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.
