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Risk Classification by Fuzzy Inference

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  • Per-Johan Horgby

    (Department of Economics, School of Economics and Commercial Law, Göteborg University, Vasagatan 1, S-411 80 Göteborg, Sweden)

Abstract

Traditionally, policyholders in life insurance are classified in simple mortality tables, most often according to only a few risk characteristics. Instead of a risk classification according to the numerical rating system, this article describes how to classify by using a fuzzy inference methodology. By defining risk factors as fuzzy sets, it is shown that an insurer can utilize multiple prognostic factors that are imprecise and vague. The presented fuzzy risk classification provides a more realistic way of modeling mortality risks since it allows for compensations and interactions between multiple risk factors. The Geneva Papers on Risk and Insurance Theory (1998) 23, 63–82. doi:10.1023/A:1008682014796

Suggested Citation

  • Per-Johan Horgby, 1998. "Risk Classification by Fuzzy Inference," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 23(1), pages 63-82, June.
    • Handle: RePEc:pal:genrir:v:23:y:1998:i:1:p:63-82
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    Cited by:

    1. de Andres-Sanchez, Jorge, 2007. "Claim reserving with fuzzy regression and Taylor's geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 145-163, January.
    2. Dalkilic, Turkan Erbay & Tank, Fatih & Kula, Kamile Sanli, 2009. "Neural networks approach for determining total claim amounts in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 236-241, October.

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