# Improving Insurance Catastrophic Data with Resampling and GAN Methods

arXiv: https://arxiv.org/abs/2410.17294 · HTML: https://mlsanity.com/paper/2410.17294/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 22 references that are in the map.
- First posted on arXiv 22 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data](https://mlsanity.com/paper/2203.03489/) (2022)

## Abstract (arXiv)

The precise and large dataset concerning catastrophic events is very important for insurers. To improve the quality of such data three methods based on the bootstrap, bootknife, and GAN algorithms are proposed. Using numerical experiments and real-life data, simulated outputs for these approaches are compared based on the mean squared (MSE) and mean absolute errors (MAE). Then, a direct algorithm to construct a fuzzy expert's opinion concerning such outputs is also considered.