Estimating Total Claim Size in the Auto Insurance Industry: a Comparison between Tweedie and Zero-Adjusted Inverse Gaussian Distribution

Abstract

The objective of this article is to estimate insurance claims from an auto dataset using the Tweedie and zero-adjusted inverse Gaussian (ZAIG) methods. We identify factors that influence claim size and probability, and compare the results of these methods which both forecast outcomes accurately. Vehicle characteristics like territory, age, origin and type distinctly influence claim size and probability. This distinct impact is not always present in the Tweedie estimated model. Auto insurers should consider estimating total claim size using both the Tweedie and ZAIG methods. This allows for an estimation of confidence interval based on empirical quantiles using bootstrap simulation. Furthermore, the fitted models may be useful in developing a strategy to obtain premium pricing.

Keywords

• auto insurance
• claim size
• regression
• Tweedie
• ZAIG mater.