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Wyświetlanie 1-3 z 3
Tytuł:
General linear model – an effective tool for analysis of claim severity in motor third party liability insurance
Autorzy:
Šoltés, Erik
Zelinová, Silvia
Bilíková, Mária
Powiązania:
https://bibliotekanauki.pl/articles/1186927.pdf
Data publikacji:
2019-12-10
Wydawca:
Główny Urząd Statystyczny
Tematy:
general linear model
claim severity
motor third party liability insurance
least squares means
Opis:
The paper focuses on the analysis of claim severity in motor third party liability insurance under the general linear model. The general linear model combines the analyses of variance and regression and makes it possible to measure the influence of categorical factors as well as the numerical explanatory variables on the target variable. In the paper, simple, main and interaction effects of relevant factors have been quantified using estimated regression coefficients and least squares means. Statistical inferences about least-squares means are essential in creating tariff classes and uncovering the impact of categorical factors, so the authors used the LSMEANS, CONTRAST and ESTIMATE statements in the GLM procedure of the Statistical Analysis Software (SAS). The study was based on a set of anonymised data of an insurance company operating in Slovakia; however, because each insurance company has its own portfolio subject to changes over time, the results of this research will not apply to all insurance companies. In this context, the authors feel that what is most valuable in their work, is the demonstration of practical applications that could be used by actuaries to estimate both the claim severity and the claim frequency, and, consequently, to determine net premiums for motor insurance (regardless of whether for motor third party liability insurance or casco insurance.
Źródło:
Statistics in Transition new series; 2019, 20, 4; 13-31
1234-7655
Pojawia się w:
Statistics in Transition new series
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Segmentation and estimation of claim severity in motor third-party liability insurance through contrast analysis
Autorzy:
Reiff, Marian
Šoltés, Erik
Komara, Silvia
Šoltésová, Tatiana
Zelinová, Silvia
Powiązania:
https://bibliotekanauki.pl/articles/22443156.pdf
Data publikacji:
2022
Wydawca:
Instytut Badań Gospodarczych
Tematy:
general linear model
claim severity
motor third party liability insurance
least squares means
contrast analysis
Opis:
Research background: Using the marginal means and contrast analysis of the target variable, e.g., claim severity (CS), the actuary can perform an in-depth analysis of the portfolio and fully use the general linear models potential. These analyses are mainly used in natural sciences, medicine, and psychology, but so far, it has not been given adequate attention in the actuarial field. Purpose of the article: The article's primary purpose is to point out the possibilities of contrast analysis for the segmentation of policyholders and estimation of CS in motor third-party liability insurance. The article focuses on using contrast analysis to redefine individual relevant factors to ensure the segmentation of policyholders in terms of actuarial fairness and statistical correctness. The aim of the article is also to reveal the possibilities of using contrast analysis for adequate segmentation in case of interaction of factors and the subsequent estimation of CS. Methods: The article uses the general linear model and associated least squares means. Contrast analysis is being implemented through testing and estimating linear combinations of model parameters. Equations of estimable functions reveal how to interpret the results correctly. Findings & value added: The article shows that contrast analysis is a valuable tool for segmenting policyholders in motor insurance. The segmentation's validity is statistically verifiable and is well applicable to the main effects. Suppose the significance of cross effects is proved during segmentation. In that case, the actuary must take into account the risk that even if the partial segmentation factors are set adequately, statistically proven, this may not apply to the interaction of these factors. The article also provides a procedure for segmentation in case of interaction of factors and the procedure for estimation of the segment's CS. Empirical research has shown that CS is significantly influenced by weight, engine power, age and brand of the car, policyholder's age, and district. The pattern of age's influence on CS differs in different categories of car brands. The significantly highest CS was revealed in the youngest age category and the category of luxury car brands.
Źródło:
Equilibrium. Quarterly Journal of Economics and Economic Policy; 2022, 17, 3; 803-842
1689-765X
2353-3293
Pojawia się w:
Equilibrium. Quarterly Journal of Economics and Economic Policy
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Claim modeling and insurance premium pricing under a bonus-malus system in motor insurance
Autorzy:
Ieosanurak, Weenakorn
Khomkham, Banphatree
Moumeesri, Adisak
Powiązania:
https://bibliotekanauki.pl/articles/24987758.pdf
Data publikacji:
2023
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
bonus malus system
claim severity
exponential-GaL distribution
motor insurance
number of claims
Poisson-GaL distribution
system bonus malus
rozkład wykładniczy
ubezpieczenie komunikacyne
Opis:
Accurately modeling claims data and determining appropriate insurance premiums are vital responsibilities for non-life insurance firms. This article presents novel models for claims that offer improved precision in fitting claim data, both in terms of claim frequency and severity. Specifically, we suggest the Poisson-GaL distribution for claim frequency and the exponential-GaL distribution for claim severity. The traditional method of assigning automobile premiums based on a bonus-malus system relies solely on the number of claims made. However, this may lead to unfair outcomes when an insured individual with a minor severity claim is charged the same premium as someone with a severe claim. The second aim of this article is to propose a new model for calculating bonus-malus premiums. Our proposed model takes into account both the number and size of claims, which follow the Poisson-GaL distribution and the exponential-GaL distribution, respectively. To calculate the premiums, we employ the Bayesian approach. Real-world data are used in practical examples to illustrate how the proposed model can be implemented. The results of our analysis indicate that the proposed premium model effectively resolves the issue of overcharging. Moreover, the proposed model produces premiums that are more tailored to policyholders’ claim histories, benefiting both the policyholders and the insurance companies. This advantage can contribute to the growth of the insurance industry and provide a competitive edge in the insurance market.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2023, 33, 4; 637--650
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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