All Actuaries Summit 2026 Papers
Read the papers that have been submitted for this year's Summit.
Lessons from the Courtroom: Navigating Trustee Duties and Insurance Complexities
Authors Jeff Humphreys (CHR Consulting) and Karen Lau (Mercer Super)
Abstract
Since the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry in 2018 (the Royal Commission), litigation in the superannuation sector, including insurance in superannuation, has intensified. Recent and ongoing class actions and regulator enforcement now target practices that may breach core trustee obligations. This paper explores the landscape:
• What were the issues facing recent legal proceedings?
• Did governance, systems, or behaviour fail?
• What lessons emerge from these failures?
• What duties do trustees (and insurers) owe to members in offering group insurance, especially in areas where consent or disclosure is ambiguous?
• Beyond the headline cases, are there latent risks still under the radar?
• What role do actuaries have in reducing legal, operational, and reputational risks?
• Looking forward, what might ‘good practice’ look like?
Stripping unnecessary complexity from the retirement process
Author Anthony Asher
Abstract
This paper examines how complexity in the processes followed in retirement can be reduced without significantly compromising equity or fiscal sustainability. The processes involve navigating tax, age pension and home and aged care benefits and means tests. It argues that the current system imposes unnecessary administrative and behavioural burdens on retirees, advisers, industry and government agencies. Telephone waiting times on calls to Centrelink can be over an hour on most days.
Reform cannot be considered without addressing the egregious inequities in the treatment of renters against homeowners; the impact of the asset test that penalises saving; effective marginal tax rates of over 100% for age pensioners still working and the 100,000 people on waiting lists for home care packages.
The paper proposes a series of reforms guided by principles of simplicity, materiality, and lifecycle equity. Key recommendations include integrating income and asset tests through a unified framework, abolishing seventeen minor concessions and allowances, harmonising tax and welfare systems and home and aged care settings. A more radical alternative—abolishing both means testing and superannuation tax concessions—is also considered, drawing on international comparisons such as New Zealand. While acknowledging transitional and political challenges.
The paper concludes that meaningful simplification is both feasible and necessary to improve efficiency, fairness, and decision-making in retirement.
Navigating the Electric Vehicle Environment: E Bikes and E-Scooters
Author Lachlan Clark
Abstract
The increasing adoption of Personal Mobility Devices (PMDs) across Australia is leading to a diverse and everchanging landscape. PMDs are small, electrically powered devices, designed for a singular user to travel short distances. They typically include devices such as electric scooters (e-scooters), electric bikes (e-bikes) and electric skateboards (eskateboards).
More broadly, their increased adoption represents a sustainable, affordable and efficient alternative to traditional transportation methods; promoting healthier lifestyles whilst creating efficient solutions to common infrastructure issues including traffic congestion and the ‘last mile’ problem. However, the rapid adoption of PDMs has also introduced various new and distinct challenges for insurers, regulators, policymakers and urban planners.
Different licensing, road and vehicle design laws have created significant different experiences between Australian states and territories. PMDs can exhibit vastly different risk characteristics to other forms of conventional transport due to their unconventional design, rider behaviour and vehicle usage. Road infrastructure and urban environments, state-based legislation, interactions with pedestrians and other road users and the implementation of telematics all have the propensity to further influence risk profiles, thereby creating further complexities for insurers.
With a focus on e-bikes and e-scooters; this paper considers the academic literature to look at their different risks characteristics and contributing factors - which includes the regulation on licensing, PMD designs, road use (helmets and speed limits) and road infrastructure. A review of Australian injury data provides a better understanding of their risks for physical injury and fire damage. Lastly, the paper illustrates the insurance gap that presently exists for Australian PMD users given the current insurance regulations and design of the Compulsory Third Party and Workers Compensation schemes.
Beyond our means? A distributional approach to assessing actuarial reserving in general insurance
Read the paper
Download the resource
Authors Calise Liu, Andrew Song, Alan Xian
Abstract
Fundamentally, actuaries are asked to put a number to uncertain future events. We spend a lot of effort quantifying this uncertainty to provide a range of reasonable outcomes. As part of standard valuation work, actuaries will present a central estimate alongside a metric for uncertainty, for example, a quantile of the loss distribution in the form of a risk margin or a risk adjustment.
