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The article discusses the intersection of genetic testing and actuarial science in the context of life insurance. It highlights the complexities of genetic information, emphasizing that genetic variation is not limited to single-gene disorders like Huntington's disease. The article explores the potential for adverse selection in insurance and the need for actuaries to rely on medical studies to make underwriting decisions. It also examines the regulatory responses to genetic information in different countries. The author concludes that while genetic testing has not yet fully integrated into mainstream medicine, there is still a role for actuaries in understanding and navigating the implications of genetic information in insurance. [Extracted from the article]

But to achieve these objectives, actuaries and actuarial science have to work with climate scientists, as they are the leading experts to provide detailed information on historical weather patterns, ongoing climate changes, and future climate predictions. This includes developing new insurance or financial products for climate-related risks, for example flood and crop insurance, and pricing these risks appropriately, or improving product design to incentivize risk mitigation. While there is a growing interest in climate-related risks within the actuarial profession, professionals and climate scientists sometimes lack the resources to help them quantify the financial impacts of climate change. [Extracted from the article]

This article comments on the paper "Less-expensive long-term annuities linked to mortality, cash and equity" by Kevin Fergusson and Eckard Platen, appearing in this issue of the Annals of Actuarial Science. It adds two perspectives to their thought-provoking contribution. The first is a similarity to some recent work in quantitative finance on "deep hedging" that leverages machine learning models to find the cheapest replication strategy for a derivative payoff in a largely model-free setting. The second perspective engages with some of the interesting implications of their approach and draws parallels to literature in asset pricing and macro-finance. These perspectives point to the potential need for more fundamental shifts than the authors of the paper are advertising. [ABSTRACT FROM AUTHOR]

Moving from a Defined Benefit (DB) to a Notional Defined Contribution (NDC) pension formula creates significant re-distributive effects. We estimate the amount and the intensity of these effects in the case of the Italian transition to NDC, which began in 1995. Based on administrative data of the main Italian pension scheme (FPLD), we study the evolution of yearly inequality within old-age pension benefits. Furthermore, we study the adequacy and the actuarial fairness of the pension system, by estimating the replacement rates and the Net Present Value Ratio distribution for workers who retired in the period 1996–2019. Our results show that the very generous interpretation of acquired rights determined by the 1995 reform has contributed to maintaining a high level of adequacy and a significant level of intergenerational imbalance. The financial costs of this imbalance are estimated and its extent is significant. [ABSTRACT FROM AUTHOR]

Booth & Tickle ([4]), in their exhaustive work, review the main methodological developments in (stochastic) mortality modelling from 1980 onwards focusing not only on Lee-Carter or GLM-based methodologies but also on parametric models and old-age mortality. Li & Lee ([11]) presented an extension of the Lee & Carter ([8]) model by studying mortality within a multi-population context, whereby countries with similar economic performance are expected to have similar long-term mortality trends. Another way of studying mortality is not by extrapolating aggregate trends with a suitable model, but by studying the underlying causes of death. Exactly 11 years ago, Sweeting ([15]) noted in his Editorial that " I Even with the uncertainties around the choices of models and parameters, [stochastic mortality modeling] can be used to give a probabilistic assessment of the range of outcomes i ". [Extracted from the article]

The study of the distributions of sums of dependent risks is a key topic in actuarial sciences, risk management, reliability and in many branches of applied and theoretical probability. However, there are few results where the distribution of the sum of dependent random variables is available in a closed form. In this paper, we obtain several analytical expressions for the distribution of the aggregated risks under dependence in terms of copulas. We provide several representations based on the underlying copula and the marginal distribution functions under general hypotheses and in any dimension. Then, we study stochastic comparisons between sums of dependent risks. Finally, we illustrate our theoretical results by studying some specific models obtained from Clayton, Ali-Mikhail-Haq and Farlie-Gumbel-Morgenstern copulas. Extensions to more general copulas are also included. Bounds and the limiting behavior of the hazard rate function for the aggregated distribution of some copulas are studied as well. [ABSTRACT FROM AUTHOR]

