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 state-of-the-art neural network techniques. We evaluate the predictive modeling efficacy of datasets synthesized from publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling . The trained synthesizers are able to capture representative characteristics of the real datasets . This workflow is implemented via an R interface to promote adoption by researchers and data owners.
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.
R
state-of-the-art neural network techniques. We evaluate the predictive modeling efficacy of datasets synthesized from
CTGAN, a recently proposed neural network architecture for generating tabular data. Applying the proposed workflow to
R
. The trained synthesizers are able to capture representative characteristics of the real datasets
, we evaluate the synthesized datasets from a few perspectives: machine learning efficacy, distributions of variables, and stability of model parameters