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INSURANCE (26 journals)

Showing 1 - 26 of 26 Journals sorted alphabetically
Annals of Actuarial Science     Full-text available via subscription   (Followers: 2)
Assurances et gestion des risques     Full-text available via subscription  
Astin Bulletin     Full-text available via subscription   (Followers: 1)
Banks in Insurance Report     Hybrid Journal   (Followers: 1)
Blätter der DGVFM     Hybrid Journal   (Followers: 2)
British Actuarial Journal     Full-text available via subscription   (Followers: 1)
Geneva Papers on Risk and Insurance - Issues and Practice     Hybrid Journal   (Followers: 14)
Geneva Risk and Insurance Review     Hybrid Journal   (Followers: 8)
Health Affairs     Full-text available via subscription   (Followers: 83)
Insurance Markets and Companies     Open Access   (Followers: 1)
Insurance: Mathematics and Economics     Hybrid Journal   (Followers: 10)
International Journal of Business Continuity and Risk Management     Hybrid Journal   (Followers: 28)
International Journal of Forensic Engineering     Hybrid Journal   (Followers: 3)
International Journal of Forensic Engineering and Management     Hybrid Journal   (Followers: 3)
International Journal of Health Economics and Management     Hybrid Journal   (Followers: 12)
International Social Security Review     Hybrid Journal   (Followers: 8)
Journal for Labour Market Research     Open Access   (Followers: 10)
Journal of Derivatives & Hedge Funds     Hybrid Journal   (Followers: 9)
Journal of Risk and Insurance     Hybrid Journal   (Followers: 18)
Journal of Risk Finance     Hybrid Journal   (Followers: 6)
Risk Management     Hybrid Journal   (Followers: 15)
Risk Management & Insurance Review     Hybrid Journal   (Followers: 11)
Scandinavian Actuarial Journal     Hybrid Journal   (Followers: 2)
SourceOECD Finance & Investment/Insurance & Pensions     Full-text available via subscription   (Followers: 3)
The Geneva Reports     Free   (Followers: 2)
Zeitschrift für die gesamte Versicherungswissenschaft     Hybrid Journal   (Followers: 1)
Similar Journals
Journal Cover
Annals of Actuarial Science
Number of Followers: 2  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 1748-4995 - ISSN (Online) 1748-5002
Published by Cambridge University Press Homepage  [400 journals]
  • Editorial

    • Free pre-print version: Loading...

      Authors: Katrien Antonio; Christophe Dutang, Andreas Tsanakas
      Pages: 205 - 206
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S174849952100018X
      Issue No: Vol. 15, No. 2 (2021)
       
  • AI in actuarial science – a review of recent advances – part 1

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      Authors: Ronald Richman
      Pages: 207 - 229
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499520000238
      Issue No: Vol. 15, No. 2 (2021)
       
  • AI in actuarial science – a review of recent advances – part 2

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      Authors: Ronald Richman
      Pages: 230 - 258
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S174849952000024X
      Issue No: Vol. 15, No. 2 (2021)
       
  • A neural network model for solvency calculations in life insurance

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      Authors: Lucio Fernandez-Arjona
      Pages: 259 - 275
      Abstract: Insurance companies make extensive use of Monte Carlo simulations in their capital and solvency models. To overcome the computational problems associated with Monte Carlo simulations, most large life insurance companies use proxy models such as replicating portfolios (RPs). In this paper, we present an example based on a variable annuity guarantee, showing the main challenges faced by practitioners in the construction of RPs: the feature engineering step and subsequent basis function selection problem. We describe how neural networks can be used as a proxy model and how to apply risk-neutral pricing on a neural network to integrate such a model into a market risk framework. The proposed model naturally solves the feature engineering and feature selection problems of RPs.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499520000330
      Issue No: Vol. 15, No. 2 (2021)
       
  • Clustering driving styles via image processing

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      Authors: Rui Zhu; Mario V. Wüthrich
      Pages: 276 - 290
      Abstract: It has become of key interest in the insurance industry to understand and extract information from telematics car driving data. Telematics car driving data of individual car drivers can be summarised in so-called speed–acceleration heatmaps. The aim of this study is to cluster such speed–acceleration heatmaps to different categories by analysing similarities and differences in these heatmaps. Making use of local smoothness properties, we propose to process these heatmaps as RGB images. Clustering can then be achieved by involving supervised information via a transfer learning approach using the pre-trained AlexNet to extract discriminative features. The K-means algorithm is then applied on these extracted discriminative features for clustering. The experiment results in an improvement of heatmap clustering compared to classical approaches.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499520000317
      Issue No: Vol. 15, No. 2 (2021)
       
  • Statistical features of persistence and long memory in mortality data

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      Authors: Gareth W. Peters; Hongxuan Yan, Jennifer Chan
      Pages: 291 - 317
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499521000129
      Issue No: Vol. 15, No. 2 (2021)
       
  • Multi-output Gaussian processes for multi-population longevity modelling

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      Authors: Nhan Huynh; Mike Ludkovski
      Pages: 318 - 345
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499521000142
      Issue No: Vol. 15, No. 2 (2021)
       
