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

Duan, Zhengmin; Chang, Yonglian; Wang, Qi; Chen, Tianyao; Zhao, Qing

doi:10.3390/ijfs6010018

Cited by 8 publications

(11 citation statements)

References 15 publications

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“…The second paper entitled "Risk Culture during the Last 2000 Years-From an Aleatory Society to the Illusion of Risk Control" by Milkau (2017) studies the history of the risk dated back to the Roman "Aleatory Society" and its up-to-date developments. The third paper entitled "A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform" by Duan et al (2018) investigates auto insurance ratemaking models by making use of the auto burden index. Based on data from a Chinese insurance company, authors built a clustering model that classifies auto insurance rates into three risk levels.…”

Section: Papersmentioning

confidence: 99%

Editorial for Special Issue “Finance, Financial Risk Management and their Applications”

Chan

2018

IJFS

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We are pleased to announce the Special Issue on the Finance, Financial Risk Management and their Applications in the International Journal of Financial Studies. This Special Issue collects papers pertaining to several lines of research related to finance and financial risks. This Guest Editor’s note synthesizes the contributing authors’ propositions and findings regarding these developments and hopes that new areas can be opened for future researches.

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Section: Papersmentioning

confidence: 99%

Editorial for Special Issue “Finance, Financial Risk Management and their Applications”

Chan

2018

IJFS

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“…In Rempala and Derrig (2005), a finite mixture model as a clustering technique was proposed to study the claim severity in problems where data imputation is needed. More recently, in Duan et al (2018), clustering was used to identify the low-and high-risk class of policyholders in an insurance company before a logistic regression model can be applied for risk quantification. Clustering was also used for statistical analysis of geographical insurance data in the USA, where zip codes were used as an atomic geographical rating unit Peck and Kuan (1983).…”

Section: Introductionmentioning

confidence: 99%

Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering

Xie

2019

Risks

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Territory design and analysis using geographical loss cost are a key aspect in auto insurance rate regulation. The major objective of this work is to study the design of geographical rating territories by maximizing the within-group homogeneity, as well as maximizing the among-group heterogeneity from statistical perspectives, while maximizing the actuarial equity of pure premium, as required by insurance regulation. To achieve this goal, the spatially-constrained clustering of industry level loss cost was investigated. Within this study, in order to meet the contiguity, which is a legal requirement on the design of geographical rating territories, a clustering approach based on Delaunay triangulation is proposed. Furthermore, an entropy-based approach was introduced to quantify the homogeneity of clusters, while both the elbow method and the gap statistic are used to determine the initial number of clusters. This study illustrated the usefulness of the spatially-constrained clustering approach in defining geographical rating territories for insurance rate regulation purposes. The significance of this work is to provide a new solution for better designing geographical rating territories. The proposed method can be useful for other demographical data analysis because of the similar nature of the spatial constraint.

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

Cited by 8 publications

References 15 publications

Editorial for Special Issue “Finance, Financial Risk Management and their Applications”

Editorial for Special Issue “Finance, Financial Risk Management and their Applications”

Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering

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

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