Mack-Net model: Blending Mack's model with Recurrent Neural Networks

Eduardo Ramos-P\'erez, Pablo J. Alonso-Gonz\'alez and Jos\'e Javier N\'u\~nez-Vel\'azquez

Papers from arXiv.org

Abstract: In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007-2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measures that relate profitability with risk are crucial in order to understand the financial position of insurance firms. Taking advantage of the increasing computational power, this paper introduces a stochastic reserving model whose aim is to improve the performance of the traditional Mack's reserving model by applying an ensemble of Recurrent Neural Networks. The results demonstrate that blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities.

Date: 2022-05
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ias and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in Expert Systems with Applications. Volume 201, 1 September 2022, 117146

Downloads: (external link)
http://arxiv.org/pdf/2205.07334 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2205.07334

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().