Modelling the disability severity score in motor insurance claims: an application to the Spanish case

Bodily injury claims have the greatest impact on the claim costs of motor insurance companies. The disability severity of motor claims is assessed in numerous European countries by means of score systems. In this paper a zero inflated generalized Poisson regression model is implemented to estimate the disability severity score of victims in-volved in motor accidents on Spanish roads. We show that the injury severity estimates may be automatically converted into financial terms by insurers at any point of the claim handling process. As such, the methodology described may be used by motor insurers operating in the Spanish market to monitor the size of bodily injury claims. By using insurance data, various applications are presented in which the score estimate of disability severity is of value to insurers, either for computing the claim compensation or for claim reserve purposes.

Mixture of bivariate Poisson regression models with an application to insurance

In a recent paper Bermúdez [2009] used bivariate Poisson regression models for ratemaking in car insurance, and included zero-inflated models to account for the excess of zeros and the overdispersion in the data set. In the present paper, we revisit this model in order to consider alternatives. We propose a 2-finite mixture of bivariate Poisson regression models to demonstrate that the overdispersion in the data requires more structure if it is to be taken into account, and that a simple zero-inflated bivariate Poisson model does not suffice. At the same time, we show that a finite mixture of bivariate Poisson regression models embraces zero-inflated bivariate Poisson regression models as a special case. Additionally, we describe a model in which the mixing proportions are dependent on covariates when modelling the way in which each individual belongs to a separate cluster. Finally, an EM algorithm is provided in order to ensure the models’ ease-of-fit. These models are applied to the same automobile insurance claims data set as used in Bermúdez [2009] and it is shown that the modelling of the data set can be improved considerably.

A priori ratemaking using bivariate poisson regression models

In automobile insurance, it is useful to achieve a priori ratemaking by resorting to gene- ralized linear models, and here the Poisson regression model constitutes the most widely accepted basis. However, insurance companies distinguish between claims with or without bodily injuries, or claims with full or partial liability of the insured driver. This paper exa- mines an a priori ratemaking procedure when including two di®erent types of claim. When assuming independence between claim types, the premium can be obtained by summing the premiums for each type of guarantee and is dependent on the rating factors chosen. If the independence assumption is relaxed, then it is unclear as to how the tari® system might be a®ected. In order to answer this question, bivariate Poisson regression models, suitable for paired count data exhibiting correlation, are introduced. It is shown that the usual independence assumption is unrealistic here. These models are applied to an automobile insurance claims database containing 80,994 contracts belonging to a Spanish insurance company. Finally, the consequences for pure and loaded premiums when the independence assumption is relaxed by using a bivariate Poisson regression model are analysed.

Modelling dependence in a ratemaking procedure with multivariate Poisson regression models

When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.

Estimation of parametric and nonparametric models for univariate claim severity distributions - an approach using R

This paper presents an analysis of motor vehicle insurance claims relating to vehicle damage and to associated medical expenses. We use univariate severity distributions estimated with parametric and non-parametric methods. The methods are implemented using the statistical package R. Parametric analysis is limited to estimation of normal and lognormal distributions for each of the two claim types. The nonparametric analysis presented involves kernel density estimation. We illustrate the benefits of applying transformations to data prior to employing kernel based methods. We use a log-transformation and an optimal transformation amongst a class of transformations that produces symmetry in the data. The central aim of this paper is to provide educators with material that can be used in the classroom to teach statistical estimation methods, goodness of fit analysis and importantly statistical computing in the context of insurance and risk management. To this end, we have included in the Appendix of this paper all the R code that has been used in the analysis so that readers, both students and educators, can fully explore the techniques described

Testing extreme value copulas to estimate the quantile

Testing weather or not data belongs could been generated by a family of extreme value copulas is difficult. We generalize a test and we prove that it can be applied whatever the alternative hypothesis. We also study the effect of using different extreme value copulas in the context of risk estimation. To measure the risk we use a quantile. Our results have motivated by a bivariate sample of losses from a real database of auto insurance claims. Methods are implemented in R.

The role of different factors related to social impact of heavy events: considerations about the intensity thresholds in densely populated areas

In the assessment of social impact caused by meteorological events, factors of different natures need to be considered. Not only does hazard itself determine the impact that a severe weather event has on society, but also other features related to vulnerability and exposure. The requests of data related to insurance claims received in meteorological services proved to be a good indicator of the social impact that a weather event causes, according to studies carried out by the Social Impact Research Group, created within the framework of the MEDEX project. Taking these requests as proxy data, diverse aspects connected to the impact of heavy rain events have been studied. The rainfall intensity, in conjunction with the population density, has established itself as one of the key factors in social impact studies. One of the conclusions we obtained is that various thresholds of rainfall should be applied for areas of varying populations. In this study, the role of rainfall intensity has been analysed for a highly populated urban area like Barcelona. A period without significant population changes has been selected for the study to minimise the effects linked to vulnerability and exposure modifications. First, correlations between rainfall recorded in different time intervals and requests were carried out. Afterwards, a method to include the intensity factor in the social impact index was suggested based on return periods given by intensity duration frequency (IDF) curves.

Determinants of the decision to appeal against motor bodily injury settlements awarded by Spanish trial courts

Automobile bodily injury disputes represent one of the main causes of litigation faced by Spanish Courts. In this paper a multinomial model is implemented to analyse which factors determine the decision to appeal against the verdicts of trial courts. Use of a dataset of motor insurance claims revealed differences between the determinants of a claimant’s decision to appeal and those of insurers. Among other results it is shown that discrepancies regarding the permanent disability sustained affect the insurer’s decision to appeal. In contrast, the claimant pays more attention to differences in the stated temporary disability. Conclusions are drawn regarding which factors could reduce the percentage of appealed cases.