A core part of the actuarial control cycle is the monitoring and assessment of the models against experience. In practice, this is focussed on the central estimate, with less emphasis placed on the distribution. “Actual vs Expected” analysis assesses the percentage variation against the central estimate, with a layer of actuarial judgement applied to justify the materiality of any differences (e.g. based on portfolio maturity, size of book, known events). This is likely sufficient in many cases but in situations where the modelling decisions/results are less clear cut, we wanted to explore the ability to leverage the distributional thinking already performed at past valuations to provide a better data-driven assessment to support judgement.
It turns out that this isn't as simple as just applying the overall risk margin to the central estimate and backing out some quantiles. This is because:
• The central estimate models are almost always different from the risk margin models
• Risk margins include consideration of systemic risk which may not be in the historical data
• Adjustments in central estimate models (e.g. blending or accounting for data quality/scarcity) means analytical expressions can be hard to derive explicitly
• Allocation - how do you allocate a risk margin calculated for the full projection period, to just the next reporting period?
In this paper, we describe an approach to calculate a distribution for each cell in the projection (or the combination of cells you are interested in). Most importantly, the additional time and effort required to do this analysis is minimal; much of it would already be part of your standard valuation pipeline.
Having access to these distributions opens up a number of areas where "distributional hindsight analysis" can be useful to help support actuarial decision making by adding statistical rigour (because you can't do statistical tests on X% above the mean). Examples of such decisions include:
1. When should we change our roll-forward modelling assumptions?
2. When should we revise our risk margins?
3. How outlying was the new event and does it require a post-balance date adjustment?
4. Should I analyse my new portfolio separately or should I just combine it with my current book?
Beyond pairwise correlation: capturing nonlinear and higher-order dependence with distance statistics
Authors Benjamin Avanzi, Guillaume Boglioni Beaulieu, Pierre Lafaye de Micheaux, Ho Ming Lee, Bernard Wong and Rui Zhou
Abstract
Measuring and modelling dependence between risks is crucial in many actuarial applications, such as when assessing diversification benefits or setting capital requirements. Current industry practice relies heavily on Pearson’s correlation coefficient, despite well-known limitations. In particular, it captures only linear association, is restricted to pairwise relationships, and does not naturally extend to multivariate settings involving multiple random variables or random vectors (such as when one-hot encoding categorical variables). Sole reliance on Pearson’s linear correlation may fail to detect important nonlinear, higher-order, and mutual dependence structures.
In this paper, we discuss and illustrate several distance-based dependence statistics which do not suffer from the same limitations as correlation, and discuss how they can be used in actuarial applications. In the bivariate setting, we consider the Hellinger correlation (Geenens & Lafayede Micheaux, 2022)as a tool for measuring dependence between two continuous univariate random variables, and distance covariance (Székely et al., 2007) as a tool for detecting and testing dependence, especially when random vectors are involved. We then discuss extensions to higher-dimensional settings, including joint distance covariance for assessing mutual dependence across multiple random variables (or vectors), and the auto-distance correlation function for time series applications such as forecasting mortality rates over time.
Throughout the paper, we illustrate the use of these tools in actuarial contexts and we direct the reader to available software implementations. Overall, the paper aims to provide actuaries with a practical introduction to distance-based dependence statistics and to show how they can complement classical correlation-based tools in actuarial workflows.
Strategic Climate Resilience in First Nations Social Housing: Actuarial Tools for Economic and Social Impact
Authors Sharanjit Paddam, Olivia Brodhurst, Kate Cotter, Evelyn Yong, Ondrej Bures, Portia Elliott
Abstract
With climate change amplifying the frequency and severity of natural hazards—NSW recording a record 35 disaster declarations in FY 2023–24, more than any previous year—the NSW Government Aboriginal Housing Office (AHO) recognised the urgent need to enhance the resilience of its housing portfolio. In collaboration with Finity and the Resilient Building Council (RBC), the project combined dwelling-level resilience ratings, peril modelling, and wellbeinginformed cost–benefit analysis to guide strategic investment in housing upgrades. This initiative is a vital step toward safeguarding First Nations (Aboriginal and Torres Strait Islander) housing stock against future climate risks.
