  The quality of generalized linear models (GLMs), frequently used by insurance
companies, depends on the choice of interacting variables. The search for
interactions is time-consuming, especially for data sets with a large number of
variables, depends much on expert judgement of actuaries, and often relies on
visual performance indicators. Therefore, we present an approach to automating
the process of finding interactions that should be added to GLMs to improve
their predictive power. Our approach relies on neural networks and a
model-specific interaction detection method, which is computationally faster
than the traditionally used methods like Friedman H-Statistic or SHAP values.
In numerical studies, we provide the results of our approach on artificially
generated data as well as open-source data.