The aggregate claims distribution of a life insurance portfolio with a pairwise positive dependence structure

The individual life model has always been considered as the one closest to the real situation of the total claims of a life insurance portfolio. It only makes the ¿nearly inevitable assumption¿ of independence of the lifelenghts of insured persons in the portfolio. Many clinical studies, however, have demonstrated positive dependence of paired lives such as husband and wife. In our opinion, it won¿t be unrealistic expecting a considerable number of married couples in any life insurance portfolio (e.g. life insurance contracts formalized at the time of signing a mortatge) and these dependences materially increase the values for the stop-loss premiums associated to the aggregate claims of the portfolio. Since the stop-loss order is the order followed by any risk averse decison maker, the simplifying hypothesis of independence constitute a real financial danger for the company, in the sense that most of their decisions are based on the aggregated claims distribution. In this paper, we will determine approximations for the distribution of the aggregate claims of a life insurance portfolio with some married couples and we will describe how to make safe decisions when we don¿t know exactly the dependence structure between the risks in each couple. Results in this paper are partly based on results in Dhaene and Goovaerts (1997)

The aggregate claims distribution of a life insurance portfolio with a pairwise positive dependence structure

The individual life model has always been considered as the one closest to the real situation of the total claims of a life insurance portfolio. It only makes the ¿nearly inevitable assumption¿ of independence of the lifelenghts of insured persons in the portfolio. Many clinical studies, however, have demonstrated positive dependence of paired lives such as husband and wife. In our opinion, it won¿t be unrealistic expecting a considerable number of married couples in any life insurance portfolio (e.g. life insurance contracts formalized at the time of signing a mortatge) and these dependences materially increase the values for the stop-loss premiums associated to the aggregate claims of the portfolio. Since the stop-loss order is the order followed by any risk averse decison maker, the simplifying hypothesis of independence constitute a real financial danger for the company, in the sense that most of their decisions are based on the aggregated claims distribution. In this paper, we will determine approximations for the distribution of the aggregate claims of a life insurance portfolio with some married couples and we will describe how to make safe decisions when we don¿t know exactly the dependence structure between the risks in each couple. Results in this paper are partly based on results in Dhaene and Goovaerts (1997)

An Introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions

We present a real data set of claims amounts where costs related to damage are recorded separately from those related to medical expenses. Only claims with positive costs are considered here. Two approaches to density estimation are presented: a classical parametric and a semi-parametric method, based on transformation kernel density estimation. We explore the data set with standard univariate methods. We also propose ways to select the bandwidth and transformation parameters in the univariate case based on Bayesian methods. We indicate how to compare the results of alternative methods both looking at the shape of the overall density domain and exploring the density estimates in the right tail.

Loss risk through fraud in car insurance

Our objective is to analyse fraud as an operational risk for the insurance company. We study the effect of a fraud detection policy on the insurer's results account, quantifying the loss risk from the perspective of claims auditing. From the point of view of operational risk, the study aims to analyse the effect of failing to detect fraudulent claims after investigation. We have chosen VAR as the risk measure with a non-parametric estimation of the loss risk involved in the detection or non-detection of fraudulent claims. The most relevant conclusion is that auditing claims reduces loss risk in the insurance company.

Individual prediction of automobile bodily injury claims liabilities

[spa] La mayoría de siniestros con daños corporales se liquidan mediante negociación, llegando a juicio menos del 5% de los casos. Una estrategia de negociación bien definida es, por tanto, fundamental para las compañías aseguradoras. En este artículo asumimos que la compensación monetaria concedida en juicio es la máxima cuantía que debería ser ofrecida por el asegurador en el proceso de negociación. Usando una base de datos real, implementamos un modelo log-lineal para estimar la máxima oferta de negociación. Perturbaciones no-esféricas son detectadas. Correlación ocurre cuando más de una siniestro se liquida en la misma sentencia judicial. Heterocedasticidad por grupos se debe a la influencia de la valoración del forense en la indemnización final.

Individual prediction of automobile bodily injury claims liabilities

[spa] La mayoría de siniestros con daños corporales se liquidan mediante negociación, llegando a juicio menos del 5% de los casos. Una estrategia de negociación bien definida es, por tanto, fundamental para las compañías aseguradoras. En este artículo asumimos que la compensación monetaria concedida en juicio es la máxima cuantía que debería ser ofrecida por el asegurador en el proceso de negociación. Usando una base de datos real, implementamos un modelo log-lineal para estimar la máxima oferta de negociación. Perturbaciones no-esféricas son detectadas. Correlación ocurre cuando más de una siniestro se liquida en la misma sentencia judicial. Heterocedasticidad por grupos se debe a la influencia de la valoración del forense en la indemnización final.

Forecasting the maximum compensation offer in the automobile BI claims negotiation process

Most motor bodily injury (BI) claims are settled by negotiation, with fewer than 5% of cases going to court. A well-defined negotiation strategy is thus very useful for insurance companies. In this paper we assume that the monetary compensation awarded in court is the upper amount to be offered by the insurer in the negotiation process. Using a real database, a log-linear model is implemented to estimate the maximal offer. Non-spherical disturbances are detected. Correlation occurs when various claims are settled in the same judicial verdict. Group wise heteroscedasticity is due to the influence of the forensic valuation on the final compensation amount. An alternative approximation based on generalized inference theory is applied to estimate confidence intervals on variance components, since classical interval estimates may be unreliable for datasets with unbalanced structures.