  Since May 2018, the General Data Protection Regulation (GDPR) has introduced
new obligations to industries. By setting a legal framework, it notably imposes
strong transparency on the use of personal data. Thus, people must be informed
of the use of their data and must consent the usage of it. Data is the raw
material of many models which today make it possible to increase the quality
and performance of digital services. Transparency on the use of data also
requires a good understanding of its use through different models. The use of
models, even if efficient, must be accompanied by an understanding at all
levels of the process that transform data (upstream and downstream of a model),
thus making it possible to define the relationships between the individual's
data and the choice that an algorithm could make based on the analysis of the
latter. (For example, the recommendation of one product or one promotional
offer or an insurance rate representative of the risk.) Models users must
ensure that models do not discriminate against and that it is also possible to
explain its result. The widening of the panel of predictive algorithms - made
possible by the evolution of computing capacities -- leads scientists to be
vigilant about the use of models and to consider new tools to better understand
the decisions deduced from them . Recently, the community has been particularly
active on model transparency with a marked intensification of publications over
the past three years. The increasingly frequent use of more complex algorithms
(\textit{deep learning}, Xgboost, etc.) presenting attractive performances is
undoubtedly one of the causes of this interest. This article thus presents an
inventory of methods of interpreting models and their uses in an insurance
context.