Surveys of representative properties identified resilience to flood, bushfire, storm and heat, informing targeted retrofit pathways such as re-roofing, window replacement and passive cooling. The results show that the investment case varies by peril: flood adaptation is most compelling where raising a home is feasible (within realistic limits), bushfire resilience where exposure and improvability align, and storm resilience when tenant wellbeing benefits are considered. Heat resilience emerged as a portfolio-wide priority due to substantial wellbeing gains, even where insured losses are nil.
By quantifying both asset protection and tenant wellbeing outcomes, the framework demonstrates why public-sector resilience decisions should extend beyond insured losses alone. The assessment provides AHO with a robust, socially meaningful basis for undertaking property resilience upgrades, demonstrating duty of care, and prioritising investment under a changing climate. More broadly, it offers a scalable blueprint for climate adaptation in public and First Nations housing across Australia.
The challenge for actuaries lies in developing funding frameworks for resilience programs and extending this work to include commercial buildings and infrastructure.
From Data Taker to Data Generator: Introduction to Causal Inference and Randomised Control Trial Playbook
Authors Laura Zhao and Fei Huang
Abstract
As actuaries, we rely on observational data to build models, understand historical patterns and tell stories. Those datasets show us what has happened, but rarely tell us why things happen. The why matters because it pinpoints to action and consequence, it enables decision makers to understand causal effects of changes rather than being blinded by correlations, and it provides robust policy evaluations.
Understanding the why, or the cause and effect is challenging, there are techniques and methods to illuminate the hidden causal relationship, randomised controlled trial (RCT) is widely considered as the gold standard for uncovering causality, as it directly addresses the problem of endogeneity by ensuring treatment assignment is exogenous, while other causal methods such as differences-in-differences, regression discontinuity, or instrumental variables arguably suffer from the endogeneity problem to various degrees.
RCT, as a method of field experiment, generates meaningful data, sharper insights and business guidance beyond what observational data can provide. It adds to the actuary's toolkit and redefines our understanding of why things happen. This paper will introduce causal inference, the endogeneity problem, an RCT playbook with practical reflections, and a case study, which hopefully will inspire more actuaries to explore and embrace the spirit of experimentation.
An Interpretable Deep Learning Model for General Insurance Pricing
Authors Patrick J. Laub, Tu Pho, Bernard Wong
Abstract
The rapid advancement of machine learning has provided an opportunity to transform the modeling techniques in actuarial analytics. Novel machine learning methods, especially deep learning, have demonstrated versatile modeling capability and superior predictive performance compared to traditional actuarial approaches such as Generalised Linear Models. However, the widespread adoption of deep learning techniques in the insurance industry is often hindered by the lack of model interpretability, as the intricacies of their inner workings remain obscured behind the complex model architecture. This lack of interpretability is further complicated by the absence of a generally accepted definition of what an interpretable model is. There are also various practical requirements, such as smoothness and monotonicity, that a pricing model in general insurance should possess in addition to being interpretable.
This paper introduces the Actuarial Neural Additive Model, an inherently interpretable deep learning model for general insurance pricing that offers fully transparent and interpretable results while retaining the strong predictive power of neural networks. This model assigns a dedicated neural network (or subnetwork) to each individual covariate and pairwise interaction term to independently learn its impact on the modeled output while implementing various architectural constraints to allow for essential interpretability (e.g. sparsity) and practical requirements (e.g. smoothness, monotonicity) in insurance applications. The development of our model is grounded in a solid foundation, where we establish a concrete definition of interpretability within the insurance context, complemented by a rigorous mathematical framework. Comparisons in terms of prediction accuracy are made with traditional actuarial and stateof-the-art machine learning methods using both synthetic and real insurance datasets. The results show that the proposed model outperforms other methods in most cases while offering complete transparency in its internal logic, underscoring the strong interpretability and predictive capability.
Towards Fairer Retirement Outcomes: Health Related Mortality Modelling
Authors Pramo Samarasinghe, Fei Huang, Francis KC Hui, Andrés M Villegas
Abstract
Accurate mortality prediction underpins actuarial practice, informing insurance pricing, retirement income planning, and product design. Building on recent evidence of socio-economic mortality differentials in Australia (Huang, Hui and Villegas, 2025), this study extends that framework by incorporating health-related variables for retirees and distilling them into a transparent Health Index. Using the ABS Personal Level Integrated Data Asset (PLIDA), linked to Medicare and Pharmaceutical Benefits records, we develop interpretable models that integrate the Health Index as an additional factor within the existing socio-economic model. By grouping individuals into risk-based cohorts, the approach mitigates adverse selection risks and preserves equity in risk pooling.
