The claim frequency examines the number of claims and the average claim severity takes account of the average amount of claims conditional on occurence. The frequency-severity model is a standard model of insurance claims, which separately models the claim frequency and average claim severity. Thus, an accurate model of insurance claims is significant to competency and profits of an insurer. In contrary, the overestimation can reduce liquid capital of the insurer and then hamper business expansion. The underestimation of loss can make the insurer not hold enough risk capital and hence raise bankruptcy risk. Further, the model helps the insurer determine a suitable level of risk capital. Then, the insurer loses profitable and gain underpriced policies, both resulting in economic losses.
#PARAMETRE GRABIT DRIVERS#
For instance, Dionne, Gouriéroux, and Vanasse point out that in auto insurance, if an insurer charges too little for young drivers and too much for old drivers, young drivers will be attracted while old drivers will switch to competitors.
It is important to charge the policyholder with a fair premium. Specifically, the model enables an insurer to set a fair premium for each individual policy. The model helps an insurer accurately estimate potential loss and make appropriate actuarial decisions. Insurance claims modeling is a topic of great concern in non-life insurance. The results show that our model is superior to other state-of-the-art models. Then, we demonstrate the application of our model with a French auto insurance claim data. A simulation study shows excellent prediction performance of our model. The model can flexibly capture the nonlinear relation between the claim frequency (severity) and predictors and complex interactions among predictors and can fully capture the nonlinear dependence between the claim frequency and severity. To overcome restrictions of linear or additive forms and to relax the independence assumption, we develop a data-driven dependent frequency-severity model, where we combine a stochastic gradient boosting algorithm and a profile likelihood approach to estimate parameters for both of the claim frequency and average claim severity distributions, and where we introduce the dependence between the claim frequency and severity by treating the claim frequency as a predictor in the regression model for the average claim severity. The standard GLM and GAM frequency-severity models assume independence between the claim frequency and severity.
0 kommentar(er)
