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.

Why Focusing on Individual Components Falls Short

Actuarial performance is often assessed by looking at individual parts of the function.

Systems are reviewed for efficiency. Models for accuracy. Data for quality. Processes for timeliness.

Each of these matters. However, assessed in isolation, they only tell part of the story.

When Strong Components Don’t Deliver Strong Outcomes

Improving individual components doesn’t necessarily improve overall performance.

A model may be technically robust, yet difficult to operate within existing processes.
A system may be powerful, but misaligned with how teams actually work.
Data may be available, but not structured in a way that supports efficient use.

In each case, the component performs as expected, yet the effectiveness of the function remains constrained.

Performance Is Defined by Interaction

What becomes clear is that performance is not simply the sum of its parts. It is shaped by how those parts interact.

Where alignment is lacking, friction emerge:

• Processes are adapted to accommodate system limitations
• Workarounds are developed to compensate for data gaps
• Additional controls are introduced to manage uncertainty

Over time, these adjustments become embedded.

What begins as a workaround becomes standard practice, increasing complexity and making underlying issues harder to identify.

Why Traditional Reviews Miss the Problem

When components are reviewed individually, they can appear adequate, or even strong.

It is only when viewed collectively that the disconnects become visible.

This shifts the focus away from individual capabilities and towards how the function operates as a whole.

A More Integrated View of Actuarial Performance

Improving actuarial performance requires a different lens.

Not one focused on optimising individual components, but one that considers how the function operates as an interconnected system.

Only then is it possible to identify where misalignment exists, and how improvement can be approached in a more coordinated and effective 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 A Different Lens on Actuarial Performance appeared first on MBE.

Where Complexity Comes From

In many organisations, systems are implemented independently of how work is actually performed.

This creates gaps between components, which are filled with manual workarounds.

Processes then adapt to accommodate these limitations. Additional steps are introduced to bridge systems, manage data issues or support model execution. Over time, these adjustments become embedded.

What begins as a workaround gradually becomes standard practice, increasing complexity and reducing efficiency.

The Hidden Cost of Data

Data presents a similar challenge.

Data is often available, but not structured or integrated in a way that supports models, processes or decision-making.

As a result, a significant proportion of actuarial effort is spent extracting, reconciling and transforming data before it can be used.

These activities are frequently embedded within processes, making them difficult to isolate and even harder to improve.

When Capability Increases Complexity

Models add further complexity.

Technically robust models can become difficult to operate, maintain or explain within existing workflows.

As functionality increases, so does operational complexity, placing greater demands on data, processes and the people required to manage them.

Inconsistent Ways of Working

At the same time, ways of working evolve inconsistently.

Teams, methodologies and processes develop in different directions, reducing clarity, increasing variation and limiting overall performance.

Friction Between Components

While individual components may appear to function adequately in isolation, the interaction between them introduces friction.

• Processes compensate for system limitations
• Data requires repeated preparation
• Models become harder to operate within existing workflows

This friction increases effort, extends timelines and reduces the ability of the function to respond effectively to new demands.

A System-Level Perspective

Understanding actuarial performance requires looking beyond individual components and focusing on how they interact.

Many of these challenges are addressed in isolation. But the underlying issue is structural.

A more complete view requires seeing the actuarial function as a connected system, where complexity is shaped not by individual elements, but by how they come together in practice.

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 Actuarial Functions Aren’t Broken – They’re Complex appeared first on MBE.

Why Investment in Actuarial Capability Does Not Always Improve Performance

Despite sustained investment in systems, data and modelling capability, many organisations continue to struggle to achieve meaningful improvements in overall actuarial performance.

These initiatives often deliver localised gains. However, they do not consistently translate into broader, sustained improvements in how the actuarial function operates.

This is not a question of capability. It is a question of approach.

The Problem with Isolated Improvement

In many organisations, improvement efforts are delivered as a series of individual initiatives, rather than as part of a coordinated whole.

Systems, data and models are developed or enhanced independently. While each initiative may be valid in isolation, the overall impact on performance remains limited.

Improvements in one area are frequently constrained by limitations in another.

• Systems may be upgraded, but processes remain unchanged
• Data is improved, but not structured for effective use
• Models become more sophisticated, but harder to operate

As a result, individual components continue to function, but overall performance does not reflect the level of investment.

Performance is a System, Not a Set of Components

At its core, actuarial performance is shaped by how people, processes, methodologies, systems, data and models work together.

Where these elements are not aligned, inefficiencies become embedded.

• Processes compensate for system limitations
• Data requires repeated preparation
• Models become difficult to operate or explain

Over time, this leads to increasing complexity and reduced responsiveness.

A Different Way of Thinking About Improvement

Improving actuarial performance requires a shift in perspective, from optimising individual components to understanding how the function operates as a whole.

