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 zeroadjusted 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.

A Logistic Regression Based Auto Insurance Rate-Making Model Designed for the Insurance Rate Reform

“…Based on the new regulations of the reform, we used the data of an insurance company in Chongqing, China, with a total of 33,373 sets of insurance policies, ensuring the authenticity and effectiveness in the analysis. While Adriana Bruscato Bortoluzzo (2011) classified the auto types into luxury, medium and small with an index respectively. In this article, we introduce the auto burden index into the model to precisely quantify the auto types, transforming the auto types into specific values, which is described by the formula, Single commonly used accessories price × accessories loss rate ÷ auto sales price × 100…”

“…In this article, y is equals to 1 if the event occurs and 0 otherwise, p denotes the probability that the policy will be claimed. Adriana Bruscato Bortoluzzo (2011) pointed out that the claim probability is more convincing than the claim size. Therefore, this article uses the Logistic Regression model to predict the claim probability.…”

A nonparametric data mining approach for risk prediction in car insurance: a case study from the Montenegrin market

“…claim probability) and linear regression model for the mean claim size. GLMs and more flexible Tweedie's compound Poisson models are often used to construct insurance tariffs (Bortoluzzo et al, 2011;Ohlsson & Johansson, 2010). However, even these more general models still can yield problems in modelling high-dimensional relationships which is quite common for insurance data-sets.…”

“…The generalised linear models (GLS) and other more flexible stochastic models are used in recent studies to predict insurance tariffs on a micro-level, i.e., on level of individual claims (Bortoluzzo, Claro, Caetano, & Artes, 2011;David, 2015;Ohlsson & Johansson, 2010). For these models a major limitation is that the structure is restricted to a linear form, which can be too rigid for real applications.…”

Modelos De Poisson Inflada De Zeros E Binomial Negativa Inflada De Zeros Na Previsão De Sinistro De Automóveis

“…Considerando as características do ambiente, observou-se que carteiras de sinistros de automóveis com maior quantidade de automóveis expostos possuem maior probabilidade e maior número esperado de sinistros; quanto maior a importância segurada média da carteira, ou seja, carteira caracterizada por mais carros de luxo ou mais caros, menor o risco, ou seja, menor a quantidade de sinistros esperada; automóveis da região Sul apresentam menor risco. Estes resultados são semelhantes aos encontrados por Filho e Lugon (2004), Boucher et al (2009), Bortoluzzo et al (2011) e MARTINS (2012).…”