Risk Quantification-based Underwriting Assessment Model
DOI:
https://doi.org/10.54097/gxe1mx05Keywords:
Uderwriting risk index, AHP, Gray Relation Analysis, CAPM, GE matrix.Abstract
With extreme weather events increasingly causing losses in regions, the insurance sector is confronted with a growing dilemma. Insurance companies urgently need to develop risk quantification-based underwriting assessment model (RQ-UA) to keep pace with the evolving environment. The underwriting risk index is partitioned into 11 indicators, and the AHP combined with GRA is utilized to calculate the weights of these indicators. This paper employ the Capital Asset Pricing Model (CAPM) from Economics to develop the RQ-UA assessment model. Applying Japan and Ecuador to this model, the acceptable thresholds for were calculated to be 0.3291 and 0.1843, indicating that underwriting can proceed when values remain below these figures. To enhance the model's flexibility in specialized regions, this paper introduce two new factors: the Housing Resilience Index (HRI) and the Government Subsidy Index (GSI). By combining the GE matrix with the model, can be calculated. Further validation with Japan and Ecuador showed that, with the intervention of property developers, the thresholds for in the two countries increased by 32.9% and 17.8%.
With extreme weather events increasingly causing losses in regions, the insurance sector is confronted with a growing dilemma. Insurance companies urgently need to develop risk quantification-based underwriting assessment model (RQ-UA) to keep pace with the evolving environment. The underwriting risk index is partitioned into 11 indicators, and the AHP combined with GRA is utilized to calculate the weights of these indicators. This paper employ the Capital Asset Pricing Model (CAPM) from Economics to develop the RQ-UA assessment model. Applying Japan and Ecuador to this model, the acceptable thresholds for were calculated to be 0.3291 and 0.1843, indicating that underwriting can proceed when values remain below these figures. To enhance the model's flexibility in specialized regions, this paper introduce two new factors: the Housing Resilience Index (HRI) and the Government Subsidy Index (GSI). By combining the GE matrix with the model, can be calculated. Further validation with Japan and Ecuador showed that, with the intervention of property developers, the thresholds for in the two countries increased by 32.9% and 17.8%.
Downloads
References
[1] Steptoe H, Souch C, Slingo J. Advances in numerical weather prediction, data science, and open‐source software herald a paradigm shift in catastrophe risk modeling and insurance underwriting [J]. Risk Management and Insurance Review, 2022, 25 (1): 69-81.
[2] Wang K. Overcoming Extreme Weather: The Insurance Industry’s Road to Recovery [C] // 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024). Atlantis Press, 2024: 452-460.
[3] Lv M, Yin J. A Study of the Property Insurance Industry Based on the Property Insurance Underwriting Assessment (PIUA) Model [J]. International Journal of New Developments in Engineering and Society, 2024, 8 (2).
[4] Zhu F. Forecasting Catastrophe: Integrating GAN-ALSTM and Monte Carlo Simulations for Insurance Underwriting in Extreme Weather [C] // 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024: 928-932.
[5] Shen P. Integrating Natural and Social Indicators for Enhanced Insurance Underwriting Strategies [J]. Highlights in Business, Economics and Management, 2024, 33: 367-374.
[6] Calabrese R, Dombrowski T, Mandel A, et al. Impacts of extreme weather events on mortgage risks and their evolution under climate change: A case study on Florida [J]. European Journal of Operational Research, 2024, 314 (1): 377-392.
[7] Wang T K, Zhang Q, Chong H Y, et al. Integrated supplier selection framework in a resilient construction supply chain: An approach via analytic hierarchy process (AHP) and grey relational analysis (GRA) [J]. Sustainability, 2017, 9 (2): 289.
[8] Mills E. A global review of insurance industry responses to climate change [J]. The Geneva Papers on Risk and Insurance-Issues and Practice, 2009, 34: 323-359.
[9] Tsanakas A, Desli E. Measurement and pricing of risk in insurance markets [J]. Risk Analysis: An International Journal, 2005, 25 (6): 1653-1668.
[10] Karydas C, Xepapadeas A. Pricing climate change risks: CAPM with rare disasters and stochastic probabilities [J]. CER-ETH Working Paper Series Working Paper, 2019, 19: 311.
[11] Rowan S, Kwiatkowski K. Assessing the relationship between social vulnerability, social capital, and housing resilience [J]. Sustainability, 2020, 12 (18): 7718.
[12] Härdle W K, Cabrera B L. The implied market price of weather risk [J]. Applied Mathematical Finance, 2012, 19 (1): 59-95.
[13] Tsakalerou M. GE/McKinsey matrices revisited: a mixed mode tool for multi-criteria decision analysis [J]. Eur. J. Cont. Econ. & Mgmt., 2015, 2: 92.
[14] Okoko C O. An Application of McKinsey Matrix in the Assessment of Route Attractiveness in Kenya Airways Ltd [D]. University of NAIROBI, 2006.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Business, Economics and Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
