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Fundamentals of Risk Measurement and Aggregation for Insurance Applications

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Modeling Decisions for Artificial Intelligence (MDAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9880))

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Abstract

The fundamentals of insurance are introduced and alternatives to risk measurement are presented, illustrating how the size and likelihood of future losses may be quantified. Real data indicate that insurance companies handle many small losses, while large or extreme claims occur only very rarely. The skewness of the profit and loss probability distribution function is especially troublesome for risk quantification, but its strong asymmetry is successfully addressed with generalizations of kernel estimation. Closely connected to this approach, distortion risk measures study the expected losses of a transformation of the original data. GlueVaR risk measures are presented. The notions of subadditivity and tail-subadditivity are discussed and an overview of risk aggregation is given with some additional applications to insurance.

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Authors and Affiliations

  1. Department of Econometrics, Riskcenter-IREA, Universitat de Barcelona, Av. Diagonal, 690, 08034, Barcelona, Spain

    Montserrat Guillen, Catalina Bolancé & Miguel Santolino

Authors
  1. Montserrat Guillen
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  2. Catalina Bolancé
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  3. Miguel Santolino
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Corresponding author

Correspondence to Montserrat Guillen .

Editor information

Editors and Affiliations

  1. University of Kövde , Skövde, Sweden

    Vicenç Torra

  2. Toho Gakuen , Kunitachi, Tokyo, Japan

    Yasuo Narukawa

  3. Enginyeria de la Informacio I de les Com, Univ Autonoma de Barcelona, Bellaterra, Spain

    Guillermo Navarro-Arribas

  4. Universitat d'Andorra , Sant Julià de Lòria, Andorra

    Cristina Yañez

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© 2016 Springer International Publishing Switzerland

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Guillen, M., Bolancé, C., Santolino, M. (2016). Fundamentals of Risk Measurement and Aggregation for Insurance Applications. In: Torra, V., Narukawa, Y., Navarro-Arribas, G., Yañez, C. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2016. Lecture Notes in Computer Science(), vol 9880. Springer, Cham. https://doi.org/10.1007/978-3-319-45656-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-45656-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45655-3

  • Online ISBN: 978-3-319-45656-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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