Abstract This paper overviews an overlapping generations financial cash flow valuation model that evaluates the financial sustainability of English NHS Trusts. It quantifies the financial sustainability constraints related to societal demographic shifts that affect their ability to maintain service delivery quality. The financial model computes a new long-term financial sustainability performance metric based on the notion of the Social Return on Investment (“SROI”). The measure evaluates the financial sustainability of two English acute care hospital foundation Trusts. Significant generational imbalances are identified for both sample NHS Trusts, both within different cohorts of existing generations, and between existing and future generational cohorts. Suggestions are provided for implementing these ideas and expanding actuaries’ expertise and skill sets.
Abstract Health insurers systematically underinvest in prevention. Programme costs are immediate but claims benefits accrue over years, and actuaries have lacked a formal mechanism to translate behavioural intervention evidence into pricing-ready claims adjustments. This paper introduces the Behavioural Adjustment Factor (BAF), a multiplicative actuarial framework that quantifies the claims impact of behavioural interventions by decomposing reach, efficacy, clinical translation, and durability into a single pricing-ready construct. To the best of the author’s knowledge, the BAF is the first actuarial framework to decompose behavioural intervention impact into condition-specific claims projections suitable for pricing and reserving. Drawing on randomised controlled trial evidence, the framework distinguishes interventions that generate reliable claims savings from those that do not. Programme architecture is shown to matter more than incentive magnitude, and the distinction between disease management and general lifestyle programmes emerges as the principal axis along which actuarial expectations should diverge. A worked hypertension example illustrates how the four BAF components combine to produce a defensible claims-adjustment range, and a sensitivity analysis highlights the dominant role of effect persistence. The framework provides confidence intervals, Monte Carlo integration for Solvency II capital modelling, a milestone-based pilot funding structure, and a clear pathway from international evidence to UK-calibrated practice.
Abstract Health and care (H&C) actuaries are well positioned to benefit from recent advances in data science as machine learning (ML) techniques have become increasingly transparent and accessible. The ML developments allow actuaries to detect complex nonlinear patterns and interactions that are difficult to capture using traditional generalised linear models (GLMs), without sacrificing the clarity and governance advantages that make GLMs central to actuarial practice. Using a large life insurance data set, we demonstrate and appraise three emerging hybrid approaches: interpretable boosted linear models, XGBoost-informed GLM and an interaction detection workflow. Our findings show that actuaries can improve modelling accuracy, measured by Poisson deviance, by integrating ML insights into traditional modelling techniques, achieving a practical balance of interpretability, expert judgement, and modern analytical innovation.
An increasing number of reports highlight the potential of machine learning (ML) methodologies over the conventional generalised linear model (GLM) for non-life insurance pricing. In parallel, national and international regulatory institutions are accentuating their focus on pricing fairness to quantify and mitigate algorithmic differences and discrimination. However, comprehensive studies that assess both pricing accuracy and fairness remain scarce. We propose a benchmark of the GLM against mainstream regularised linear models and tree-based ensemble models under two popular distribution modelling strategies (Poisson-gamma and Tweedie), with respect to key criteria including estimation bias, deviance, risk differentiation, competitiveness, loss ratios, discrimination and fairness. Pricing performance and fairness were assessed simultaneously on the same samples of premium estimates for GLM and ML models. The models were compared on two open-access motor insurance datasets, each with a different type of cover (fully comprehensive and third-party liability). While no single ML model outperformed across both pricing and discrimination metrics, the GLM significantly underperformed for most. The results indicate that ML may be considered a realistic and reasonable alternative to current practices. We advocate that benchmarking exercises for risk prediction models should be carried out to assess both pricing accuracy and fairness for any given portfolio.
This paper presents an actuarially oriented approach for estimating health state utility values using an enhanced EQ-5D-5L framework that incorporates demographic heterogeneity directly into a Generalised Linear Model (GLM). Using data from 148 patients with Stage IV non-small cell lung cancer (NSCLC) in South Africa, an inverse Gaussian GLM was fitted with demographic variables and EQ-5D-5L domain responses to explain variation in visual analogue scale (VAS) scores. Model selection relied on Akaike Information Criterion, Bayesian Information Criterion, and residual deviance, and extensive diagnostic checks confirmed good calibration, no overdispersion, and strong robustness under bootstrap validation. The final model identified age, gender, home language, and financial dependency as significant predictors of perceived health, demonstrating that utility values differ meaningfully across demographic groups. By generating subgroup-specific estimates rather than relying on uniform value sets, the framework supports more context-sensitive cost-effectiveness modelling and fairer resource allocation. Although developed in the South African NSCLC setting, the methodology is generalisable and offers actuaries and health economists a replicable tool for integrating population heterogeneity into Health Technology Assessment, pricing analysis, and value-based care.
