot stop at improving individual components.

The opportunity lies in reducing friction between those components and improving how the whole actuarial workflow operates.

Addressing fragmentation creates the conditions for greater consistency, more effective use of existing technology and a stronger foundation for intelligent automation.

Automation Isn’t the End Goal

Automation is an important part of actuarial modernisation, but it should not be mistaken for the final objective.

Automating individual tasks can improve efficiency within a specific process. However, if those tasks remain disconnected, the wider operating model may still depend on manual intervention and repeated coordination.

The progression is therefore broader than task automation alone:
Manual process → Automated task → Integrated workflow → Orchestrated operating model

Consider a workflow in which data arrives, undergoes validation, feeds into model runs, produces outputs for checking, and then moves into reporting and exception management.

Automating the model run may improve one stage of the process. But if data validation remains manual, outputs need to be transferred between systems, and exceptions must be identified through separate activities, significant operational friction remains.

An integrated and orchestrated workflow aims to coordinate these activities more effectively.

This requires organisations to look beyond the performance of individual tasks and examine how information, processes and decisions connect across the actuarial lifecycle.

The goal is not automation for its own sake. It is an operating model in which technology supports a more coordinated, repeatable and efficient end-to-end process.

That distinction becomes increasingly important as insurers consider the next stage of modernisation: AI-enabled actuarial operations.

Phase 3: The AI-Enabled Actuarial Function

As actuarial operating models become better integrated and orchestrated, AI and intelligent automation create opportunities to change how work is executed and where actuarial expertise is applied.

The emerging possibility is a workflow in which technology does more than execute predefined instructions. It can increasingly support the coordination of activities across a process, help identify exceptions and direct attention towards areas requiring human review.

A future-oriented workflow could look like this:

Data → Models → Validation → Exception management → Actuary → Decision
AI & automation increasingly support execution, coordination and workflow across the process.

Within this model, technology can increasingly support activities such as:

• Execution of routine processes.
• Coordination of workflow activities.
• Routine validation.
• Identification of exceptions.
• Workflow management.

Actuarial professionals can then focus more of their capacity on the areas where their expertise is most valuable:

• Professional judgement.
• Interpretation of results.
• Challenging assumptions and outcomes.
• Governance and oversight.
• Communication of insights.
• Decision support.

The intention is not to remove the actuary from the process. It is to reduce the operational burden surrounding actuarial judgement, allowing professionals to concentrate on interpretation, challenge and decisions.

AI does not remove actuarial expertise. It changes where that expertise creates value.

However, this transition depends on the quality of the operating foundation beneath it.

Why AI Isn’t the Starting Point

AI-enabled operations are an emerging stage of actuarial modernisation, but implementing AI alone does not resolve the underlying challenges of an actuarial function.

If data is fragmented, AI inherits that fragmentation. If processes are inefficient, automation can accelerate inefficient processes. If models are poorly integrated, AI does not automatically resolve the integration problem. And if governance is unclear, introducing AI creates additional questions rather than removing them.

The foundation matters.

Organisations need to understand their processes, improve how systems and models connect, establish appropriate controls and create a more coherent operating model before expecting intelligent automation to deliver its full potential.

This is not an argument against AI. It is a recognition that its effectiveness depends on the environment in which it operates.

The six enablers of actuarial modernisation provide a useful framework for assessing that foundation.

The Six APM^TM Enablers

At MBE Consulting, the APM Framework considers actuarial performance across six interconnected enablers: People, Processes, Methodologies, Systems, Models and Data. Together, they provide a useful lens for assessing the operating foundation required for modernisation

PEOPLE
Do teams have the skills and capacity required for a modern actuarial function?

Modernisation must consider how people work alongside technology and how actuarial expertise is applied as routine activities become increasingly automated.

PROCESSES
Are workflows efficient, repeatable and scalable?

Processes should minimise unnecessary steps, duplication and manual hand-offs while supporting consistent execution across the actuarial lifecycle.

METHODOLOGIES
Are actuarial methodologies designed for the current business and reporting environment?

Methodologies need to support the requirements of the organisation and work effectively within its broader modelling, reporting and operational processes.

SYSTEMS
Do technology platforms support the required operating model?

Systems should enable the necessary connectivity between activities rather than simply functioning as separate solutions.

MODELS
Are models efficient, maintainable, transparent and fit for purpose?

Models remain central to actuarial work, but their effectiveness also depends on how they connect with data, systems and downstream processes.

DATA
Is data accessible, controlled, consistent and usable?

Data underpins the actuarial lifecycle. Its quality, accessibility and consistency influence the effectiveness of modelling, reporting, validation and any subsequent AI-enabled processes.

Technology touches all six enablers, but technology alone does not resolve every challenge within them. Together, these six areas provide the foundation for operational optimisation and the intelligent automation that may follow.

Bringing the Three Phases Together

The evolution of actuarial modernisation can be understood as three connected phases.

Phase 1: Technology Implementation

Digitalise processes, implement IFRS 17, replace legacy technology and establish the necessary platforms and capabilities.

Phase 2: Operational Optimisation

Optimise processes, automate repetitive tasks, integrate data and systems, and orchestrate end-to-end workflows.

Phase 3: Intelligent Automation

Build towards AI-enabled actuarial operations in which technology increasingly supports execution, coordination and exception identification, while human expertise remains central to judgement and decision-making.

The six enablers (People, Processes, Methodologies, Systems, Models and Data) provide the foundation throughout all three phases. Automation and AI are capabilities that can be layered across the operating model as that foundation develops.

The phases are connected rather than isolated. The progress made in implementation creates the opportunity for optimisation, while a stronger and more integrated operating model creates better conditions for intelligent automation.

