Papers › Generative Synthesis of Insurance Datasets
Generative Synthesis of Insurance Datasets
Kevin Kuo
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections