Auto Insurance Premium Calculation Using Generalized Linear Models

David, Mihaela

doi:10.1016/s2212-5671(15)00059-3

Cited by 46 publications

(38 citation statements)

References 12 publications

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“…Results have been shown that the reduction of pure premium is observed together with the increase in age of the insured person, the age of insurance contract, and the growth of bonus coefficient-Malus [17]. A study was done by [30] found that there are nine rating factors were suggested.…”

Section: Rating Factors In Calculating Premiummentioning

confidence: 99%

“…The second issue is related to the complexity of statistical analysis, which has become more apparent. Due to this problem, actuaries had to solve the problem of finding a model that can explain the event of risk realistically [16] [17] and a model that able to handle complex problems in exploiting varying information [18]. In this context, Multiple Linear Regression (MLR), is used to evaluate the impact of additional rating factor (besides the current factors set up by PIAM) on the premium calculation.…”

Section: Introductionmentioning

confidence: 99%

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Impact De-Tarrification in Modeling Motor Insurance Premium in Malaysia

Manan*¹,

Hasan²,

Hasan³

et al. 2019

IJRTE

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De-tariffication has become a hot topic for Malaysian motor insurers after effectively implemented on 1 July 2017. Generally, the insurance companies need to set a rating factor for their premium before calculating the price on selected premium. Typically, these rating factors are based on the risk profile of the policyholder. That means, the price of the premium determined by the risk factors from the profile of the policyholder. The aim of this research to investigate the impact after de-tariff implemented among the motor insurance industry. This research also investigates the effect of de-tariff on the Good Service Tax (GST) in the premium calculation. Multiple Linear Regression (MLR) was used to determine the most significant rating factor that influence the calculation of the premium received by the motor insurance industry. Once these k rating factors and parameters are identified, the value of premiums can be calculated by taking into account these rating factors and parameters in the de-tariff formula and comparing with the existing model. The result of the study indicates that de-tariff model has lower premium compared to Malaysia tariff model. Furthermore, GST is also found to have a significant impact on the motor insurance premium, where policyholders are required to pay higher premiums than non-GST premiums. The results will help the insurance companies to find new formulas in considering new rating factors and improve the accuracy of premium calculations.

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Section: Rating Factors In Calculating Premiummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact De-Tarrification in Modeling Motor Insurance Premium in Malaysia

Manan*¹,

Hasan²,

Hasan³

et al. 2019

IJRTE

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show abstract

“…But the coefficients of auto burden index and driving areas did not pass the significance test in the two distributions, which requires to be further analyzed. Mihaela David (2015) used the Gamma distribution to fit the claim costs and its influencing factors. Judging from the fitting results of the inverse Gaussian distribution and Gamma distribution, most p-values of parameter estimation in Gamma distribution are smaller than that of the inverse Gaussian distribution, indicating that the fitting result of Gamma distribution is relatively better.…”

Section: Loss Distribution Of Claim Costmentioning

confidence: 99%

A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform

Duan

Chang

Wang

et al. 2018

IJFS

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Abstract:Using a generalized linear model to determine the claim frequency of auto insurance is a key ingredient in non-life insurance research. Among auto insurance rate-making models, there are very few considering auto types. Therefore, in this paper we are proposing a model that takes auto types into account by making an innovative use of the auto burden index. Based on this model and data from a Chinese insurance company, we built a clustering model that classifies auto insurance rates into three risk levels. The claim frequency and the claim costs are fitted to select a better loss distribution. Then the Logistic Regression model is employed to fit the claim frequency, with the auto burden index considered. Three key findings can be concluded from our study. First, more than 80% of the autos with an auto burden index of 20 or higher belong to the highest risk level. Secondly, the claim frequency is better fitted using the Poisson distribution, however the claim cost is better fitted using the Gamma distribution. Lastly, based on the AIC criterion, the claim frequency is more adequately represented by models that consider the auto burden index than those do not. It is believed that insurance policy recommendations that are based on Generalized linear models (GLM) can benefit from our findings.

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“…(David, 2015) and (Duan et al, 2018)). As David (2015) indicates, generalized linear models allow for the modelling of a non-linear behaviour and a non-Gaussian distribution of residuals, which is very useful for the analysis of non-life insurance, where claim frequency and claim cost follow an asymmetric density, which is clearly non-Gaussian. A special case of generalized linear model (GzLM) is the general linear model (GLM), which we use in the article to assess the impact of relevant factors on claim severity.…”

Section: Introductionmentioning

confidence: 99%

“…The Poisson regression model is frequently used to model claim frequency and the Gamma regression model is used to model claim costs (see, e.g. (David, 2015) and (Duan et al, 2018)). As David (2015) indicates, generalized linear models allow for the modelling of a non-linear behaviour and a non-Gaussian distribution of residuals, which is very useful for the analysis of non-life insurance, where claim frequency and claim cost follow an asymmetric density, which is clearly non-Gaussian.…”

Section: Introductionmentioning

confidence: 99%

General Linear Model: An Effective Tool for Analysis of Claim Severity in Motor Third Party Liability Insurance

Šoltés

Zelinová

Bilíková

2019

Statistics in Transition New Series

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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

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Auto Insurance Premium Calculation Using Generalized Linear Models

Cited by 46 publications

References 12 publications

Impact De-Tarrification in Modeling Motor Insurance Premium in Malaysia

Impact De-Tarrification in Modeling Motor Insurance Premium in Malaysia

A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform

General Linear Model: An Effective Tool for Analysis of Claim Severity in Motor Third Party Liability Insurance

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