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Editorial on the Special Issue on Insurance: complexity, risks and its connection with social sciences

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  • Diego Zappa

    (Universitá Cattolica del Sacro Cuore)

  • Gian Paolo Clemente

    (Universitá Cattolica del Sacro Cuore)

  • Francesco Della Corte

    (Universitá Cattolica del Sacro Cuore)

  • Nino Savelli

    (Universitá Cattolica del Sacro Cuore)

Abstract

No abstract is available for this item.

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  • Diego Zappa & Gian Paolo Clemente & Francesco Della Corte & Nino Savelli, 2023. "Editorial on the Special Issue on Insurance: complexity, risks and its connection with social sciences," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 125-130, December.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-023-01705-9
    DOI: 10.1007/s11135-023-01705-9
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    References listed on IDEAS

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    1. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    2. Guangyuan Gao & Mario V. Wüthrich, 2019. "Convolutional Neural Network Classification of Telematics Car Driving Data," Risks, MDPI, vol. 7(1), pages 1-18, January.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Cerqueti, Roy & Ferraro, Giovanna & Iovanella, Antonio, 2019. "Measuring network resilience through connection patterns," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 320-329.
    5. Gian Paolo Clemente & Pierpaolo Marano, 2020. "The broker model for peer-to-peer insurance: an analysis of its value," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(3), pages 457-481, July.
    6. Łukasz Delong & Mario V. Wüthrich, 2020. "Neural Networks for the Joint Development of Individual Payments and Claim Incurred," Risks, MDPI, vol. 8(2), pages 1-34, April.
    7. Guangyuan Gao & Shengwang Meng & Mario V. Wüthrich, 2019. "Claims frequency modeling using telematics car driving data," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2019(2), pages 143-162, February.
    8. Hainaut, Donatien, 2018. "A Neural-Network Analyzer for Mortality Forecast," LIDAM Reprints ISBA 2018027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    9. Kim, Hyong & Gardner, Errol, 2015. "The science of winning in financial services — competing on analytics: opportunities to unlock the power of data," Journal of Financial Perspectives, EY Global FS Institute, vol. 3(2), pages 13-24.
    10. Emanuela Raffinetti, 2023. "A Rank Graduation Accuracy measure to mitigate Artificial Intelligence risks," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 131-150, December.
    11. Clemente, Gian Paolo & Cornaro, Alessandra, 2022. "A multilayer approach for systemic risk in the insurance sector," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    12. Aslam, Faheem & Hunjra, Ahmed Imran & Ftiti, Zied & Louhichi, Wael & Shams, Tahira, 2022. "Insurance fraud detection: Evidence from artificial intelligence and machine learning," Research in International Business and Finance, Elsevier, vol. 62(C).
    13. Schnürch, Simon & Korn, Ralf, 2022. "Point And Interval Forecasts Of Death Rates Using Neural Networks," ASTIN Bulletin, Cambridge University Press, vol. 52(1), pages 333-360, January.
    14. 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.
    15. Chengcheng Zhang & Yujia Ding & Qidi Peng, 2023. "How do demand-side incentives relate to insurance transitioning behavior of public health insurance enrollees? A novel voting ensemble approach for ranking factors of mixed data types," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 217-246, December.
    16. Montserrat Guillen & Jens Perch Nielsen & Ana M. Pérez-Marín & Valandis Elpidorou, 2020. "Can Automobile Insurance Telematics Predict the Risk of Near-Miss Events?," North American Actuarial Journal, Taylor & Francis Journals, vol. 24(1), pages 141-152, January.
    17. Miyata, Akihiro & Matsuyama, Naoki, 2022. "Extending The Lee–Carter Model With Variational Autoencoder: A Fusion Of Neural Network And Bayesian Approach," ASTIN Bulletin, Cambridge University Press, vol. 52(3), pages 789-812, September.
    18. Leduc, Matt V. & Thurner, Stefan, 2017. "Incentivizing resilience in financial networks," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 44-66.
    19. Cohen Sabban, Isaac & Lopez, Olivier & Mercuzot, Yann, 2022. "Automatic analysis of insurance reports through deep neural networks to identify severe claims," Annals of Actuarial Science, Cambridge University Press, vol. 16(1), pages 42-67, March.
    20. Hainaut, Donatien, 2018. "A Neural-Network Analyzer For Mortality Forecast," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 481-508, May.
    21. 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.
    22. Jessica Pesantez-Narvaez & Montserrat Guillen & Manuela Alcañiz, 2019. "Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression," Risks, MDPI, vol. 7(2), pages 1-16, June.
    23. Tae-Ho Kang & Ashish Sharma & Lucy Marshall, 2021. "Assessing Goodness of Fit for Verifying Probabilistic Forecasts," Forecasting, MDPI, vol. 3(4), pages 1-11, October.
    24. Henckaerts, Roel & Antonio, Katrien, 2022. "The added value of dynamically updating motor insurance prices with telematics collected driving behavior data," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 79-95.
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