The objective is to move from having modern technology to operating a modern actuarial function.

The Question Actuarial Leaders Should Now Be Asking

The next generation of actuarial modernisation is not simply about buying another platform. It is not simply about implementing AI, and it is not finished when IFRS 17 goes live.

The more important question is:

How effectively does your actuarial operating model convert data and models into timely, reliable and decision-useful insight?

Answering this requires a broader view of the actuarial function: how people work, how processes operate, how methodologies are applied, how systems connect, how models perform and how data moves across the organisation.

It also requires recognising that efficiency is determined not only by the performance of individual components, but by how effectively the entire workflow operates.

For insurers that have already invested significantly in actuarial technology, this represents an important opportunity to realise more value from their existing capabilities.

The next phase begins with optimisation, progresses through integration and orchestration, and creates the foundation for AI-enabled actuarial operations.

Ultimately, the goal is not simply to modernise technology. It is to modernise how the actuarial function delivers value.

The post The Next Phase Of Actuarial Modernisation appeared first on MBE.

Why AI Alone Won’t Transform Your Actuarial Function

Introduction

Artificial intelligence is rapidly changing the conversation across insurance.

From underwriting and claims to pricing, customer service and risk management, the potential applications appear almost limitless. AI promises faster decisions, improved productivity, deeper insights and entirely new ways of operating.

But there is an important distinction that is often overlooked:

AI has the potential to transform actuarial functions. But AI, on its own, is not transformation.

In fact, introducing AI into an inefficient operating environment can simply make existing problems happen faster.

Fragmented processes become faster fragmented processes. Poor-quality data is processed more quickly. Disconnected systems generate more automated outputs. Teams may gain another powerful tool without changing the way they actually work.

The question, therefore, should not simply be: “Where can we use AI?”
It should also be: “What are we building AI into?”

That is the missing link.

AI Doesn’t Replace Actuarial Transformation. It Accelerates It

There is a temptation to see AI as a shortcut around the difficult work of transformation.

If actuarial processes are slow, automate them. If teams spend too much time manipulating data, introduce AI. If reporting takes too long, use generative tools to accelerate analysis.

These applications may create genuine value. But they do not automatically address the underlying causes of inefficiency.

An actuarial function may still be constrained by:

• Manual and fragmented processes
• Poor or inconsistent data
• Spreadsheet dependency
• Disconnected systems
• Unclear ownership and decision rights
• Weak governance
• Limited integration between teams and technology

AI does not operate independently of these conditions. It inherits them.

And because AI can increase the speed at which work is performed, it can also accelerate the consequences of an ineffective operating environment.

AI accelerates whatever already exists.

That can be a powerful advantage when the underlying foundations are strong. But when they are weak, faster technology does not necessarily create better performance. It can simply create faster inefficiency.

Real transformation requires organisations to understand how work is performed today, where friction exists and what needs to change before, alongside and after AI is introduced.

AI should be part of the transformation journey. It should not be mistaken for the transformation itself.

AI Isn’t Your Competitive Advantage. Your Operating Model Is.

As AI capabilities become more widely available, access to the technology itself will become less of a differentiator.

Competitors can access similar models. They can purchase similar platforms. They can hire similar technical talent.

Over time, the question will become less about who has AI and more about who knows how to make AI perform.

Two insurers may deploy similar AI capabilities and achieve completely different results. One may struggle to move beyond experimentation. The other may fundamentally improve productivity, decision-making and speed.

The difference is unlikely to be explained by the algorithm alone. It will depend on the environment surrounding it.

That includes:

• How work is designed
• How data is managed and made available
• How people interact with AI-enabled capabilities
• How decisions are governed
• How processes connect across functions
• How technology is embedded into everyday operations

The real competitive advantage lies in the operating model that brings these elements together.

The future advantage will not simply be having AI. It will be having an organisation capable of turning AI into sustained performance.

This requires a shift in thinking.

AI should not be treated as a separate technology initiative sitting alongside the business. It needs to become part of how the business operates.

That means redesigning workflows, responsibilities, governance and ways of working—not simply adding another tool to an existing process.

Why Most AI Projects Stall in Insurance

Many AI initiatives begin with genuine momentum.

A promising opportunity is identified. A proof of concept is developed. The technology works. The demonstration is impressive.

And then the project struggles to move forward. The problem is often not the technology. It is everything around it.

AI initiatives can stall when organisations encounter challenges such as:

Weak governance

• Who owns the AI-enabled process?
• Who is accountable for the outcome?
• How are models validated, monitored and challenged?
• What happens when the output conflicts with professional judgement?
• Without clear governance, organisations can struggle to move from experimentation to operational use.

Poor data foundations

AI is only as useful as the information and context available to it.

If data is fragmented, inconsistent, inaccessible or poorly governed, the potential of AI is constrained before the model even enters the conversation.

Limited organisational adoption

A technically successful solution has limited value if the people expected to use it do not trust it, understand it or incorporate it into their daily work.

Adoption is not something that happens automatically once the technology has been deployed.
It needs to be designed into the transformation.

Disconnected processes

An AI solution that sits outside the core workflow can quickly become another destination that people need to visit. Instead of removing friction, it can introduce another step.

The real opportunity comes when AI is integrated into the flow of work—supporting decisions, automating appropriate activities and allowing people to focus their expertise where it adds the greatest value.

The gap between a successful AI pilot and measurable business value is rarely just a technology problem. It is often an operating model problem.

Modernising Actuarial Functions for the Age of AI

AI creates significant new opportunities for actuarial functions.

It can assist with analysis, automation, model interaction, documentation, scenario exploration and decision support. But these capabilities require an operational environment capable of supporting them. This is where actuarial modernisation becomes essential.

