Overcoming Extreme Weather: The Insurance Industry’s Road to Recovery

DOI

10.2991/978-94-6463-488-4_51How to use a DOI?

Keywords

Insurance industry; Extreme weather; Risk assessment model; LSTM model

Abstract

In recent years, there has been an increase in extreme weather events globally, posing challenges to the economy and the property insurance industry. The property-casualty insurance industry needs to adapt to enhance its underwriting capacity and service levels to cope with climate change risks. In order to assess whether insurance companies should underwrite in areas facing an increase in extreme weather events, this paper establishes a risk assessment model. At first, high-frequency indicators are selected as the basis for judgment, and their correlation analysis is performed. And the Bayesian formula was used to estimate the probability of occurrence of a specific extreme weather event. Next, an LSTM model was constructed using historical loss data for predicting the amount of compensation loss in different regions. Then, the TF-IDF algorithm was used to calculate the weights of the events according to their impact on the regions, so as to calculate the comprehensive risk scores of the regions. Finally, the quantitative data of the indicators are used as inputs to the SVM model to categorize the risk of different regions and provide a scientific basis for insurers’ pricing and decision-making.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Kexin Wang
PY  - 2024
DA  - 2024/08/29
TI  - Overcoming Extreme Weather: The Insurance Industry’s Road to Recovery
BT  - Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024)
PB  - Atlantis Press
SP  - 452
EP  - 460
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-488-4_51
DO  - 10.2991/978-94-6463-488-4_51
ID  - Wang2024
ER  -