Longevity risk significantly impacts the reserve adequacy ratio of annuity issuers, thereby reducing product profitability. Effectively managing this risk has thus become a priority for insurance companies. A natural hedging strategy, which involves balancing longevity risk through an optimised portfolio of life insurance and annuity products, offers a promising solution and has attracted considerable academic attention in recent years. In this study, we construct a realistic portfolio scenario comprising annuities and life insurance policies across various ages and genders. By applying Cholesky decomposition, we transform the portfolio into an uncorrelated linear model. Our objective function minimises the variance in portfolio value changes, allowing us to explore the impact of mortality on longevity risk mitigation through natural hedging. Using actuarial mathematics and the Bayesian MCMC algorithm, we analyse the factors influencing the hedging effectiveness of a portfolio with minimised variance. Empirical findings indicate that the optimal life-to-annuity ratio is influenced by multiple factors, including gender, age, projection period, and forecast horizon. Based on these findings, we recommend that insurance companies adjust their business structures and actively pursue product innovation to enhance longevity risk management.
Abstract The fast-moving field of data science is increasingly permeating into the health and care actuarial sciences. Given this context, the Institute and Faculty of Actuaries set out to form a “techniques in data science in health and care” working party. This working party was tasked with creating a framework for those actuaries working within the health and care domain that would assist them in determining which techniques are appropriate for a project. The framework presented here was developed through a combination of literature review and synthesis of expert opinion from experienced practitioners from diverse backgrounds. The framework offers a structured, itemised approach, serving as a checklist to ensure that all relevant analytics and decisions are considered and documented. Each itemised topic is covered by a summary providing guidance and relevant references for further reading. The checklist follows the natural workflow of a data analytics project, guiding users through each step to prevent omissions and maintain rigour in both analysis, reporting and peer-review. The framework blends relevant analytics elements from actuarial science, data science and epidemiology. We hope the framework will enhance transparency, reproducibility, and comprehensiveness in the reporting and peer-review of health and care data analytics projects.
Fitting loss distributions in insurance is sometimes a dilemma: either you get a good fit for the small/medium losses or for the very large losses. To be able to get both at the same time, this paper studies generalisations and extensions of the Pareto model that initially look like, for example, the Lognormal distribution but have a Pareto or GPD tail. We design a classification of such spliced distributions, which embraces and generalises various existing approaches. Special attention is paid to the geometry of distribution functions and to intuitive interpretations of the parameters, which can ease parameter inference from scarce data. The developed framework gives also new insights into the old Riebesell (power curve) exposure rating method.
This paper has been prepared by the IFoA’s Collective Defined Contribution (CDC) working party. The purpose is to raise awareness within the actuarial community and pensions industry on the wide range of design options and considerations for CDC solutions, together with a set of principles for the design work, which we believe should apply in most cases. This should also aid understanding of why different designs are better in different circumstances, and why some designs might have certain features that others would avoid.
Distribution channels such as bancassurance, brokers, agents, direct online sales, and insurance aggregators have been key to ensuring premium growth for both life and non-life insurers. However in recent years, an emerging channel known as embedded insurance has started to provide insurers with a brand-new growth driver. In this paper, we first present an introduction to embedded insurance – what it is and how it will shape insurance distribution in the industry. We then introduce a framework to classify embedded insurance recommendation system. Finally, we propose a novel insurance recommendation system using supervised learning algorithms that can be applied to e-commerce platforms. This needs-based collaborative filtering technique recommends one of three insurance products that would be most appropriate for each buyer on the Olist e-commerce platform based on order-level data. Our work is relevant for actuaries in this field interested in the pricing of embedded insurance risk as well as insurers seeking to improve insurance penetration on such platforms.
