In the past 25 years, computer scientists and statisticians developed machine learning
algorithms capable of modeling highly nonlinear transformations and interactions of input …

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 …

The logit-weighted reduced mixture of experts model (LRMoE) is a flexible yet analytically
tractable non-linear regression model. Though it has shown usefulness in modeling …

The aim of this article is to present a mixture composite regression model for claim severity
modeling. Claim severity modeling poses several challenges such as multimodality, tail …

Claim reserving is primarily accomplished using macro-level or aggregate models, with the
Chain-Ladder method being the most popular one. However, these methods are …

The projection of outstanding liabilities caused by incurred losses or claims has played a
fundamental role in general insurance operations. Loss reserving methods based on …

Telematicsdevices installed in insured vehicles provide actuaries with new risk factors, such
as the time of the day, average speeds, and other driving habits. This paper extends the …

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 …

This paper introduces a new julia package, LRMoE, a statistical software tailor-made for
actuarial applications, which allows actuarial researchers and practitioners to model and …