At MBE Consulting, we believe sustainable actuarial performance depends on the interaction between a number of critical enablers:

• People
• Processes
• Methodologies
• Systems
• Models
• Data

Together, these elements form the operating environment within which actuarial work is performed.
AI does not sit separately from these enablers. It interacts with every one of them.

AI requires appropriate data. It needs systems capable of supporting it. It needs processes that define where and how it is used. It needs methodologies that provide consistency and control. It needs governance around models and outputs.

And it needs people who understand when to trust, challenge and apply its insights.
This is why introducing AI cannot be separated from the broader question of how an actuarial function operates.

AI may be the accelerator. But the operational foundation determines how far the organisation can go.

Modernisation, therefore, is not about replacing everything with AI. It is about creating an environment where new capabilities can be introduced, adopted and scaled without adding further complexity.

From AI Pilots to Operational Performance

A proof of concept can answer an important question: Can this technology do it?

But operational transformation requires a much more difficult question:
How do we redesign the way we work around this capability?

This is the difference between an AI pilot and operational AI.

An AI Pilot

The focus is often on demonstrating technical possibility.

• Can the model analyse the information?
• Can it produce the output?
• Can it complete the task faster?

Operational AI

The focus shifts towards sustained performance.

• Where does AI fit into the workflow?
• Who owns the output?
• How is it validated?
• What controls are required?
• How will performance be measured?
• How will teams adopt new ways of working?
• Can the capability be reused and scaled?

The ultimate measure of success should not be how impressive the demonstration was. It should be whether something meaningfully changes in the day-to-day operation of the business.

• Does the process become faster?
• Does decision quality improve?
• Does the team spend less time on low-value work?
• Can capacity be redirected towards more complex actuarial challenges?
• Can the organisation scale without increasing cost at the same rate?

A successful pilot proves potential. Operational integration creates value.

The goal should not be to accumulate proofs of concept. The goal should be to build capabilities that become part of how the organisation performs.

The Missing Layer Between AI and Business Value

The AI conversation often appears deceptively simple.

AI Capability → Business Value

But there is a significant layer in between.

That layer includes the people, processes, data, models, systems, methodologies and governance that allow technology to operate effectively.

The equation is closer to this:

AI Capability

↓

The Operational Foundation
People. Processes. Methodologies. Systems. Models. Data.

↓

Sustainable Business Value
This is the missing link. Technology creates potential. But potential is not the same as performance.

Performance emerges when technology is embedded within an operating environment that allows it to be trusted, adopted, governed and scaled.

The most successful organisations will not necessarily be those that experiment with the greatest number of AI tools.

They will be the organisations that understand where AI can create the greatest value and build the operational capability required to support it.

That requires more than an AI strategy. It requires transformation.

The Real Question for Actuarial Leaders

AI will continue to evolve. The tools will become more capable. The use cases will expand. And the pressure to move faster will increase.

But actuarial functions should resist the temptation to treat AI as a shortcut around transformation.

The more useful question is not simply: “How can we introduce AI?”
It is: “Is our actuarial operating environment ready to make AI perform?”

• Are the right data foundations in place?
• Are processes designed to support integration and automation?
• Are systems connected?
• Are models and methodologies governed appropriately?
• Are people equipped to work alongside new capabilities?
• Are ownership and decision rights clear?

And, perhaps most importantly: Are we redesigning the way we operate or simply adding AI to the way we already work?

Because AI does not automatically create an efficient actuarial function. It does not automatically remove operational complexity. And it does not automatically translate investment into business value.

AI accelerates whatever is already there.

The opportunity for insurers and actuarial functions is therefore not simply to adopt AI.
It is to build the foundation that allows AI to become part of sustainable operational performance.

That is the missing link between AI potential and business value.

AI & The Missing Link

AI has the potential to reshape the insurance industry.
But technology alone will not create lasting advantage.

The organisations that succeed will be those that connect AI with the people, processes, methodologies, systems, models and data required to turn potential into performance.

AI is not a replacement for actuarial transformation. It is the next accelerator of it.

Is your actuarial function ready?

Explore how The APM Framework can help create the operational foundation for sustainable actuarial performance in the age of AI.

The post AI & The Missing Link appeared first on MBE.

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.

The post 5 Minutes With Chris Lennon appeared first on MBE.

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.

The post PRA & Funded Reinsurance appeared first on MBE.

Actuarial functions are investing heavily in new systems, data platforms, automation and modelling capabilities. But for many organisations, the expected improvement in performance is still not fully materialising.

Why?

In this edition of 5 Minutes With, we speak to Andries Beukes, Partner, Actuarial Solutions at MBE Consulting, about why actuarial performance improvement requires more than better technology. He shares his perspective on the common misconceptions surrounding transformation, the operating model challenges that limit progress and what separates high-performing actuarial teams from the rest.

The Silver Bullet Myth

From your experience, what is the most common misconception organisations have about improving actuarial performance?

The most common misconception is that there is a single “silver bullet” that will transform actuarial performance, often a new system, a faster model or a better data platform.

An actuarial function is a complex ecosystem where people, processes, systems, data, models and methodology all influence one another. Sustainable improvement comes from taking a holistic approach.

You can invest in better technology, but if the data is poor, the process is inefficient or the team does not have the right capabilities, the value will be limited.

It is a bit like a game of chess. You do not win by moving one piece. You win by understanding the board and moving the right pieces in the right sequence.

Why Technology Alone Doesn’t Improve Performance

Many organisations invest heavily in systems, data and models, yet still struggle to improve performance. Why is that?

The main reason is that technology is often implemented without changing the operating model around it.

Many organisations treat a new system, data platform or model as the transformation itself. But performance only improves when the underlying ways of working also change.

