  Due to confidentiality issues, it can be difficult to access or share
interesting datasets for methodological development in actuarial science, or
other fields where personal data are important. We show how to design three
different types of generative adversarial networks (GANs) that can build a
synthetic insurance dataset from a confidential original dataset. The goal is
to obtain synthetic data that no longer contains sensitive information but
still has the same structure as the original dataset and retains the
multivariate relationships. In order to adequately model the specific
characteristics of insurance data, we use GAN architectures adapted for
multi-categorical data: a Wassertein GAN with gradient penalty (MC-WGAN-GP), a
conditional tabular GAN (CTGAN) and a Mixed Numerical and Categorical
Differentially Private GAN (MNCDP-GAN). For transparency, the approaches are
illustrated using a public dataset, the French motor third party liability
data. We compare the three different GANs on various aspects: ability to
reproduce the original data structure and predictive models, privacy, and ease
of use. We find that the MC-WGAN-GP synthesizes the best data, the CTGAN is the
easiest to use, and the MNCDP-GAN guarantees differential privacy.
