Risk Classification for Claim Counts and Losses Using Regression Models for Location, Scale and Shape
By George Tzougas, Spyridon D. Vrontos, Nickolaos E Frangos
This paper presents and compares different risk classification models for the frequency and severity of claims employing regression models for location, scale and shape. The differences between these models are analyzed through the mean and the variance of the annual number of claims and the costs of claims of the insureds, who belong to different risk classes and interesting results about claiming behavior are obtained. Furthermore, the resulting a priori premiums rates are calculated via the expected value and standard deviation principles with independence between the claim frequency and severity components assumed.
Keywords: Claim frequency, claim severity, regression models for location, scale and shape, a priori risk classification, expected value premium calculation principle, standard deviation premium calculation principle