A better actuarial system placed into a weak operating model often just makes the inefficiencies more expensive.

The second issue is capability. Organisations may acquire the right solutions, but fail to equip people to use them effectively, embed them into workflows or translate outputs into better business decisions.

Technology can be a powerful enabler, but it does not create value in isolation. It needs to be supported by the right processes, ownership, skills and decision-making environment.

When Capacity Is Consumed By Production

What patterns do you consistently see across actuarial teams that limit performance?

A common pattern is that actuarial teams are working extremely hard, but not always on the activities that create the most value.

Too much capacity is often absorbed by production activity: running models, moving data, checking outputs, reconciling numbers and preparing reports.

These activities are important. They keep the function operating and they support critical reporting, governance and decision-making. But when they dominate the agenda, there is less time for insight, challenge and supporting better business decisions.

I also see fragmented processes, unclear ownership and tools or models that are technically sound but difficult to operate in practice. Over time, teams build workarounds to keep things moving, especially around data and reporting, and those workarounds become part of the operating model.

The result is a function that is reactive: one that delivers what is required, but with too little capacity left to improve, innovate or influence the wider business.

Start With Purpose, Not Technology

If you had to challenge one common approach to actuarial transformation, what would it be?

I would challenge the tendency to start with the solution before clearly defining the strategic direction of the actuarial function.

Too often, transformation begins with a system implementation, model rebuild or automation initiative. These may be necessary, but they should not be the starting point.

The first step should be to define what the actuarial function needs to become, what value it should create for the business and how it should support decision-making.

Without that direction, modernisation can become a set of disconnected initiatives. You may end up with a faster model or a modern system, but still have poor data, inefficient hand-offs, limited adoption or outputs that do not improve business decisions.

Successful actuarial modernisation starts with purpose. The tools and operating model should follow from that.

Establish The Baseline

Where should organisations focus first if they want to see meaningful improvement?

Organisations should start by baselining the current performance of the actuarial function.

Before setting an ambitious future direction, they need a clear and honest view of where they are today. Without understanding the current state, it is very difficult to define the right destination, prioritise the right interventions or measure whether progress is being made.

That baseline should look across the key enablers of actuarial performance. It should identify where the biggest constraints are, where effort is being absorbed and where the function is not yet creating the value it could.

Once the current state is clear, organisations can define a more realistic target state and break improvement into practical steps.

Meaningful actuarial change starts with knowing where you are, where you want to go and how you will track progress along the way.

Why Structure Matters

How does a more structured view of actuarial performance change the way organisations approach improvement?

A structured view changes the conversation from “we need to improve” to “we know where we need to improve, why it matters and how we will measure progress.”

Without structure, improvement can become subjective. It becomes harder to prioritise action, focus investment or prove that change has delivered value.

By understanding what drives the performance of the actuarial team, organisations can define meaningful KPIs, assess current performance, identify root causes and track whether interventions are working.

That visibility helps actuarial leaders focus investment where it has the greatest impact. It also creates a more evidence-based approach to building a higher-performing actuarial function.

From Production to Strategic Partnership

How do you see the role of actuarial teams evolving over the next few years?

Actuarial teams will continue to shift from being producers of outputs to strategic partners to the business. This shift will be accelerated by automation, better data platforms, more integrated systems and the rise of AI-enabled solutions.

Historically, a lot of actuarial capacity has been absorbed by production activity: running models, preparing reports, reconciling data and explaining movements. Those activities will not disappear, but the manual effort involved should reduce over time.

The opportunity is for actuarial teams to spend more time advising management and helping the business make better decisions around pricing, capital, risk, profitability and growth.

That also means the skill set must continue to evolve. Technical actuarial expertise remains essential, but teams will increasingly need to collaborate with data scientists, engineers, technology teams and AI specialists.

The best actuarial teams will design themselves around value creation, not just faster production.

Final Thoughts

In one sentence, what separates high-performing actuarial teams from the rest?

High-performing teams turn actuarial expertise into timely, trusted and commercial insight that helps business leaders create value.

What is one lesson you have learned from 20 years in this space?

One lesson is that most actuarial teams are not short of technical ability. They are usually full of capable people working extremely hard.

The challenge is often that their effort is absorbed by activity that keeps the function running, rather than activity that helps the business make better decisions. Running models, moving data, checking outputs and producing reports all matter, but they can leave very little room for insight, challenge and improvement.

That is why performance improvement is not just about better tools or faster processes. It is also about creating the space for actuaries to apply their judgement, ask better questions and influence the wider business.

When that happens, the actuarial function starts to move from being a producer of outputs to a more strategic partner to the organisation.

MBE Consulting helps insurance organisations assess and improve actuarial performance across people, processes, systems, data, models and methodology.

To discuss how your actuarial function is performing today, or where the biggest opportunities for improvement may be, get in touch with the MBE Consulting team.

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.

The post 5 Minutes With Andries Beukes appeared first on MBE.

Introduction

Throughout this series, we have explored why actuarial modernisation so often falls short of expectations. We have seen that the challenge is rarely a lack of investment, capability or technical expertise. More often, it stems from the way improvement is approached.

Actuarial performance is shaped by the interaction of people, processes, methodologies, systems, data and models. Improving any one of these areas can deliver meaningful benefits, but lasting improvement depends on understanding how they work together across the function as a whole. This raises an important question.

If actuarial performance is created through the interaction of multiple interconnected enablers, how do organisations determine where improvement should begin?

For many actuarial leaders, this is where modernisation becomes most difficult. Not because they lack ideas for improvement, but because they lack a structured way of understanding where the greatest constraints exist, how different challenges relate to one another and which initiatives will have the greatest overall impact.

