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Connecting actuarial judgment to probabilistic learning techniques with graph theory

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  • Roland R. Ramsahai

Abstract

Graphical models have been widely used in applications ranging from medical expert systems to natural language processing. Their popularity partly arises since they are intuitive representations of complex inter-dependencies among variables with efficient algorithms for performing computationally intensive inference in high-dimensional models. It is argued that the formalism is very useful for applications in the modelling of non-life insurance claims data. It is also shown that actuarial models in current practice can be expressed graphically to exploit the advantages of the approach. More general models are proposed within the framework to demonstrate the potential use of graphical models for probabilistic learning with telematics and other dynamic actuarial data. The discussion also demonstrates throughout that the intuitive nature of the models allows the inclusion of qualitative knowledge or actuarial judgment in analyses.

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  • Roland R. Ramsahai, 2020. "Connecting actuarial judgment to probabilistic learning techniques with graph theory," Papers 2007.15475, arXiv.org.
  • Handle: RePEc:arx:papers:2007.15475
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    References listed on IDEAS

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    1. Garrido, J. & Genest, C. & Schulz, J., 2016. "Generalized linear models for dependent frequency and severity of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 205-215.
    2. Parodi, Pietro, 2012. "Computational intelligence with applications to general insurance: a review," Annals of Actuarial Science, Cambridge University Press, vol. 6(2), pages 344-380, September.
    3. Parodi, Pietro, 2012. "Computational intelligence with applications to general insurance: a review," Annals of Actuarial Science, Cambridge University Press, vol. 6(2), pages 307-343, September.
    4. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
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    Cited by:

    1. Denuit, Michel & Robert, Christian Y., 2020. "Conditional mean risk sharing for dependent risks using graphical models," LIDAM Discussion Papers ISBA 2020029, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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