The challenge is not simply identifying where improvements can be made, but understanding how those improvements interact and where misalignment is constraining performance.

This requires a closer look at how actuarial functions have evolved, and where complexity has become embedded.

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 Actuarial Transformation Keeps Failing appeared first on MBE.

Bonus rate setting is one of the most critical yet time-consuming tasks for life insurers. Traditionally reliant on expert judgment and manual iteration, it consumes valuable actuarial time and introduces inconsistency. By applying automation, this process can be transformed, delivering faster, fairer and more transparent outcomes.

Actuarial and financial models are often highly complex, producing outputs calculated to the tenth decimal place and projecting results nearly a century into the future. Yet, despite this sophistication, some critical decisions, such as determining appropriate bonus rates, still rely heavily on manual judgment. This process is typically iterative, resource-intensive and time-consuming.

In an era where artificial intelligence and automation are reshaping expectations of efficiency, there is an opportunity to enhance existing models to calculate bonus rates automatically. Drawing directly on the existing inputs and calculations in these models to set bonus rates not only reduces manual effort but also unlocks significant time and resources for higher-value analysis and decision-making.

Where Bonus Rates Are Used

Understanding where bonus rates fit within the wider insurance framework is essential before exploring how automation can improve the process.

Bonus rates play a central role in the products offered by mutual insurers and other participating life funds. In these structures, policyholders are not only customers but also share in the profits generated by the insurer. Instead of distributing all earnings directly as cash, insurers often declare bonuses that are added to the policy benefits.

These bonuses serve several purposes:

• Sharing profits fairly – ensuring policyholders benefit from the performance of the fund.
• Smoothing outcomes – using discretion to stabilise returns over time, avoiding sharp swings that might occur if profits were passed on directly each year.
• Strengthening engagement – reinforcing the value proposition of participating policies by demonstrating tangible returns.

Since bonuses directly affect both policyholder value and the insurer’s financial stability, setting the appropriate rates is a critical decision. It requires balancing fairness to policyholders, prudence for the insurer and consistency with long-term objectives, which is why it has traditionally been left to expert judgment.

How an Automated Bonus Solver Works

At its core, the automated bonus solver replaces manual judgment with a structured algorithm that finds the right rate automatically. An automated bonus solver allows insurers to formalise and streamline the process of setting bonus rates. The user first defines the key parameters and the target outcome that the bonus rate should achieve, typically a well-defined solvency threshold.

The solver then applies an iterative process:

1. Initial projection using a user-defined bonus rate – In the first iteration, the assets and liabilities are calculated using a user-defined bonus rate, typically the previous year’s declared bonus rate. This provides a starting point for the solver to assess the financial position under current conditions.
2. Check sufficiency – The solver then tests whether the available funds are adequate to meet the defined solvency target.
3. Borrowing logic – If permissible, the solver can draw on other available funds to close any shortfall.
4. Estimate revised bonus rate – Finally, the solver uses interpolation techniques to estimate a bonus rate that would bring the financial position into balance. This estimate is based on the relationship between the current financial position (from the sufficiency check) and the current bonus rate used in the liabilities calculation.

Once a revised bonus rate has been estimated, the solver re-runs the projection, checks for sufficiency and repeats the process. This continues until the difference between the required and actual outcomes is within an acceptable tolerance, ensuring convergence to the correct bonus rate.

This transforms what was once a manual, trial-and-error exercise into a systematic, repeatable and fully traceable process embedded directly in the model.

When the relationship between the bonus rate and the financial position is approximately linear, the solver will typically converge within 2-3 iterations, as the interpolation step provides an accurate estimate of the required adjustment. In cases where the relationship is non-linear, more advanced mathematical techniques are applied to accelerate convergence. Algorithms such as the Newton–Raphson method, which use first and second derivatives to refine estimates, are well suited to this problem, allowing the solver to reach the correct bonus rate efficiently, even in complex, non-linear settings.

Dynamic Projections

While the solver described above focuses on determining an appropriate bonus rate at a single valuation date, actuarial teams are often equally interested in how these rates may evolve over time. This longer-term view is referred to as the bonus path, the projected trajectory of future bonus rates that reflects expected experience on the insured portfolio and allows actuaries to assess the financial dynamics under a range of scenarios.

In many actuarial models, this is achieved through dynamic projections, where the bonus solver is applied sequentially at each future time step across the projection horizon. The process begins at the valuation date, where the model solves for the bonus rate required to meet defined conditions e.g. achieving target policyholder returns or maintaining solvency coverage. Once this rate is determined, the model rolls forward the assets and liabilities by one period, and the solver is applied again at the next time step. This cycle continues for each year of the projection horizon, which can extend to 100 years or more.