Exploratory analysis shows that mortality risk rises steadily with higher healthcare use, further increased by comorbidity and polypharmacy, but are partly offset by regular preventive care. Notably, painmedication exposure is associated with an approximately threefold higher observed mortality compared with non-exposed peers, which aligns with the rise in opioid-related deaths over the study window and may reflect addiction. Furthermore, preliminary modelling results demonstrate significant gains in accuracy and fairness, with direct implications for fairer retirement outcomes and improved management of longevity risk. To facilitate adoption, our proposed methodology translates complex models into a concise set of health-related questions potentially suitable for underwriting, bridging actuarial modelling, and health data analytics. This work complements existing socio-economic mortality research, and provides new insights into health-related mortality, offering a foundation for more accurate, transparent, and equitable retirement outcomes in Australia.
Fairness Testing for Insurance Pricing: A Statistical Inference Framework
Authors Fei Huang and Giles Hooker
Abstract
Ensuring fairness in insurance pricing has become a central concern for regulators, insurers, and the public. Existing approaches often rely on descriptive disparity measures or significance testing without explicit reference to fairness concepts or acceptable tolerances, leading to fragmented practices and ambiguous conclusions. We develop a unified framework that grounds fairness testing in classical statistical inference, providing clear definitions, reproducible methods, and auditable protocols.
Our framework advances fairness testing along four key dimensions. First, we formalise widely discussed fairness criteria as hypotheses on identifiable estimands, making fairness notions statistically precise. Second, we introduce decision rules that incorporate explicit tolerance thresholds, reflecting regulatory standards in practice. Third, we propose inference procedures that bridge actuarial practice with fairness guarantees. Fourth, we design a quote-audit protocol that specifies how to collect, test, and validate fairness claims in a manner that is transparent and replicable.
By combining inferential rigour with regulatory practicality, our approach delivers a coherent methodology that both regulators and insurers can implement. The framework integrates uncertainty quantification and audit design into a single pipeline, ensuring that fairness tests are interpretable, reproducible, and statistically robust. Beyond pricing, the principles extend to approval outcomes and process measures, providing a flexible foundation for responsible insurance analytics. The result is a regulator- and insurer-ready framework that aligns fairness testing with statistical guarantees and supports the broader goal of accountable and transparent insurance practices.
All is Fair in Love and War….but not in General Insurance
Author Jacqueline Reid
Abstract
There is a well-known quote suggesting that love and war are so important and overwhelming that people should be excused for acting in their own best interest. But general insurance is neither a battlefield nor a romance - it's a relationship built on trust, where fairness must prevail over selfinterest. In his October 2024 dialogue paper, “Fairness in Insurance”, Ian Laughlin challenged insurance Boards to elevate customer fairness as a strategic priority. This paper shifts the lens to explore the critical role general insurance actuaries can—and should—play in delivering fairer outcomes.
Laughlin recognised that Boards occupy a unique vantage point for ensuring fair customer treatment, and identified fairness as a "natural area of interest" for actuaries. But what are the obligations and rules of fairness which apply to general insurance actuaries? This paper examines what practitioners need to know about fairness and what they could, or should, be doing to achieve this objective in practice.
While Laughlin noted that Boards benefit from sitting "above the fray" with greater objectivity than management, this paper argues that general insurance actuaries are equally well positioned—if not better placed—to balance competing interests. Working in the front lines where pricing, underwriting, and claims decisions are made, general insurance actuaries can serve as vital arbiters of fairness, translating principle into practice where it matters most.