Deep neural network models have substantial advantages over traditional and machine learning methods that make this class of models particularly promising for adoption by actuaries. Nonetheless, several important aspects of these models have not yet been studied in detail in the actuarial literature: the effect of hyperparameter choice on the accuracy and stability of network predictions, methods for producing uncertainty estimates and the design of deep learning models for explainability. To allow actuaries to incorporate deep learning safely into their toolkits, we review these areas in the context of a deep neural network for forecasting mortality rates. [ABSTRACT FROM AUTHOR]

The Scenario Weights for Importance Measurement (SWIM) package implements a flexible sensitivity analysis framework, based primarily on results and tools developed by Pesenti et al. (2019). SWIM provides a stressed version of a stochastic model, subject to model components (random variables) fulfilling given probabilistic constraints (stresses). Possible stresses can be applied on moments, probabilities of given events, and risk measures such as Value-At-Risk and Expected Shortfall. SWIM operates upon a single set of simulated scenarios from a stochastic model, returning scenario weights, which encode the required stress and allow monitoring the impact of the stress on all model components. The scenario weights are calculated to minimise the relative entropy with respect to the baseline model, subject to the stress applied. As well as calculating scenario weights, the package provides tools for the analysis of stressed models, including plotting facilities and evaluation of sensitivity measures. SWIM does not require additional evaluations of the simulation model or explicit knowledge of its underlying statistical and functional relations; hence, it is suitable for the analysis of black box models. The capabilities of SWIM are demonstrated through a case study of a credit portfolio model. [ABSTRACT FROM AUTHOR]

We investigate joint modelling of longevity trends using the spatial statistical framework of Gaussian process (GP) regression. Our analysis is motivated by the Human Mortality Database (HMD) that provides unified raw mortality tables for nearly 40 countries. Yet few stochastic models exist for handling more than two populations at a time. To bridge this gap, we leverage a spatial covariance framework from machine learning that treats populations as distinct levels of a factor covariate, explicitly capturing the cross-population dependence. The proposed multi-output GP models straightforwardly scale up to a dozen populations and moreover intrinsically generate coherent joint longevity scenarios. In our numerous case studies, we investigate predictive gains from aggregating mortality experience across nations and genders, including by borrowing the most recently available "foreign" data. We show that in our approach, information fusion leads to more precise (and statistically more credible) forecasts. We implement our models in R, as well as a Bayesian version in Stan that provides further uncertainty quantification regarding the estimated mortality covariance structure. All examples utilise public HMD datasets. [ABSTRACT FROM AUTHOR]

Understanding core statistical properties and data features in mortality data are fundamental to the development of machine learning methods for demographic and actuarial applications of mortality projection. The study of statistical features in such data forms the basis for classification, regression and forecasting tasks. In particular, the understanding of key statistical structure in such data can aid in improving accuracy in undertaking mortality projection and forecasting when constructing life tables. The ability to accurately forecast mortality is a critical aspect for the study of demography, life insurance product design and pricing, pension planning and insurance-based decision risk management. Though many stylised facts of mortality data have been discussed in the literature, we provide evidence for a novel statistical feature that is pervasive in mortality data at a national level that is as yet unexplored. In this regard, we demonstrate in this work a strong evidence for the existence of long memory features in mortality data, and second that such long memory structures display multifractality as a statistical feature that can act as a discriminator of mortality dynamics by age, gender and country. To achieve this, we first outline the way in which we choose to represent the persistence of long memory from an estimator perspective. We make a natural link between a class of long memory features and an attribute of stochastic processes based on fractional Brownian motion. This allows us to use well established estimators for the Hurst exponent to then robustly and accurately study the long memory features of mortality data. We then introduce to mortality analysis the notion from data science known as multifractality. This allows us to study the long memory persistence features of mortality data on different timescales. We demonstrate its accuracy for sample sizes commensurate with national-level age term structure historical mortality records. A series of synthetic studies as well a comprehensive analysis of real mortality death count data are studied in order to demonstrate the pervasiveness of long memory structures in mortality data, both mono-fractal and multifractal functional features are verified to be present as stylised facts of national-level mortality data for most countries and most age groups by gender. We conclude by demonstrating how such features can be used in kernel clustering and mortality model forecasting to improve these actuarial applications. [ABSTRACT FROM AUTHOR]

