Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 38, 06 June 2024
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Insurance company underwriting model against extreme weather
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Abstract
This paper provides a corresponding coping strategy for developing the insurance industry under extreme weather by establishing an insurance company underwriting model. An insurance model (ICU model) for assessing catastrophe risk is proposed based on the results of some international databases and disaster resilience studies. The ICP coefficient is obtained by multiplying the regional vulnerability index with the regional risk index, where our innovatively proposed ARIMA-LSTM coupling algorithm predicts the risk index. The inverse proportionality function of the ICU coefficient is constructed based on the fact that the risk of insurance companies is positively correlated with the regional risk (ICP coefficient) and negatively correlated with the regional purchasing power (CBP coefficient). The CBP coefficients were computed by K-means clustering, and the derived ICP coefficients were used to derive the ICU coefficients for each region. Finally, the coefficients were categorized into three intervals to give the insurance company’s coverage model.
Keywords
ARIMA-LSTM coupling algorithm, K-means clustering, Insurance Company Underwriting Model, Economic vulnerability, Social vulnerability
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-461-3
- ISBN (Online)
- 978-1-83558-462-0
- Published Date
- 06 June 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/38/20240522
- Copyright
- 06 June 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited