Estimating Claim Size and Probability in the Auto-insurance Industry: the Zero-adjusted Inverse Gaussian (ZAIG) Distribution

Abstract

This article aims at the estimation of insurance claims from an auto data set. Using a ZAIG method, we identify factors that influence claim size and probability, and compared the results with the analysis of a Tweedie method. Results show that ZAIG can accurately predict claim size and probability. Factors like territory, vehicles´ advanced age, origin and body influence distinctly claim size and probability. The distinct impact is not always present in Tweedie’s estimated model. Auto insurers should consider estimating risk premium using ZAIG method. The fitted models may be useful to develop a strategy for premium pricing.