Risk Classification in Nonlife Insurance Premium Ratemaking

Amela Omerašević and Jasmina Selimović ()
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Amela Omerašević: Uniqa osiguranje d.d
Jasmina Selimović: University of Sarajevo

A chapter in Advances in Econometrics, Operational Research, Data Science and Actuarial Studies, 2022, pp 541-571 from Springer

Abstract: 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.

Keywords: Ratemaking; Forward stepwise; Neural networks; Nonlife insurance; GLM (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:conchp:978-3-030-85254-2_33

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DOI: 10.1007/978-3-030-85254-2_33

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