Dependent frequency–severity modeling of insurance claims

Shi, Peng; Feng, Xiaoping; Ivantsova, Anastasia

doi:10.1016/j.insmatheco.2015.07.006

Cited by 72 publications

(56 citation statements)

References 29 publications

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“…For another approach, Shi et al also built a dependence model between frequency and severity in [55]. They used an extra indicator variable for occurrence of claim to deal with the zero-inflated part, and built a dependence model between frequency and severity conditional on positive claim.…”

Section: Frequency Severity Dependency Modelsmentioning

confidence: 99%

Multivariate Frequency-Severity Regression Models in Insurance

Frees

Lee

Yang

2016

Risks

107

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Abstract:In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one's own vehicle, damage to another party's vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i) property; (ii) motor vehicle; and (iii) contractors' equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.

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Section: Frequency Severity Dependency Modelsmentioning

confidence: 99%

Multivariate Frequency-Severity Regression Models in Insurance

Frees

Lee

Yang

2016

Risks

107

View full text Add to dashboard Cite

show abstract

“…Multiple Linear Regression (MLR) is a statistical methods that quantify the relationship between a dependent variable and a set of independent variables [36][17][10][11] [12]. This method takes into account the correlations between predictor variables and assesses the effect of each predictor variable when other variables are removed [37].…”

Section: B Multiple Linear Regression Model To Identify Rating Factomentioning

confidence: 99%

“…Little research has been carried out in the area of the implementation of this de-tarrification in the motor insurance industry. There are several study has been carried out to identify new rating factor for their new set up premium [1][2][3][4][5][6][7][8][9][10][11][12], but little study on the impact of de-tarrification in the motor insurance company. The implementation of de-tariffication may be different from tariff (i.e., Malaysian Model), especially when facing Good Service Tax (GST).…”

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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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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“…Methods that are appropriate in the case of correlation between frequency and severity components are dealt with by, e.g. Shi et al (2015). The above facts motivated us to consider a separate modelling, so the paper focuses only on the claim severity in motor third party liability (MTPL) insurance.…”

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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Dependent frequency–severity modeling of insurance claims

Cited by 72 publications

References 29 publications

Multivariate Frequency-Severity Regression Models in Insurance

Multivariate Frequency-Severity Regression Models in Insurance

Impact De-Tarrification in Modeling Motor Insurance Premium in Malaysia

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

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