This is the missing discipline.

From Understanding to Action

Understanding why performance is constrained is only part of the challenge. The more difficult task is deciding where improvement should begin. For many actuarial leaders, this is where modernisation becomes most difficult.

• Where should we start?
• Which areas require the most attention?
• How do we prioritise competing initiatives?
• How do we ensure that improvements are coordinated rather than fragmented?

These are not straightforward questions. The complexity of the actuarial environment, combined with ongoing business, regulatory and operational demands, makes it difficult to step back and assess the function in a structured and objective way.

As a result, improvement efforts are often guided by immediate priorities, localised needs or the most visible opportunities for change. While this can be effective in addressing individual issues, it does not always provide a clear path towards sustained, system-wide performance improvement.

Moving from understanding to action requires something more.

It requires a structured way of creating visibility over the actuarial function, identifying where the greatest constraints exist and prioritising improvement in a way that strengthens the performance of the function as a whole.

Why Structured Assessment Matters

Effective improvement begins with understanding the current state of the actuarial function. Not simply within individual areas, but across the function as a whole. This means developing a clear view of where strengths exist, where constraints are limiting performance and how different issues relate to one another.

Without this visibility, improvement initiatives are often based on assumptions rather than evidence. Different parts of the function may be evaluated using different criteria, making it difficult to compare capabilities, establish priorities or measure progress over time.

A structured assessment addresses this by providing a consistent way of understanding performance across the actuarial function. Rather than focusing on individual initiatives or isolated issues, it creates a broader view of how the key enablers operate together and where improvement is likely to have the greatest overall impact.

This enables actuarial leaders to:

• identify the constraints that have the greatest influence on overall performance.
• understand how issues in one area affect capability elsewhere.
• prioritise improvement initiatives more effectively.
• develop a clearer and more coordinated roadmap for change.

Most importantly, it shifts improvement from being reactive to becoming deliberate. Rather than responding to the most visible problems, organisations are able to focus on the areas that will create the greatest long-term improvement in actuarial performance.

The Missing Discipline

Over the past decade, actuarial modernisation has rightly focused on strengthening specific areas of the function. Organisations have invested in new systems, improved data, enhanced modelling capability and increased automation. These investments remain essential and continue to play a critical role in the evolution of the actuarial function.

However, sustainable performance improvement depends on something more. It requires a disciplined way of understanding how the function operates as a whole. Without that discipline, improvement initiatives are often prioritised independently, progress is measured inconsistently and underlying constraints can remain hidden despite significant investment.

This is not a question of effort. Nor is it a question of technical capability.

It is about creating sufficient visibility to understand where performance is being constrained, how different issues relate to one another and where improvement will have the greatest overall impact.

That is the missing discipline.

Not another transformation programme. Not another technology investment. But a structured way of understanding the actuarial function before deciding how it should evolve.

A Practical Way Forward

These principles underpin MBE Consulting’s Actuarial Performance Management (APM) Framework.

The framework was developed in response to a challenge we have observed repeatedly across actuarial functions.

While organisations invest significantly in modernisation, there is often no consistent way of understanding current capability, identifying where the greatest constraints exist or prioritising improvement across the function as a whole.

The APM Framework addresses this by providing a structured approach to assessing actuarial performance across six interconnected enablers: People, Processes, Methodologies, Systems, Data and Models.

Rather than evaluating these areas independently, it considers how they interact, where misalignment is creating operational friction and which constraints are having the greatest impact on overall performance.

This provides actuarial leaders with a consistent way to:

• understand current capability across the function.
• identify the constraints that have the greatest impact on performance.
• prioritise improvement initiatives based on overall value.
• develop a coordinated roadmap for sustainable change.

Ultimately, the value of the APM Framework lies not in the assessment itself, but in the visibility and clarity it provides.

By establishing a common understanding of how the function operates, organisations are better able to align stakeholders, make better informed investment decisions and focus improvement where it will have the greatest long-term impact.

Structured assessment is not the end of the modernisation journey. It is the foundation on which more effective actuarial transformation can be built.

This concludes our “Missing Link in Actuarial Modernisation” series.

Over the past eight articles, we’ve explored why actuarial transformation often falls short, what distinguishes high-performing functions and why structured assessment is the missing discipline.

If you’d like to understand how these principles apply to your own actuarial function, we’d love to continue the conversation.

The post The Missing Discipline appeared first on MBE.

Introduction

By this point, most actuarial leaders recognise that improving performance is not simply a matter of upgrading systems, improving data or enhancing models.

The challenge is rarely identifying opportunities for improvement. The challenge is understanding how those opportunities relate to one another and where effort should be focused to achieve the greatest overall impact.

This is often where improvement becomes difficult. Individual areas of the actuarial function are typically well understood. Teams understand their systems, their models, their data and their processes. What is often less clear is how these different elements collectively shape performance.

Without a structured way of viewing the function, it becomes difficult to distinguish between symptoms and underlying constraints, or to understand how changes in one area affect another.

This requires a different perspective. One that views actuarial performance not through individual initiatives, but through the key factors that collectively enable the function to operate effectively.

A Structured View of Actuarial Performance

While individual areas of the actuarial function are often well understood, there is rarely a consistent way of viewing how they fit together.

Discussions around improvement typically focus on specific initiatives, systems, models, data or processes. These conversations are important, but they can make it difficult to understand how different issues relate to one another or how they collectively influence performance.

As a result, organisations often find themselves addressing symptoms rather than underlying constraints. A process issue may be rooted in system limitations. A modelling challenge may be driven by data constraints. A delivery bottleneck may stem from unclear responsibilities or inconsistent methodologies.

