IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04748464.html
   My bibliography  Save this paper

Editorial

Author

Listed:
  • Katrien Antonio

    (KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven)

  • Christophe Dutang

    (ASAR - Applied Statistics And Reliability - ASAR - LJK - Laboratoire Jean Kuntzmann - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

  • Andreas Tsanakas

    (The Business School (formerly Cass), City, University of London)

Abstract

This is the editorial introduction of the special issue of the Annals of Actuarial Science on Insurance Data Science.

Suggested Citation

  • Katrien Antonio & Christophe Dutang & Andreas Tsanakas, 2021. "Editorial," Post-Print hal-04748464, HAL.
    • Handle: RePEc:hal:journl:hal-04748464
    DOI: 10.1017/S174849952100018X
    Note: View the original document on HAL open archive server: https://hal.science/hal-04748464v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04748464v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1017/S174849952100018X?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Richman, Ronald, 2021. "AI in actuarial science – a review of recent advances – part 2," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 230-258, July.
    2. Hu, Sen & O’Hagan, Adrian & Sweeney, James & Ghahramani, Mohammadhossein, 2021. "A spatial machine learning model for analysing customers’ lapse behaviour in life insurance," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 367-393, July.
    3. Zhu, Rui & Wüthrich, Mario V., 2021. "Clustering driving styles via image processing," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 276-290, July.
    4. Peters, Gareth W. & Yan, Hongxuan & Chan, Jennifer, 2021. "Statistical features of persistence and long memory in mortality data," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 291-317, July.
    5. Richman, Ronald & Wüthrich, Mario V., 2021. "A neural network extension of the Lee–Carter model to multiple populations," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 346-366, July.
    6. Tseung, Spark C. & Badescu, Andrei L. & Fung, Tsz Chai & Lin, X. Sheldon, 2021. "LRMoE.jl: a software package for insurance loss modelling using mixture of experts regression model," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 419-440, July.
    7. Fernandez-Arjona, Lucio, 2021. "A neural network model for solvency calculations in life insurance," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 259-275, July.
    8. Richman, Ronald, 2021. "AI in actuarial science – a review of recent advances – part 1," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 207-229, July.
    9. Kwasa, Shadrack & Jones, Daniel, 2021. "A practical support vector regression algorithm and kernel function for attritional general insurance loss estimation," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 394-418, July.
    10. Pesenti, Silvana M. & Bettini, Alberto & Millossovich, Pietro & Tsanakas, Andreas, 2021. "Scenario Weights for Importance Measurement (SWIM) – an R package for sensitivity analysis," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 458-483, July.
    11. Huynh, Nhan & Ludkovski, Mike, 2021. "Multi-output Gaussian processes for multi-population longevity modelling," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 318-345, July.
    12. Hu, Sen & Murphy, T. Brendan & O’Hagan, Adrian, 2021. "mvClaim: an R package for multivariate general insurance claims severity modelling," Annals of Actuarial Science, Cambridge University Press, vol. 15(2), pages 441-457, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hung-Tsung Hsiao & Chou-Wen Wang & I.-Chien Liu & Ko-Lun Kung, 2024. "Mortality improvement neural-network models with autoregressive effects," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 363-383, April.
    2. Benjamin Avanzi & Greg Taylor & Melantha Wang & Bernard Wong, 2023. "Machine Learning with High-Cardinality Categorical Features in Actuarial Applications," Papers 2301.12710, arXiv.org.
    3. Aleksandar Arandjelovi'c & Julia Eisenberg, 2024. "Reinsurance with neural networks," Papers 2408.06168, arXiv.org.
    4. Jamotton, Charlotte & Hainaut, Donatien, 2024. "Latent Dirichlet Allocation for structured insurance data," LIDAM Discussion Papers ISBA 2024008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Francesca Perla & Salvatore Scognamiglio, 2023. "Locally-coherent multi-population mortality modelling via neural networks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 157-176, June.
    6. Corsaro, Stefania & Marino, Zelda & Scognamiglio, Salvatore, 2024. "Quantile mortality modelling of multiple populations via neural networks," Insurance: Mathematics and Economics, Elsevier, vol. 116(C), pages 114-133.
    7. Yang Qiao & Chou-Wen Wang & Wenjun Zhu, 2024. "Machine learning in long-term mortality forecasting," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 49(2), pages 340-362, April.
    8. Sebastian Calcetero-Vanegas & Andrei L. Badescu & X. Sheldon Lin, 2023. "Claim Reserving via Inverse Probability Weighting: A Micro-Level Chain-Ladder Method," Papers 2307.10808, arXiv.org, revised Jun 2024.
    9. Nhan Huynh & Mike Ludkovski, 2021. "Joint Models for Cause-of-Death Mortality in Multiple Populations," Papers 2111.06631, arXiv.org.
    10. Thilini Dulanjali Kularatne & Jackie Li & Yanlin Shi, 2022. "Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model," Risks, MDPI, vol. 10(11), pages 1-23, November.
    11. Mark Kiermayer & Christian Wei{ss}, 2022. "Neural calibration of hidden inhomogeneous Markov chains -- Information decompression in life insurance," Papers 2201.02397, arXiv.org.
    12. Mathias Valla & Xavier Milhaud & Anani Ayodélé Olympio, 2023. "Including individual Customer Lifetime Value and competing risks in tree-based lapse management strategies," Post-Print hal-03903047, HAL.
    13. Övgücan Karadağ Erdemir, 2023. "A Comparative Perspective on Multivariate Modeling of Insurance Compensation Payments with Regression-Based and Copula-Based Models," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 161-171, December.
    14. Andrea Nigri & Susanna Levantesi & Jose Manuel Aburto, 2022. "Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(8), pages 199-232.
    15. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.
    16. Mathias Valla & Xavier Milhaud & Anani Ayodélé Olympio, 2023. "Including individual Customer Lifetime Value and competing risks in tree-based lapse management strategy," Working Papers hal-03903047, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04748464. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.