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Neural networks approach for determining total claim amounts in insurance

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  • Dalkilic, Turkan Erbay
  • Tank, Fatih
  • Kula, Kamile Sanli

Abstract

In this study, we present an approach based on neural networks, as an alternative to the ordinary least squares method, to describe the relation between the dependent and independent variables. It has been suggested to construct a model to describe the relation between dependent and independent variables as an alternative to the ordinary least squares method. A new model, which contains the month and number of payments, is proposed based on real data to determine total claim amounts in insurance as an alternative to the model suggested by Rousseeuw et al. (1984) [Rousseeuw, P., Daniels, B., Leroy, A., 1984. Applying robust regression to insurance. Insurance: Math. Econom. 3, 67-72] in view of an insurer.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:insuma:v:45:y:2009:i:2:p:236-241
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    References listed on IDEAS

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    1. 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.
    2. Cheng, Chi-Bin & Lee, E. Stanley, 2001. "Switching regression analysis by fuzzy adaptive network," European Journal of Operational Research, Elsevier, vol. 128(3), pages 647-663, February.
    3. Rousseeuw, P. & Daniels, B. & Leroy, A., 1984. "Applying robust regression to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 3(1), pages 67-72, January.
    4. Shapiro, Arnold F., 2002. "The merging of neural networks, fuzzy logic, and genetic algorithms," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 115-131, August.
    5. Rob Kaas & Marc Goovaerts & Jan Dhaene & Michel Denuit, 2008. "Modern Actuarial Risk Theory," Springer Books, Springer, edition 2, number 978-3-540-70998-5, December.
    6. Shapiro, Arnold F., 2004. "Fuzzy logic in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 399-424, October.
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    Cited by:

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    2. Banghee So & Jean-Philippe Boucher & Emiliano A. Valdez, 2021. "Synthetic Dataset Generation of Driver Telematics," Risks, MDPI, vol. 9(4), pages 1-19, March.

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