RISK FACTORS SELECTION WITH DATA MINING METHODS FOR INSURANCE PREMIUM RATEMAKING
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 acc...
Main Authors: | , |
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Format: | Article |
Language: | deu |
Published: |
Faculty of Economics University of Rijeka
2020-12-01
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Series: | Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu |
Subjects: | |
Online Access: | https://www.efri.uniri.hr/upload/Zbornik%202_2020/12-Omerasevic_et_al-2020-2.pdf |
Summary: | 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 nonlife insurance premium pricing today in most developed insurance markets in the
world |
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ISSN: | 1331-8004 1846-7520 |