This paper introduces a new julia package, LRMoE, a statistical software tailor-made for actuarial applications, which allows actuarial researchers and practitioners to model and analyse insurance loss frequencies and severities using the Logit-weighted Reduced Mixture-of-Experts (LRMoE) model. LRMoE offers several new distinctive features which are motivated by various actuarial applications and mostly cannot be achieved using existing packages for mixture models. Key features include a wider coverage on frequency and severity distributions and their zero inflation, the flexibility to vary classes of distributions across components, parameter estimation under data censoring and truncation and a collection of insurance ratemaking and reserving functions. The package also provides several model evaluation and visualisation functions to help users easily analyse the performance of the fitted model and interpret the model in insurance contexts. [ABSTRACT FROM AUTHOR]

Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate, but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as "Deep Learning". This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning. [ABSTRACT FROM AUTHOR]

Rapid advances in artificial intelligence (AI) and machine learning are creating products and services with the potential not only to change the environment in which actuaries operate but also to provide new opportunities within actuarial science. These advances are based on a modern approach to designing, fitting and applying neural networks, generally referred to as "Deep Learning." This paper investigates how actuarial science may adapt and evolve in the coming years to incorporate these new techniques and methodologies. Part 1 of this paper provides background on machine learning and deep learning, as well as an heuristic for where actuaries might benefit from applying these techniques. Part 2 of the paper then surveys emerging applications of AI in actuarial science, with examples from mortality modelling, claims reserving, non-life pricing and telematics. For some of the examples, code has been provided on GitHub so that the interested reader can experiment with these techniques for themselves. Part 2 concludes with an outlook on the potential for actuaries to integrate deep learning into their activities. Finally, a supplementary appendix discusses further resources providing more in-depth background on machine learning and deep learning. [ABSTRACT FROM AUTHOR]

The Lee–Carter (LC) model is a basic approach to forecasting mortality rates of a single population. Although extensions of the LC model to forecasting rates for multiple populations have recently been proposed, the structure of these extended models is hard to justify and the models are often difficult to calibrate, relying on customised optimisation schemes. Based on the paradigm of representation learning, we extend the LCmodel to multiple populations using neural networks, which automatically select an optimal model structure. We fit this model to mortality rates since 1950 for all countries in the Human Mortality Database and observe that the out-of-sample forecasting performance of the model is highly competitive. [ABSTRACT FROM AUTHOR]

The document is the cover and front matter of the ANZIAM Journal, Volume 65, Issue 4, published in October 2023. The journal is edited by Andrew Bassom and Michael H. Meylan, with the editorial office located at the University of Tasmania and the University of Newcastle in Australia. The document also lists the contact information for the editors and associate editors from various universities in Australia, New Zealand, Canada, and the United Kingdom. The cover design is credited to Hilary Booth. [Extracted from the article]

If we adopt the classical perspective of mortality as comprising a current "base" assumption, and a future variation assumption (typically an assumption of improving mortality), there are a range of ways in which both the base and the future variation will likely change. The actuarial skillset clearly covers many of the areas that have occupied the news and our lives since early 2020: the parameterisation of risk, models of infectious spread and the related hospitalisation and mortality impacts, and estimation of the longer-term impacts of many of the measures introduced to control the pandemic. This special issue of the Annals of Actuarial Science features a range of papers relating to the coronavirus pandemic. [Extracted from the article]

The authors correct name is Hamza Hanbali. Pricing insurance policies with offsetting relationship - ERRATUM The publisher apologises that upon publication of this article the authors name was swapped around, presenting the surname as their first. [Extracted from the article]