Preliminary communication
https://doi.org/10.18045/zbefri.2020.2.667
Risk factors selection with data mining methods for insurance premium ratemaking
Amela Omerašević
; Uniqa osiguranje d.d. Sarajevo, Bosnia and Herzegovina.
Jasmina Selimović
orcid.org/0000-0001-8485-8642
; School of Economics and Business, University of Sarajevo, Bosnia and Herzegovina
Abstract
Insurance companies that have adopted the application of data mining methods in their business have become more competitive in the insurance market. Data mining methods provides the insurance industry with numerous advantages: shorter data processing times, more sophisticated methods for more accurate data analysis, better decision-making, etc. Insurance companies use data mining methods for various purposes, from marketing campaigns to fraud prevention. The process of insurance premium pricing was one of the first applications of data mining methods in insurance industry. The application of the data mining method in this paper aims to improve the results in the process of non-life 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, Decision trees and Neural networks. Generalized linear models (GLM) were used for premium ratemaking, as the main statistical model for non-life insurance premium pricing today in most developed insurance markets in the world.
Keywords
GLM; data mining methods; forward stepwise; decision trees; neural networks
Hrčak ID:
249266
URI
Publication date:
30.12.2020.
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