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 …

We give a survey on the field of telematics car driving data research in actuarial science. We
describe and discuss telematics car driving data, we illustrate the difficulties of telematics …

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 …

Unstructured data are a promising new source of information that insurance companies may
use to understand their risk portfolio better and improve the customer experience. However …

There is evidence that the driving style of an autonomous vehicle is important to increase the
acceptance and trust of the passengers. The driving situation has been found to have a …

Handling nominal covariates with a large number of categories is challenging for both
statistical and machine learning techniques. This problem is further exacerbated when the …

Telematics data provide real-time information that can be used to develop new insurance
pricing models in all lines, ranging from motor insurance to homeowners and health …

We propose, in this paper, a comprehensive study on risk assessment related to car
insurance, based on claim and telematics data, collected from a dataset of voluntary drivers …

This chapter considers convolutional neural (CN) networks. These are special network
architectures that are useful for time-series and spatial data modeling, eg, applied to image …

Machine learning and deep learning present a set of powerful modeling approaches, that in
recent years have shown significant improvements across different fields of application. In …