GEMAct is a comprehensive actuarial package, based on the collective risk theory framework, that offers a set of tools for non-life (re)insurance costing, stochastic claims reserving, and loss aggregation.
The variety of available functionalities makes GEMAct modeling very flexible and provides actuarial scientists and practitioners with a powerful tool that fits into the expanding community of Python programming language.
Please visit our website to see our documentation and tutorial.
The accompanying paper is registered with DOI doi:10.1017/S1748499524000022.
APA citation:
Pittarello, G., Luini, E., & Marchione, M. M. (2024). GEMAct: a Python package for non-life (re)insurance modeling. Annals of Actuarial Science, 1–37. doi:10.1017/S1748499524000022
BibteX citation:
@article{Pittarello_Luini_Marchione_2024,
title={GEMAct: a Python package for non-life (re)insurance modeling},
DOI={10.1017/S1748499524000022},
journal={Annals of Actuarial Science},
author={Pittarello, Gabriele and Luini, Edoardo and Marchione, Manfred Marvin},
year={2024},
pages={1–37}}
The manuscript pre-print is instead available at ArXiV:2303.01129.
Please do not hesitate to contact us if you are interested in helping us in expanding our package, you can find our contact details here. Possible future enhancements could involve the introduction of new probability distribution families, the implementation of supplementary methodologies for the approximation of quantiles of the sum of random variables, and the addition of costing procedures for exotic and nontraditional reinsurance solutions.
Previous versions were presented at the Mathematical and Statistical Methods for Actuarial Sciences and Finance 2022, and at the Actuarial Colloquia 2022, in the ASTIN section.
We want to especially thank the students in Statistica per le assicurazioni, M.Sc. in Economia e Finanza, at the University of Milano-Bicocca for having tested the package.