Making all the right noise - Using diffusion deep learning models to perform multivariate prediction for simulation and reserving
Authors Hugh Miller, Justin Sik Kwok Wong and Callum Sleigh
Abstract
As AI adoption accelerates, there is continued interest in adapting ideas to new contexts to see how deep learning models can be applied. For actuaries, there is an ongoing opportunity for new models to handle multivariate prediction problems where there are important dependencies between outputs. Examples include: claims reserving (estimating a suite of accident and development period combinations), pricing (where different risks are estimated and combined into a risk price), microsimulation (where many different characteristics are simulated over time in a consistent way) and DFA models (where different parameters will evolve over time to determine capital positions, with strong correlations across measures). Diffusion models are a common deep learning approach, particularly in image generation. Starting with random variation, a trained model will successively ‘de-noise’ a dataset until it resembles something coherent. In many cases they outperform other model structures, including in a small number of research papers testing applicability to tabular data. Our paper applies diffusion models to two key actuarial problems. First, we apply diffusion to microsimulation, where interrelated outcomes must be jointly predicted. The diffusion setup significantly outperforms the GAN and VAE model approaches explored in our 2025 summit paper (Gains ratio on a discriminator model of 28%, compared to about 70% for prior models), at the expense of speed. Second, we reformulate reserving using actuarial triangles as a deep learning image completion problem and obtain promising results on a database of historical data. Results are reasonable, although some tail misfit is evident – comparable with results seen elsewhere in the literature. The models represent an important contribution, with the potential to substantially simplify the training of complex systems, as well as augmenting standard actuarial practice. To the authors’ knowledge, this is the first time such diffusion models have been applied in an actuarial context.
Reputation Risk as a Quantitative Actuarial Practice
Author Colin Priest
Abstract
Reputation risk has historically resisted quantitative actuarial analysis because relevant data are sparse, stakeholder responses are strategic, and causal pathways to nancial loss are indirect. This paper demonstrates how adversarial risk analysis, generative AI digital twins, and Bayesian estimation can make it tractable, using the Qantas 2023 AGM governance crisis as a case study. The framework correctly identi es the modal outcome at four of ve decision nodes and nds that commissioning an independent governance review dominates all alternative Board strategies across all plausible parameter values. The aim is not to claim perfect prediction of unprecedented events, but to provide a replicable actuarial work ow for analysing reputation-driven strategic risk in data-poor settings. All code and tools will be released as open source.
Applying 2025 Nobel Economic Insights to Actuarial Practice in Health Insurance
Read the paper
Author Joy Liu
Abstract While technological disruption often dominates discussions of innovation, sustainable growth in healthcare fundamentally relies on the institutional mechanisms that facilitate the adoption of new knowledge. This paper applies the groundbreaking insights of the 2025 Nobel Prize in Economic Sciences, awarded to Joel Mokyr, Philippe Aghion, and Peter Howitt, to the Australian Private Health Insurance (PHI) sector. Innovation is reframed not merely as novelty, but as the reduction of friction between clinical knowledge and systemic implementation. Growth is conceptualised as an endogenous process, driven by internal institutional mechanisms rather than external technological shocks. In PHI, these mechanisms operate through pricing, benefit design, and risk pooling.
Using preventative care programs as a case study, the paper illustrates how incremental, nondrastic innovations are absorbed and scaled within the PHI system. Actuaries are identified as key mediators of this process, managing uncertainty, aligning incentives, and preserving the stability required for innovations to mature. A simulation model quantifies these dynamics to demonstrate how marginal institutional improvements cumulate into significant long-term gains. The model also shows how actuarial decisions determine whether an innovation contributes to sustainable growth or systemic strain.
By bridging economic theory with practical modelling, the paper provides a coherent framework for analysing institutional adoption. It positions actuaries not as passive responders to innovation, but as active catalysts who enable measurable and durable improvements in healthcare systems.
Aged Care Financing in Australia: Individual, Government and Aged Care Provider Perspectives
Author Michael Sherris
Abstract
Aged care financing in Australia presents a complex interplay of considerations across individuals, government policy, and aged care providers. For individuals, the financial risks and costs associated with aged care in later life require careful planning and integration with retirement strategies. Financing strategies should integrate superannuation savings and home equity to fund care needs, with growing importance placed on understanding the timing, affordability, and accessibility of aged care services. As longevity increases, so too does the need for sustainable personal financing models that can accommodate retirement income and both residential and in-home care options. From a government perspective, aged care reform is underway with the introduction of a new Aged Care Act and a shift in policy that places greater financial responsibility on individuals, especially in the Support at Home (SAH) program. This marks a departure from the Aged Care Royal Commission’s recommendation for a dedicated aged care levy, instead favouring increased co-contributions. These changes have significant budgetary implications and highlight a contrast with the National Disability Insurance Scheme (NDIS), which operates under a publicly funded, entitlement-based model. Aged care providers must navigate evolving regulatory and financial landscapes, including prudential standards that affect liquidity, investment practices, and operational margins. The aged care sector faces a wide range of financial sustainability pressures, including rising wages, regulatory compliance demands, and tightening margins. At the same time, with the new Aged Care Act prudential rules, the potential phase-out of refundable accommodation deposits (RADs), and changes to Support-at-Home (SAH) pricing and co-payments, providers face challenges in funding future capital requirements, maintaining liquidity, and supporting growth. Providers need to respond with workforce innovation and other strategic f inancing decisions while balancing care obligations with financial sustainability. Providers, whether for-profit, not-for-profit, or government-operated, face varying pressures in adapting to increased future demand and maintaining service quality. This paper explores these intersecting perspectives to illuminate the challenges and opportunities in aged care financing, offering insights into how actuaries can contribute to shaping resilient and equitable solutions for Australia’s ageing population.