Viewed in isolation, each issue appears separate. In practice, they are often interconnected. Without a structured view of the function, it becomes difficult to answer some fundamental questions:

• Where are the most significant constraints on performance?
• How do different challenges relate to one another?
• Which areas should be prioritised, and in what sequence?
• How will change in one area affect the rest of the function?

These questions require more than an understanding of individual components. They require a consistent way of understanding how performance is created across the actuarial function as a whole.

One practical way of achieving this is to consider the function through a set of core enablers that collectively shape performance.

The Six Enablers of Actuarial Performance

Through our work with actuarial functions, six areas consistently emerge as the primary enablers of performance.

While organisations may use different terminology or structures, the same underlying themes repeatedly influence how effectively the function operates and delivers value.

These six enablers are:

• People – the skills, experience and capacity required to deliver actuarial work effectively.
• Processes – the structure and flow of activities that underpin consistent and timely delivery.
• Methodologies – the approaches, assumptions and techniques used to produce results that are both robust, explainable and appropriate.
• Systems – the tools and platforms that support execution, efficiency and scale.
• Data – the availability, quality and accessibility of information required for analysis, modelling and insight.
• Models – the mechanisms through which actuarial calculations, projections and analysis are performed.

Individually, each of these areas contributes to actuarial performance. Collectively, they provide a structured way of understanding how the actuarial function operates, where constraints exist and how improvement opportunities relate to one another.

Importantly, no single enabler determines performance on its own. The value comes from understanding them together.

Performance Is Created Through Interaction

The value of the six enablers lies not simply in identifying important areas of the actuarial function. It lies in understanding how those areas influence one another.

A model may be technically robust, but its effectiveness depends on the quality of the data that supports it, the systems through which it operates, the methodologies that govern its use and the people responsible for maintaining and interpreting its outputs.

The same principle applies across all six enablers. Challenges rarely sit neatly within a single area. More often, they emerge through the interaction between them.

A process issue may be driven by system limitations. Data challenges may increase reliance on manual workarounds. Inconsistent methodologies may create additional complexity across models and reporting processes.

Viewed individually, these issues can appear unrelated. Viewed collectively, they often reveal the underlying constraints affecting performance.

This perspective provides a more complete understanding of how the actuarial function operates and where improvement efforts are likely to have the greatest impact.

Achieving Balance Across the Function

The objective is not to maximise every enabler independently.

In practice, actuarial performance is rarely constrained by a single area operating in isolation. More often, constraints emerge where capability is uneven, priorities are misaligned or one enabler limits the effectiveness of another.

This is why improving performance requires more than raising the maturity of individual components.

It requires an understanding of where the most significant constraints exist and how different areas of the function contribute to overall performance.

By considering the six enablers collectively, organisations can develop a clearer view of current capability and identify where targeted improvement is likely to have the greatest impact. In some cases, this may involve investing in technology. In others, it may require simplifying processes, improving data accessibility, strengthening methodologies or addressing capability gaps within teams.

The value of this perspective is not that it provides a single answer. It provides a structured way of identifying where effort should be focused and how improvement can be approached in a more coordinated and effective manner.

Rather than viewing performance through a series of disconnected initiatives, organisations gain a clearer understanding of how the actuarial function operates as a whole.

Creating Visibility Over Performance

In many organisations, actuarial performance is assessed indirectly through delivery timelines, model outputs or individual projects. While these provide useful indicators, they do not always offer a clear view of how effectively the actuarial function is operating as a whole.

This is where the six enablers become particularly valuable. Rather than focusing on isolated metrics or individual initiatives, they provide a structured way of understanding the factors that collectively influence performance.

This perspective helps create greater visibility over how the function operates, where constraints exist and how different challenges relate to one another. Importantly, it shifts the conversation away from individual problems and towards overall capability.

The question then shifts from “What should we improve?” to a far more valuable one: “Where are the constraints that are having the greatest impact on performance?”

This subtle shift in perspective can fundamentally change how improvement is approached. Instead of responding to issues as they emerge, organisations are better positioned to understand the function as a whole, prioritise improvement more effectively and focus effort where it will create the greatest value.

From Understanding to Improvement

The six enablers do not provide a solution in themselves. Their value lies in providing a structured way of understanding performance.

By viewing the actuarial function through a consistent set of interconnected enablers, organisations gain greater visibility over how performance is created, where constraints exist and how improvement efforts relate to one another. This changes the conversation.

Rather than asking which individual component should be improved next, actuarial leaders can begin to consider how improvement should be prioritised across the function as a whole.

That shift in perspective is often the first step towards more coordinated, sustainable and effective performance improvement.

This article forms part of MBE Consulting’s “Missing Link in Actuarial Modernisation” series, exploring why improvement efforts stall, and what it takes to achieve meaningful, system-wide performance improvement.

The post The Six Enablers appeared first on MBE.

Introduction

High-performing actuarial functions are not defined by the absence of complexity.

They operate within the same constraints as other functions: legacy systems, regulatory pressure, increasing data demands, evolving business expectations and ongoing pressure to deliver more insight, more quickly.

The difference lies in how they respond.

Having seen many actuarial functions, certain patterns consistently emerge. Stronger functions tend to have a clearer understanding of how work is delivered, where constraints exist and how improvement activity contributes to the performance of the function as a whole.

They are not necessarily investing more. They are usually operating with greater clarity, consistency and control.

They Understand How Work Actually Flows

Stronger actuarial functions have a clear view of how work moves through the function from end to end.

This goes beyond understanding individual tasks, systems or models. It means understanding how data enters the process, how it is transformed, how models are operated, how outputs are reviewed and how results are ultimately used by the business. That clarity matters.