As the population ages, the provision of adult long-term care (LTC) is one of the major challenges facing the UK and other developed nations. LTC funding for the elderly is complex, reflecting the range and level of services provided, with the total cost depending on the duration of LTC required. Institutional care settings (e.g., nursing/residential care homes) represent the most expensive form of LTC. Planning and funding for institutional LTC requires an understanding of the factors affecting the mortality (and hence duration and cost of care) of such LTC recipients. Using data provided by Bupa, one of the largest LTC providers in Britain, this paper investigates factors affecting the mortality of residents of institutional LTC facilities over the period 2016-2019. Consistent with existing research, most residents were female and had a higher average age profile compared with male residents. For those residents who died during the investigation period, the average length of stay was approximately 1.6 times longer for females relative to males. For both males and females, new residents experienced higher mortality in the first-year post admission compared to existing residents. Variations in the mortality of the residents were analysed by condition, funding status and care type on admission.
In 1987, the United Nations Brundtland Commission defined sustainability as “meeting the needs of the present without compromising the ability of future generations to meet their own needs.” In recent years, the sustainability agenda has grown in importance, with many countries, regulators, industries shifting to implement sustainable practices. For retirement funds this means providing a lasting income in retirement for members, whilst ensuring a positive contribution to society and the environment. Retirement funds, with long-term liabilities, are therefore well placed and can play a significant role in contributing to the overall objective. This paper explores how retirement funds in various countries are progressing this agenda. We then introduce a sustainability reporting index, which measures the breadth and quality of how retirement funds can report on pricing in social and environmental externalities in the provision of a pension promise. The sustainability reporting index includes the financial inclusion aspects of retirement funds as well as how social and environmental externalities can be factored into the running of a fund and how its assets are invested. It explores the key areas that need to be monitored, the types of data required and the types of analytics that can be used by various stakeholders. The sustainability reporting index is intended to provide a benchmark against which various stakeholders can measure the effectiveness of their approach in pricing in these externalities. Actuaries of retirement funds can use the framework to go beyond focussing purely on the financial aspects of a fund, incorporating material non-financial aspects to ensure the provision of a sustainable pension income.
Observed competitive market profit margins in property and casualty insurance have typically been higher than the capital assets pricing model adjustment for risky loss cashflows would suggest. Explanations for this difference include frictions from operating an insurance business and capital risks that are not adequately recognised and rewarded by the theory. It is proposed that the difference may instead be related to the consumption of insurance services and claim fulfilment with an additional fair profit margin evaluated using marginal utility pricing principles.
This research presents the design, pricing, and consumer testing results of a potential private financial product that integrates retirement savings with social care funding through contributions to a supplemental defined contribution pension scheme. With this product, some contributions will be earmarked specifically to cover social care expenses if needed post-retirement. Our research indicates that offering benefits that address both retirement income supplementation and social care funding in a combined approach is appealing to consumers and could help overcome behavioural barriers to planning for social care. As with established defined contribution schemes, this product is designed for distribution in the workplace. Employees can contribute a portion of their earnings to their pension accounts. Employers may partially or fully match these contributions, further incentivising participation. In addition to financial support, participants will gain access to social care coordination services designed to facilitate ageing at home. These services will help retirees navigate care options, coordinate necessary support, and optimise the use of their allocated social care funds, ultimately promoting independence and well-being in later life.
Disclosing transition plans to meet future net zero climate targets requires organisations to fundamentally move beyond traditional historical-oriented stewardship reporting towards forward-looking accountability to meet their obligations to their future shareholders and stakeholders. However, despite a range of varying requirements concerning disclosure of climate-related targets to meet the Paris Agreement, confusion remains over the appropriate form, content and standard of transition plan disclosure that are required to implement these targets. The former UK based Transition Plan Taskforce set out globally leading requirements for transition plan reporting in 2023, however the extent to which these recommendations have since been implemented has not yet been comprehensively analysed. This paper summarises the key differences between UK, European and International guidelines for transition plans and then discusses the results of an analysis of variations in transition plan reporting practices by a sample of globally large financial and industrial organisations. It is predicted that a combination of both firm-level climate risk and country-level institutional factors are associated with the propensity to produce public transition plans. The empirical results are largely supportive of these predictions. Firms with greater levels of engagement with climate risk (as proxied by the CDP score), and UK and-or EU based firms, are more likely to produce climate transition plans. The empirical results are corroborated by qualitative analysis, which compares examples of good practice transition plan reporting by a sub-sample of firms within each industry sector. It is concluded that the resulting lack of clarity by regulatory authorities, and diversity in transition plan reporting practices by globally large financial and industrial firms, may potentially result in confusion and a lack of informed decision-making by their stakeholders and policymakers concerning climate-related resilience and risk mitigation actions.
