The present material is written for students enrolled in actuarial master programs and
practicing actuaries, who would like to gain a better understanding of insurance data …

Boosting techniques and neural networks are particularly effective machine learning
methods for insurance pricing. Often in practice, the sum of fitted values can depart from the …

The Gini index does not give a strictly consistent scoring function. Therefore, simply
maximizing the Gini index may lead to a wrong model choice. The main issue is that the Gini …

Wüthrich (Eur Actuar J, https://doi. org/10.1007/s13385-022-00339-9, 2023) established that
the Gini index is a consistent scoring rule in the class of autocalibrated predictors. This note …

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity
and frequency models using covariates to explain the claims loss exposure. In this paper …

This paper purposes to formalize the three business models dominating peer‐to‐peer (P2P)
property and casualty insurance: the self‐governing model, the broker model, and the carrier …

By exploiting massive amounts of data, machine learning techniques provide actuaries with
predictors exhibiting high correlation with claim frequencies and severities. However, these …

Autocalibration is a desirable property since it ensures that the information contained in a
candidate premium is used without any bias. It turns out to be intimately related to the …

Actuaries use predictive modeling techniques to assess the loss cost on a contract as a
function of observable risk characteristics. State-of-the-art statistical and machine learning …

Pricing an insurance product covering motor third-party liability is a major challenge for
actuaries. Comprehensive statistical modelling and modern computational power are …