IDEAS home Printed from https://ideas.repec.org/h/spr/conchp/978-3-030-85254-2_33.html
   My bibliography  Save this book chapter

Risk Classification in Nonlife Insurance Premium Ratemaking

In: Advances in Econometrics, Operational Research, Data Science and Actuarial Studies

Author

Listed:
  • Amela Omerašević

    (Uniqa osiguranje d.d)

  • Jasmina Selimović

    (University of Sarajevo)

Abstract

The aim of this research is to explore and analyze the benefits of risk classification methods by using data mining techniques on premium ratemaking in nonlife insurance. We rely on generalized linear models (GLMs) framework for nonlife premiums and examine the impact of specific data mining techniques on classifications of risk in motor hull insurance in Bosnia and Herzegovina. We study this relationship in an integrated framework considering a standard risk model based on the application of Poisson GLM for claims frequency estimate. Although GLM is a widely used method to determine insurance premiums, improvements of GLM by using the data mining methods identified in this paper may solve practical challenges for the risk models. The application of the data mining method in this paper aims to improve the results in the process of nonlife insurance premium ratemaking. The improvement is reflected in the choice of predictors or risk factors that have an impact on insurance premium rates. The following data mining methods for the selection of prediction variables were investigated: forward stepwise and neural networks. We provide strong and robust evidence that the use of data mining techniques influences premium ratemaking in nonlife insurance.

Suggested Citation

  • Amela Omerašević & Jasmina Selimović, 2022. "Risk Classification in Nonlife Insurance Premium Ratemaking," Contributions to Economics, in: M. Kenan Terzioğlu (ed.), Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, pages 541-571, Springer.
    • Handle: RePEc:spr:conchp:978-3-030-85254-2_33
    DOI: 10.1007/978-3-030-85254-2_33
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:conchp:978-3-030-85254-2_33. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.