Template-Type: ReDIF-Paper 1.0
Author-Name:  Adriana Bruscato Bortoluzzo
Author-Name-First: Adriana Bruscato
Author-Name-Last: Bortoluzzo
Author-Name:  Danny Pimentel Claro
Author-Name-First: Danny Pimentel
Author-Name-Last: Claro
Author-Name: Marco Antonio Leonel Caetano
Author-Name-First: Marco Antonio Leonel
Author-Name-Last: Caetano
Author-Name: Rinaldo Artes
Author-Name-First: Rinaldo
Author-Name-Last: Artes
Title: 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.
Length:  16 pages
Creation-Date:  2009
Order-URL: https://repositorio.insper.edu.br/handle/11224/5759
File-URL: https://repositorio.insper.edu.br/handle/11224/5759
File-Format: text/html
File-Function: Full text
Number: 056
Handle: RePEc:aap:wpaper:056