It allows teams to identify where inefficiencies exist, where dependencies create risk and where improvement will have the greatest impact. It also helps avoid the common problem of improving one part of the process while leaving the wider operating environment unchanged.

In practical terms, high-performing functions understand not only what work is being done, but how that work flows across people, processes, data, systems and models.

They Reduce Operational Friction

A common feature of less effective environments is the amount of friction embedded in day-to-day delivery.

Manual workarounds become normal. Data preparation absorbs time. Processes depend on key individuals. Models require specialist knowledge to operate or explain. These issues may be manageable individually, but collectively they reduce efficiency and make the function harder to change.

Stronger functions are more deliberate in reducing this friction. Processes are designed with systems in mind. Data is structured to support how it will actually be used. Models are developed with consideration for operation, maintenance and explanation. Responsibilities are clearer, reducing reliance on informal knowledge or repeated intervention.

The result is not necessarily a simpler function, but a more coherent one.

They Approach Change Deliberately

All actuarial functions have to respond to immediate demands. Regulatory change, reporting deadlines, business requests and internal priorities all create pressure for action.

The difference is that stronger functions are better able to balance short-term response with longer-term improvement. They avoid layering new solutions onto existing structures without considering the wider impact. Instead, change is approached with a clearer understanding of how individual initiatives contribute to the evolution of the function.

This does not mean change is slower. In many cases, it becomes more effective because teams understand what they are changing, why it matters and how it affects the wider operating environment.

They Create Consistency Without Removing Judgement

Consistency is often an overlooked driver of actuarial performance.

Where approaches vary significantly across teams, products or reporting areas, complexity increases. Outputs become harder to compare, processes more difficult to manage and results more challenging to explain.

High-performing functions address this by establishing clearer standards across methodologies, processes and data usage, while still allowing appropriate judgement where it’s required.

This distinction is important. The aim is not to remove professional judgement from actuarial work. It is to create enough consistency that judgement can be applied transparently, rather than being embedded in undocumented differences between teams or processes.

They Connect Technical Work to Business Use

The purpose of the actuarial function is not only to produce accurate results. It is to support decision-making.

Stronger functions have a clearer connection between the work performed, the outputs produced and the value delivered to the business. This requires more than technical quality. It requires outputs that are timely, explainable and relevant to the decisions they support. It also requires actuarial teams to understand how their work is used beyond the function itself.

Better data, more usable models, clearer methodologies and more efficient processes all contribute to this. But the value comes from how these elements are brought together to support better business outcomes.

They Build for Sustainable Improvement

High-performing actuarial functions do not focus solely on solving today’s challenges. They build capability that supports longer-term performance.

A clearer operating environment provides greater transparency. Dependencies are better understood. Key risks are easier to identify. The impact of change can be assessed more effectively.

This allows actuarial leaders to move from managing issues as they arise towards actively shaping how the function evolves.

The Difference

High-performing actuarial functions are not simply better because they have stronger individual components.

They are stronger because they understand how those components work together in practice.

They have greater clarity over how work flows, where constraints exist and how improvement activity contributes to overall performance.

For many organisations, the challenge is not recognising what good looks like. It is establishing a practical way to move towards it.

This article forms part of MBE Consulting’s “Missing Link in Actuarial Modernisation” series, exploring why improvement efforts stall, and what it takes to achieve meaningful, system-wide performance improvement.

The post What High-Performing Functions Do Differently appeared first on MBE.

Introduction

Actuarial modernisation has become a strategic priority across the insurance industry. Increasing data volumes, evolving regulatory requirements and the growing demand for forward-looking insight have placed greater pressure on actuarial functions to improve how they operate.

In response, many organisations have invested significantly in modernisation initiatives. Systems have been upgraded, data platforms enhanced and models refined. These investments are often well considered and, in many cases, have delivered clear benefits within specific areas of the function.

However, a more fundamental question remains: why do these investments so rarely translate into sustained improvements in overall actuarial performance?

The issue is not simply whether organisations are investing enough. In many cases, they are. The more difficult question is whether investment is being directed towards the areas that will make the greatest difference to the performance of the actuarial function as a whole.

Significant Investment, Limited Performance Improvement

Investment in actuarial modernisation has accelerated significantly over recent years.

Organisations have invested in new modelling platforms, enhanced data capabilities, automation initiatives and increasingly sophisticated analytical tools. In many cases, these investments have delivered tangible benefits. Processes have become faster, reporting has become more automated and technical capabilities have improved.

Viewed individually, many of these initiatives can be considered successful. However, a recurring challenge remains. While investment often improves specific components of the actuarial function, it does not consistently translate into broader improvements in overall performance.

Actuarial teams frequently find themselves operating with better systems, more data and increasingly sophisticated models, yet still facing many of the same challenges. Manual workarounds persist. Processes remain difficult to navigate. Dependencies continue to constrain delivery. The function becomes more capable, but not necessarily more effective.

This creates an important distinction. The challenge is rarely the quality of the investment itself. More often, it’s the assumption that improving individual components will automatically improve the performance of the function as a whole.

In practice, this is rarely the case.

The Attraction of Visible Change

One reason investment does not always translate into improved performance is that organisations are naturally drawn towards visible forms of change.

New systems can be implemented. Data platforms can be enhanced. Models can be redesigned. These initiatives produce tangible outputs, clear milestones and demonstrable progress. They are often easier to define, fund and govern than broader operational change.

This is not surprising. Technology investments frequently have well-defined business cases, dedicated budgets and measurable deliverables. Progress can be tracked, benefits can be quantified and success can be clearly communicated.

The most significant constraints on actuarial performance, however, are not always found within technology or modelling capability. More often, they emerge through the interaction of people, processes, methodologies, systems, data and models.