Inequality is an inherent quality of society. This paper provides actuarial insights into the recognition, measurement, and consequences of inequality. Key underlying concepts are discussed, with an emphasis on the distinction between inequality of opportunity and inequality of outcome. To better design and maintain approaches and programmes that mitigate its adverse effects, it is important to understand its contributing causes. The paper outlines strategies for reflecting on and addressing inequality in actuarial practice. Actuaries are encouraged to work with policymakers, employers, providers, regulators, and individuals in the design and management of sustainable programmes to address some of the critical issues associated with inequality. These programmes can encourage more equal opportunities and protect against the adverse financial effects of outcomes.
This paper presents a comprehensive analysis of the frequency and severity of accidents involving electric vehicles (EVs) in comparison to internal combustion engine vehicles (ICEVs). It draws on extensive data from Norway from 2020 to 2023, a period characterised by significant EV adoption. We examine over two million registered EVs that collectively account for 28 billion kilometres of travel. In total we have analysed 139 billion kilometres of travel and close to 14,0000 accidents across all fuel types. We supplement this data with data from the Highway Loss Data Institute in the US and Association of British Insurers data in the UK as well as information from the Guy Carpenter large loss motor database. A thorough analysis comparing accident frequency and severity of EVs with ICEVs in the literature to date has yet to be conducted, which this paper aims to address. This research will assist actuaries and analysts across various domains, including pricing, reserving and reinsurance considerations. Our findings reveal a notable reduction in the frequency of accidents across all fuel types over time. Specifically, EVs demonstrate a lower accident frequency compared to ICEVs, a trend that may be attributed more to advancements in technology rather than the inherent characteristics of the fuel type, even when adjusted for COVID. Furthermore, our analysis indicates that EVs experience fewer accidents involving single units relative to non-EV and suggests a decrease in driver error and superior performance on regular road types. Reduction in EV accident frequency of 17% and a change in the distribution of average severity with higher damage costs and lower injury costs leading to an overall reduction of 11% However, it is important to note that when accidents do occur, the number of units involved as a proxy for severity involving EVs is marginally higher than those involving ICEVs. The average claim cost profile for EVs changes significantly with property damage claims being more expensive and bodily injury claims being less expensive for EVs. Overall, our research concludes that EVs present a lower risk profile compared to their ICEV counterparts, highlighting the evolving landscape of vehicle safety in the context of increasing EV utilisation.
Governments all over the world are struggling to control the spiralling costs of healthcare – the UK government is no exception. Its long-term strategy includes a much greater focus on prevention: to keep people as healthy and productive as possible for longer. This paper asks whether a greater focus on prevention is a possible lifeline for the National Health Service (NHS) as is often claimed, but it also examines other benefits to society. After considering various examples of prevention and the metrics used to measure their effectiveness, we use tobacco consumption as a case study to evaluate the costs to the public purse and to wider society. We give further examples, including obesity, but in less depth. We find that whilst there are significant benefits to public expenditure, including the NHS, in both cases, these are dwarfed by wider benefits to society both in terms of tangible economic benefits and improved well-being. We offer several suggestions for improving our understanding of the effectiveness of prevention policies in general and how the Actuarial profession can contribute to this debate.
This paper provides practical guidance to UK-based financial institutions (UKFIs) that are subject to the “operational resilience” guideline requirements of the Bank of England (BoE), Prudential Regulatory Authority and Financial Conduct Authority, issued in 2021, and fully effective for 31 March 2025. It contains practical suggestions and recommendations to assist UKFIs in implementing the guidelines. The scope of the paper covers issues related to (a) overviewing the latest equivalent operational resilience guidance in other countries and internationally (b) identifying key issues related to risk culture, risk appetite, information technology, tolerance setting, risk modelling, scenario planning and customer oriented operational resilience (c) identifying a framework for operational resilience based on a thorough understanding of these parameters and (d) designing and implementing an operational resilience maturity dashboard based on a sample of large UKIFs. The study also contains recommendations for further action, including enhanced controls and operational risk management frameworks. It concludes by identifying imperative policy actions to ensure that the implementation of the guidelines is more effective.