The Failure Metric
Read the paper
Author Ilan Leas
This paper explores lessons from life insurance product failures.
Through ten global case studies, three key conclusions were drawn:
1. Failure in life insurance is slow and cumulative, but with a journey that is predictable. It is typically the result of small decisions compounding, driven by pushing boundaries (stretching guarantees, nudging benefits, assuming behaviours and chasing competitiveness) in an attempt to address a genuine consumer need.
2. Most failures are behavioural, structural and human. Actuaries tend to focus on assumptions and the models, but we will inevitably get these wrong. Whilst there are specific micro lessons to take away from each case study, the failures emerge where misaligned incentives creep in, we forget insurance 101 rules, we ignore distribution realities and we can’t foresee future systems or how outcomes will be judged years later.
3. There are no silver bullets, perfect designs or foolproof rules we can put in place that stop failure or will stop us repeating history. But we can learn from other industries more used to failure, and we can ask sharper, more uncomfortable questions to help decision makers better assess the risks.
Ten questions were developed from the lessons and are proposed to encourage longer term thinking by decision makers when designing products. They are:
1. If volatility is inevitable, how have we incorporated the impact into the design?
2. What risks do our customers think they have transferred?
3. How have we set up this product for the successor team/s that will be managing this in the future?
4. What experiments have we run to test behaviours, both at sale and over the life of the product?
5. What specific lessons from our own past product failures or from failures we have witnessed elsewhere are explicitly embedded in the design? 6. How is this product going to be a no brainer for our customers?
7. How have we built in positive surprises to the design?
8. If we assume some information is being hidden from us in our testing, what could it be and how are we incorporating this into the design?
9. What are all the incremental changes to the product since launch and how does this cumulative upgraded design compare to the original in terms of risks?
10. If we fast forward to the balance sheet 10 years from now, where is it sensitive to changes in assumptions?
The goal can never be to eliminate failure which would be unrealistic in a particularly longterm business defined by uncertainty. But to change its shape. Smaller. Earlier. More transparent. The kind that teaches quickly, still protects customers, and ultimately strengthens the system. Few industries have the same depth of experience, the same long-term datasets and cashflows, or the same ability to observe how decisions play out over decades. This is an asset and is a source of competitive advantage if we learn from it.
Sequencing Risk and Asset Allocation
Author Colin R. Grenfell FIA, FIAA, FASFA
Abstract
Driving a car downhill requires different skills from those required to drive the car uphill.
Similarly, to secure optimum superannuation member benefits in the decumulation stage, different asset allocation strategies are required from those which apply in the accumulation stage. Why? This is primarily due to sequencing risk.
Many authors have attempted to define sequencing risk. The definitions are usually reasonably consistent with the definition in this paper. Some have illustrated it with simplified examples, but few, if any, have quantified it and then measured it based on historical data.
This research compares the sequencing risk faced by a retiree or an annuitant invested in a balanced portfolio, with that resulting from investing in:
- Australian shares,
- international shares or
- a combination of Australian shares and cash.
Comparisons are also included for Australian direct and listed property, semi-government bonds and inflation-linked government bonds. In the final section results are presented for ‘real’ sequencing risk allowing for price inflation.
The comparisons use both the Austmod historical data and simulated scenarios. The comparisons reveal that Australian shares combined with cash, offers better sequencing risk protection than traditional balanced portfolios.
This paper and presentation are an update and extension of an Actuaries Digital article with the same title which was published 30 April 2025.
Subsequently, that article was published on LinkedIn and since then it has experienced sustained and continuing interest from professional readers.