Processes evolve over time with workarounds becoming embedded. Responsibilities become fragmented across teams. Data preparation activities become accepted as a part of normal operations. Individually, these issues may appear manageable. Collectively, they can have a significant impact on efficiency, responsiveness and overall performance.

Unlike technology investments, these challenges are often less visible. They do not sit within a single project. They are difficult to quantify, harder to prioritise and rarely addressed through a single initiative. As a result, organisations can find themselves investing heavily in areas that are easier to improve, while overlooking the underlying factors that continue to constrain the effectiveness of the function.

The challenge is not that these investments are wrong. The challenge is that they are rarely sufficient on their own.

Directing Investment Where It Matters Most

High-performing actuarial functions take a broader view of investment. Rather than focusing solely on individual initiatives, they consider how improvement efforts contribute to the effectiveness of the function as a whole.

This often means looking beyond technology alone. Investment is directed towards improving the way work is performed, reducing operational friction and strengthening the connections between different parts of the function. Processes are simplified. Roles and responsibilities are clarified. Methodologies are applied more consistently. Data is structured to support efficient decision-making rather than repeated preparation.

Importantly, these organisations recognise that sustainable improvement rarely comes from a single initiative. Instead, performance improves through a series of coordinated changes that collectively enhance the way the function operates.

This does not mean investing less in systems, models or data. These remain critical components of modern actuarial functions. The difference is that investment decisions are made with a clearer understanding of how each improvement contributes to overall performance, rather than the performance of an individual component.

As a result, improvement efforts become more targeted, more coordinated and ultimately more effective. The focus shifts from implementing change to improving outcomes.

A Better Direction

Investment remains essential to the future of the actuarial function.

New technologies, enhanced data capabilities and increasingly sophisticated models will continue to play a critical role in how actuarial teams operate and deliver value. However, investment alone does not determine performance.

The greatest improvements are often achieved not by investing more, but by investing with a clearer understanding of where the real constraints exist and how different improvements contribute to the effectiveness of the function as a whole.

The challenge is not directing investment towards visible opportunities for change. It is ensuring that investment is directed towards the areas that will have the greatest impact on overall actuarial performance.

This article forms part of MBE Consulting’s “Missing Link in Actuarial Modernisation” series, exploring why improvement efforts stall, and what it takes to achieve meaningful, system-wide performance improvement.

The post The Wrong Direction appeared first on MBE.

Introduction

While the challenges within individual areas of the actuarial function are often well understood, improving overall performance remains difficult.

This is rarely due to a lack of activity. In many insurance companies, significant effort and investment are already underway.

The difficulty lies in how improvement is approached.

Improvement in Isolation

Many initiatives are designed to address specific issues.

A new system is implemented to improve efficiency.
A model is enhanced to increase accuracy.
A data initiative is launched to improve quality and accessibility.

Each of these actions is logical and, in isolation, can deliver meaningful benefits.

However, these initiatives do not always translate into broader improvements in actuarial performance.

Uneven Change Across the Function

One reason for this is that change is often applied unevenly across the function.

Investment and focus may be concentrated in one area, while others remain relatively unchanged. As a result, the benefits of improvement are frequently constrained by what sits around them.

For example, systems may be upgraded without the necessary and corresponding changes to processes. Data may be improved without addressing how it is used within models or workflows. New methodologies may be introduced without ensuring they are consistently applied.

In these situations, individual components may improve, but overall performance does not.

Misalignment begins to emerge. Enhanced systems still rely on manual processes. Improved data is not fully integrated into existing models. Stronger methodologies prove difficult to operationalise. Capability within teams increases, but opportunities to apply it effectively remain limited.

As a result, improvements in one area can be diluted, or even negated, by limitations in another.

The Impact of Reactive Change

A further challenge is that many initiatives are driven by immediate needs.

Regulatory change, reporting deadlines or business demands often require targeted solutions within tight timeframes. While these responses are necessary, they can lead to a pattern of reactive change.

Solutions are implemented to address specific issues, without fully considering their impact on the wider system.

Over time, this contributes to an increasingly complex environment. Each new initiative adds to the structure of the function, but not always in a coordinated way. Dependencies increase, workarounds become embedded and the overall landscape becomes more difficult to navigate.

The Absence of a Clear Baseline

Another common issue is the absence of a clear baseline.

Without a structured view of current capability, it becomes difficult to determine where the most significant constraints lie, which improvements will have the greatest impact and how progress should be measured over time

This is often reinforced by the absence of a consistent approach to measuring performance.

Progress may be visible within individual areas, but far less clear at the level of the function as a whole. In the absence of this broader view, improvement efforts are often guided by perception, experience or urgency, rather than by a consistent understanding of how the function operates overall.

A Familiar Pattern – and What It Points To

These factors combine to create a familiar pattern.

Change is continuous. Effort is significant. New initiatives are introduced. Existing processes are refined. Additional capability is developed across systems, data and models.

But the resulting improvement in performance is uneven and, in many cases, difficult to sustain.

What this reveals is not a lack of activity, but a lack of coordination.

Addressing this requires a more structured and coordinated approach to improvement. An approach that considers not only what needs to change, but how those changes interact, and how they contribute to performance across the function as a whole.

Closing Thought

Improvement does not stall because of a lack of effort.

In most actuarial functions, activity is ongoing. Initiatives are delivered, capability is developed and change is continuous.

It stalls because that effort is not aligned.

Without a clear view of how different improvements interact, progress remains uneven, and the overall performance of the function is difficult to move forward in a sustained way.

This article forms part of a broader paper on The Missing Link in Actuarial Modernisation, which sets out a structured view of actuarial performance and how different components of the function interact.

The post Why Most Improvement Efforts Stall appeared first on MBE.