Improving Insurance Catastrophic Data with Resampling and GAN Methods
Machine Learning
2025-11-05 v1 Applications
Computation
Machine Learning
Abstract
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.
Cite
@article{arxiv.2410.17294,
title = {Improving Insurance Catastrophic Data with Resampling and GAN Methods},
author = {Norbert Dzadz and Maciej Romaniuk},
journal= {arXiv preprint arXiv:2410.17294},
year = {2025}
}