# Generative Synthesis of Insurance Datasets

arXiv: https://arxiv.org/abs/1912.02423 · HTML: https://mlsanity.com/paper/1912.02423/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [LocalGLMnet: interpretable deep learning for tabular data](https://mlsanity.com/paper/2107.11059/) (2021)
- [Row Conditional-TGAN for generating synthetic relational databases](https://mlsanity.com/paper/2211.07588/) (2022)
- [Scenario generation for market risk models using generative neural networks](https://mlsanity.com/paper/2109.10072/) (2021)
- [Hierarchical Conditional Tabular GAN for Multi-Tabular Synthetic Data Generation](https://mlsanity.com/paper/2411.07009/) (2024)
- [Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration](https://mlsanity.com/paper/2111.02316/) (2021)
- [Synthetic data for ratemaking: imputation-based methods vs adversarial networks and autoencoders](https://mlsanity.com/paper/2509.02171/) (2025)

## Abstract (arXiv)

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.