This open access book discusses the statistical modeling of insurance problems, a process
which comprises data collection, data analysis and statistical model building to forecast …

A main difficulty in actuarial claim size modeling is that covariates may have different effects
on the body of the conditional distribution and on its tail. To cope with this problem, we …

We discuss how mixtures of Gamma distributions with mixing probabilities, shape and rate
parameters depending on features can be fitted with neural networks. We develop two …

The task of modeling claim severities is addressed when data is not consistent with the
classical regression assumptions. This framework is common in several lines of business …

This paper addresses the task of modeling severity losses using segmentation when the
data distribution does not fall into the usual regression frameworks. This situation is not …

In property insurance claims triage, insurers often use static information to assess the
severity of a claim and to identify the subsequent actions. We hypothesize that the pattern of …

Insurance claim severity data are characterized by complex distributional phenomenons,
where flexible density estimation tools such as the finite mixture models (FMM) are …

ABSTRACT A well-designed framework for risk classification and ratemaking in automobile
insurance is key to insurers' profitability and risk management, while also ensuring that …

A key task in actuarial modelling involves modelling the distributional properties of losses.
Classic (distributional) regression approaches like Generalized Linear Models (GLMs; …

The logistic regression model is one of the most powerful statistical methods for the analysis
of binary data. Logistic regression allows using a set of covariates to explain the binary …