  One of the impediments in advancing actuarial research and developing open
source assets for insurance analytics is the lack of realistic publicly
available datasets. In this work, we develop a workflow for synthesizing
insurance datasets leveraging CTGAN, a recently proposed neural network
architecture for generating tabular data. Applying the proposed workflow to
publicly available data in the domains of general insurance pricing and life
insurance shock lapse modeling, we evaluate the synthesized datasets from a few
perspectives: machine learning efficacy, distributions of variables, and
stability of model parameters. This workflow is implemented via an R interface
to promote adoption by researchers and data owners.