A critical consideration is that each forward step requires a full prospective valuation of liabilities. For a 100-year projection:

• The first valuation (at time 0) projects cash flows over 100 years.
• The second valuation (at time 1) projects 99 years.
• The third (at time 2) projects 98 years.
• …and so on, until the final step which involves only one year.

The total computational effort is therefore:

100 + 99 + 98 + … + 1 = 5,050

In other words, a 100-year projection involves 5,050 point-in-time calculations. When multiple scenarios or sensitivities are required, for example, under different economic or demographic assumptions, the computational load multiplies rapidly, creating a significant performance bottleneck.

Solving the Time Vector at Valuation

To overcome the computational burden of dynamic projections, an alternative approach i.e. the time vector method, replaces the sequential year-by-year process with a single, multi-period solution determined at the valuation date.

Instead of solving for bonus rates one year at a time, the actuary defines the entire set of future bonus rates as a time vector, which represents the expected trajectory of bonus rates over the full projection horizon. This vector is then determined through an iterative process carried out entirely at the valuation date.

The process begins with an initial guess for the future bonus rates. Assets and liabilities are then projected once over the full 100-year horizon, and the results are tested against the required conditions at each future time step e.g. solvency targets or policyholder return thresholds. The bonus rate vector is subsequently adjusted, and the process is repeated until the conditions are simultaneously satisfied across all future years.

Since each iteration involves only one full 100-year projection, the total computational effort is dramatically reduced. Assuming convergence within five iterations, typical in practice, the process involves approximately 500 calculations, compared to the 5,050 required under the dynamic projection approach.

This represents an order-of-magnitude improvement in efficiency while preserving accuracy and internal consistency. The time vector method also simplifies scenario testing and reduces operational complexity, as the entire bonus path is determined upfront rather than through hundreds of interdependent projection loops.

Importantly, while the dynamic projection functionality requires additional and often costly licences from actuarial software vendors to handle iterative projections, the time vector approach can typically be implemented using standard functionality within existing actuarial modelling platforms. This means it can sit alongside existing model run setups and operational processes without requiring extensive system changes or additional software expenditure, making it both a technically and commercially attractive solution.

Benefits of Using an Automated Bonus Solver

Automating bonus rate setting doesn’t just streamline actuarial work — it drives real business outcomes.

Although organisations often expect the development and implementation of an automated bonus solver to be costly and complex, experience has shown that the benefits typically outweigh the investment by a significant margin. Once embedded, the solver delivers substantial time and resource savings in each valuation cycle. As a result, the payback period is usually between 6 and 18 months, depending on the complexity of the bonus algorithm and the projection framework. In addition, the computational savings realised by implementing an automated bonus solver deliver tangible benefits not only to the actuarial function but also across the insurance firm.

Key benefits include:

• Improved accuracy and consistency – Since bonus rates are derived through a systematic, algorithmic process rather than repeated manual calculations, results are both more accurate and internally consistent. The time vector method also ensures that the entire future path of bonus rates evolves coherently across the projection horizon.
• Reduced operational risk – Automation removes the dependency on repeated manual judgment at each time step. The process becomes fully governed, reproducible and auditable.
• Scalability and flexibility – Whether applied to different funds, products or geographies, the automated solver can be scaled and adapted without exponentially increasing computational effort.
• Faster decision-making and scenario analysis – The significant reduction in model run-times enables actuaries to run multiple scenarios, sensitivities or stress tests with ease, supporting better strategic decision-making.
• Future-proofing actuarial models – By embedding bonus rate setting logic directly into existing models, insurers lay the groundwork for more advanced automation and AI-driven techniques in the future, enhancing both speed and strategic agility.

Conclusion

Despite the sophistication of actuarial and financial models, critical decisions like setting bonus rates have long relied on manual judgment and iterative processes. By embedding an automated bonus solver into existing models, insurers can transform this task from a time-consuming exercise into a streamlined, repeatable and transparent calculation.

Moreover, extending this approach to solve entire bonus paths as a time vector offers a powerful enhancement over traditional dynamic projections. It dramatically reduces the number of runs required, improves computational efficiency and provides actuaries with a robust framework to assess future bonus rate evolution under a range of scenarios.

The result is not only significant efficiency gains and reduced operational risk but also the opportunity for actuarial teams to shift their focus from routine tasks to higher-value activities that drive long-term strategy. In an industry where both efficiency and fairness are paramount, automation offers a practical way forward. This enables insurers to deliver consistent outcomes for policyholders while optimising the use of scarce actuarial resources.

Automation is redefining what’s possible in actuarial modelling. Firms that embed these techniques now not only gain efficiency today but also build the foundation for next-generation AI-enabled decision-making.

To explore how MBE Consulting can help integrate automated bonus solvers or broader model automation into your actuarial framework, contact our team.

The post Solving the Bonus Puzzle with Automation appeared first on MBE.