2026-06-08T01:08:12Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/1094852024-10-26T00:04:34Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Oquendo Torres, Freddy Alejandro
author
Segovia Vargas, María Jesús
2024-10-25T07:51:11Z
2024-10-25T07:51:11Z
2024-09-18
Oquendo-Torres, F.A. & Segovia-Vargas, M.J. (2024) Sustainability risk in insurance companies: A machine learning analysis. Global Policy, 00, 1–18. Available from: https://doi.org/10.1111/1758-5899.13440
1758-5880
10.1111/1758-5899.13440
https://hdl.handle.net/20.500.14352/109485
1758-5899
https://doi.org/10.1111/1758-5899.13440
https://onlinelibrary.wiley.com/doi/10.1111/1758-5899.13440
Sustainable development constitutes a global challenge today, and the sustainable development goals (Agenda 2030) will probably set the course for the coming decades. This paper discusses sustainability in insurance companies by combining two aspects: a social approach (the environmental impact) and a business approach (the prediction of claims due to climate change). Our objective is to analyse the impact of physical risk in a home insurance portfolio and to measure in economic terms the effect of climate change in the future, by applying machine learning methodologies. Two data sources are used: a Spanish insurance portfolio with 31,998 policies and claims from 2017 to 2022, and daily meteorological variables from 290 Spanish weather stations from 2000 to 2022. Two climate scenarios are considered: RCP 4.5 (medium impact) and RCP 8.5 (high impact). On average for the period 2023–2052, the results reveal that claims will increase by 105% for the 4.5 scenario and by 129% for the 8.5 scenario. Our paper makes a clear contribution to sustainability by analysing climate risks and their impact on an insurance portfolio. It shows the grave consequences of climate change for the insurance sector's solvency and the political implications for the financial system in general.
eng
Attribution-NonCommercial-NoDerivatives 4.0 International
Sustainability risk in insurance companies: a machine learning analysis
journal article
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URL
https://docta.ucm.es/bitstreams/491a39a4-0d39-49c1-911a-64c97e641d34/download
File
MD5
09d51b46194fd6302291fb5add9d283f
1740408
application/pdf
Sustainability risk in insurance companies.pdf