IDEAS home Printed from https://ideas.repec.org/a/eee/insuma/v45y2009i2p236-241.html
   My bibliography  Save this article

Neural networks approach for determining total claim amounts in insurance

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-6687(09)00067-5
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Rousseeuw, P. & Daniels, B. & Leroy, A., 1984. "Applying robust regression to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 3(1), pages 67-72, January.
    3. 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.
    4. Rob Kaas & Marc Goovaerts & Jan Dhaene & Michel Denuit, 2008. "Modern Actuarial Risk Theory," Springer Books, Springer, edition 2, number 978-3-540-70998-5, February.
    5. 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.
    6. Shapiro, Arnold F., 2004. "Fuzzy logic in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 399-424, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Banghee So & Jean-Philippe Boucher & Emiliano A. Valdez, 2021. "Synthetic Dataset Generation of Driver Telematics," Risks, MDPI, vol. 9(4), pages 1-19, March.
    2. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. Belles-Sampera, Jaume & Merigó, José M. & Guillén, Montserrat & Santolino, Miguel, 2013. "The connection between distortion risk measures and ordered weighted averaging operators," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 411-420.
    3. Sancho Salcedo-Sanz & Leo Carro-Calvo & Mercè Claramunt & Ana Castañer & Maite Mármol, 2014. "Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms," Risks, MDPI, vol. 2(2), pages 1-14, April.
    4. Sancho Salcedo-Sanz & L. Carro-Calvo & Mercè Claramunt & Anna Castañer & Maite Marmol, 2013. "An Analysis of Black-box Optimization Problems in Reinsurance: Evolutionary-based Approaches," Working Papers XREAP2013-04, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2013.
    5. Marc Sanchez-Roger & María Dolores Oliver-Alfonso & Carlos Sanchís-Pedregosa, 2019. "Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises," Mathematics, MDPI, vol. 7(11), pages 1-22, November.
    6. Liu, Ying & Li, Xiaozhong & Liu, Yinli, 2015. "The bounds of premium and optimality of stop loss insurance under uncertain random environments," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 273-278.
    7. Zhi Chen & Melvyn Sim & Huan Xu, 2019. "Distributionally Robust Optimization with Infinitely Constrained Ambiguity Sets," Operations Research, INFORMS, vol. 67(5), pages 1328-1344, September.
    8. Härdle, Wolfgang Karl & Burnecki, Krzysztof & Weron, Rafał, 2004. "Simulation of risk processes," Papers 2004,01, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    9. Kume, Alfred & Hashorva, Enkelejd, 2012. "Calculation of Bayes premium for conditional elliptical risks," Insurance: Mathematics and Economics, Elsevier, vol. 51(3), pages 632-635.
    10. Jelena Lukić & Mirjana Misita & Dragan D. Milanović & Ankica Borota-Tišma & Aleksandra Janković, 2022. "Determining the Risk Level in Client Analysis by Applying Fuzzy Logic in Insurance Sector," Mathematics, MDPI, vol. 10(18), pages 1-17, September.
    11. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    12. Castañer, A. & Claramunt, M.M. & Lefèvre, C., 2013. "Survival probabilities in bivariate risk models, with application to reinsurance," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 632-642.
    13. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 0. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 0, pages 1-17.
    14. Mourdoukoutas, Fotios & Boonen, Tim J. & Koo, Bonsoo & Pantelous, Athanasios A., 2021. "Pricing in a competitive stochastic insurance market," Insurance: Mathematics and Economics, Elsevier, vol. 97(C), pages 44-56.
    15. Mohamed Amine Lkabous & Jean-François Renaud, 2018. "A VaR-Type Risk Measure Derived from Cumulative Parisian Ruin for the Classical Risk Model," Risks, MDPI, vol. 6(3), pages 1-11, August.
    16. Tang, Qihe & Yang, Fan, 2012. "On the Haezendonck–Goovaerts risk measure for extreme risks," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 217-227.
    17. Shuofen Hsu & Chaohsin Lin & Yaling Yang, 2008. "Integrating Neural Networks for Risk‐Adjustment Models," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(3), pages 617-642, September.
    18. Pablo Azcue & Nora Muler, 2013. "Minimizing the ruin probability allowing investments in two assets: a two-dimensional problem," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 77(2), pages 177-206, April.
    19. Julien Trufin & Stéphane Loisel, 2013. "Ultimate ruin probability in discrete time with Bühlmann credibility premium adjustments," Post-Print hal-00426790, HAL.
    20. Martin Eling & David Antonius Pankoke, 2016. "Systemic Risk in the Insurance Sector: A Review and Directions for Future Research," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 19(2), pages 249-284, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:insuma:v:45:y:2009:i:2:p:236-241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505554 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.