  • A neural network extension of the Lee–Carter model to multiple
           populations

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      Authors: Ronald Richman; Mario V. Wüthrich
      Pages: 346 - 366
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499519000071
      Issue No: Vol. 15, No. 2 (2021)
       
  • A spatial machine learning model for analysing customers’ lapse
           behaviour in life insurance

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      Authors: Sen Hu; Adrian O’Hagan, James Sweeney, Mohammadhossein Ghahramani
      Pages: 367 - 393
      Abstract: Spatial analysis ranges from simple univariate descriptive statistics to complex multivariate analyses and is typically used to investigate spatial patterns or to identify spatially linked consumer behaviours in insurance. This paper investigates if the incorporation of publicly available spatially linked demographic census data at population level is useful in modelling customers’ lapse behaviour (i.e. stopping payment of premiums) in life insurance policies, based on data provided by an insurance company in Ireland. From the insurance company’s perspective, identifying and assessing such lapsing risks in advance permit engagement to prevent such incidents, saving money by re-evaluating customer acquisition channels and improving capital reserve calculation and preparation. Incorporating spatial analysis in lapse modelling is expected to improve lapse prediction. Therefore, a hybrid approach to lapse prediction is proposed – spatial clustering using census data is used to reveal the underlying spatial structure of customers of the Irish life insurer, in conjunction with traditional statistical models for lapse prediction based on the company data. The primary contribution of this work is to consider the spatial characteristics of customers for life insurance lapse behaviour, via the integration of reliable government provided census demographics, which has not been considered previously in actuarial literature. Company decision-makers can use the insights gleaned from this analysis to identify customer subsets to target with personalized promotions to reduce lapse rates, and to reduce overall company risk.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499520000329
      Issue No: Vol. 15, No. 2 (2021)
       
  • A practical support vector regression algorithm and kernel function for
           attritional general insurance loss estimation

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      Authors: Shadrack Kwasa; Daniel Jones
      Pages: 394 - 418
      Abstract: The aim of the paper is to derive a simple, implementable machine learning method for general insurance losses. An algorithm for learning a general insurance loss triangle is developed and justified. An argument is made for applying support vector regression (SVR) to this learning task (in order to facilitate transparency of the learning method as compared to more “black-box” methods such as deep neural networks), and SVR methodology derived is specifically applied to this learning task. A further argument for preserving the statistical features of the loss data in the SVR machine is made. A bespoke kernel function that preserves the statistical features of the loss data is derived from first principles and called the exponential dispersion family (EDF) kernel. Features of the EDF kernel are explored, and the kernel is applied to an insurance loss estimation exercise for homogeneous risk of three different insurers. Results of the cumulative losses and ultimate losses predicted by the EDF kernel are compared to losses predicted by the radial basis function kernel and the chain-ladder method. A backtest of the developed method is performed. A discussion of the results and their implications follows.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499520000263
      Issue No: Vol. 15, No. 2 (2021)
       
  • LRMoE.jl: a software package for insurance loss modelling using mixture of
           experts regression model

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      Authors: Spark C. Tseung; Andrei L. Badescu, Tsz Chai Fung, X. Sheldon Lin
      Pages: 419 - 440
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499521000087
      Issue No: Vol. 15, No. 2 (2021)
       
  • mvClaim:+an+R+package+for+multivariate+general+insurance+claims+severity+modelling&rft.title=Annals+of+Actuarial+Science&rft.issn=1748-4995&rft.date=2021&rft.volume=15&rft.spage=441&rft.epage=457&rft.aulast=Hu&rft.aufirst=Sen&rft.au=Sen+Hu&rft.au=T.+Brendan+Murphy,+Adrian+O’Hagan&rft_id=info:doi/10.1017/S1748499521000099">mvClaim: an R package for multivariate general insurance claims severity
           modelling

    • Free pre-print version: Loading...

      Authors: Sen Hu; T. Brendan Murphy, Adrian O’Hagan
      Pages: 441 - 457
      Abstract: The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499521000099
      Issue No: Vol. 15, No. 2 (2021)
       
  • SWIM)+–+an+R+package+for+sensitivity+analysis&rft.title=Annals+of+Actuarial+Science&rft.issn=1748-4995&rft.date=2021&rft.volume=15&rft.spage=458&rft.epage=483&rft.aulast=Pesenti&rft.aufirst=Silvana&rft.au=Silvana+M.+Pesenti&rft.au=Alberto+Bettini,+Pietro+Millossovich,+Andreas+Tsanakas&rft_id=info:doi/10.1017/S1748499521000130">Scenario Weights for Importance Measurement (SWIM) – an R package for
           sensitivity analysis

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      Authors: Silvana M. Pesenti; Alberto Bettini, Pietro Millossovich, Andreas Tsanakas
      Pages: 458 - 483
      Abstract: 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.
      PubDate: 2021-07-01T00:00:00.000Z
      DOI: 10.1017/S1748499521000130
      Issue No: Vol. 15, No. 2 (2021)
       
 
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