Adapting P&C Risk Models: Integrating Advanced Analytics and Geospatial Data to Forecast and Price Risks from Increasing Natural Disasters
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I4P110Keywords:
Property and Casualty Insurance, Natural Disasters, Advanced Analytics, Geospatial Data, Machine Learning, Risk Modeling, Climate Change, Pricing Models, Satellite Imagery, Predictive AnalyticsAbstract
The increasing rate of occurrence and intensity of natural disasters have seen a great influence in the Property and Casualty (P&C) Insurance industry, whose paradigm shift has required a change in the way risks are assessed and priced. Conventional actuarial models which largely rely on the historical loss data are becoming less and less sufficient with the changing climatic pattern and emerging risk factors. In this paper, an attempt will be made to discuss how advanced analytics and geospatial data can be incorporated into P&C risk models to improve the prediction and pricing of natural disaster-related risks. The insurers can build more helpful and dynamic models by using machine learning algorithms and satellite imagery, on-site environmental data that can capture the present and future risk environments. This paper evaluates the use of these combined models in the recent natural disasters (in 2022), which were the most successful records, in their forecasting and pricing of risks. Also, in this paper, challenges and opportunities of this integration are discussed such as data quality, model interpretability, and regulatory considerations. The results demonstrate the importance of insurers adopting new strategies in risk modeling so as to remain afloat and address the changing needs of the market
References
[1] Müller-Crepon, C., Hunziker, P., & Cederman, L. E. (2021). Roads to rule, roads to rebel: Relational state capacity and conflict in Africa. Journal of Conflict Resolution, 65(2-3), 563-590.
[2] Karri, N. (2021). Self-Driving Databases. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 74-83. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P10
[3] Lyubchich, V., Newlands, N. K., Ghahari, A., Mahdi, T., & Gel, Y. R. (2019). Insurance risk assessment in the face of climate change: Integrating data science and statistics. Wiley Interdisciplinary Reviews: Computational Statistics, 11(4), e1462.
[4] Dey, A. J., & Sarma, H. K. D. (2021). A Survey on application of machine learning in property and casualty insurance. In Contemporary Issues in Communication, Cloud and Big Data Analytics: Proceedings of CCB 2020 (pp. 307-314). Singapore: Springer Singapore.
[5] Blier-Wong, C., Cossette, H., Lamontagne, L., & Marceau, E. (2020). Machine learning in P&C insurance: A review for pricing and reserving. Risks, 9(1), 4.
[6] Paltrinieri, N., Comfort, L., & Reniers, G. (2019). Learning about risk: Machine learning for risk assessment. Safety science, 118, 475-486.
[7] Karri, N., & Jangam, S. K. (2021). Security and Compliance Monitoring. International Journal of Emerging Trends in Computer Science and Information Technology, 2(2), 73-82. https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P109
[8] Javanmardian, K., Ramezani, S., Srivastava, A., & Talischi, C. (2021). How data and analytics are redefining excellence in P&C underwriting. McKinsey & Company, Sep, 24.
[9] Albano, R., & Sole, A. (2018). Geospatial methods and tools for natural risk management and communications. ISPRS International Journal of Geo-Information, 7(12), 470.
[10] Mutenga, S. (2015). Risk management for property casualty insurance companies (Doctoral dissertation, City University London).
[11] Wang, S., & Faber, R. (2006). Enterprise risk management for property-casualty insurance companies. CAS and SOA Jointly Sponsored Research Project.
[12] Karri, N., Pedda Muntala, P. S. R., & Jangam, S. K. (2025). Predictive Performance Tuning. International Journal of Emerging Research in Engineering and Technology, 2(1), 67-76. https://doi.org/10.63282/3050-922X.IJERET-V2I1P108
[13] Elms, D. (2019). Limitations of risk approaches. Civil Engineering and Environmental Systems, 36(1), 2-16.
[14] Gillespie, T. W., Chu, J., Frankenberg, E., & Thomas, D. (2007). Assessment and prediction of natural hazards from satellite imagery. Progress in Physical Geography, 31(5), 459-470.
[15] García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining (Vol. 72, pp. 59-139). Cham, Switzerland: Springer International Publishing.
[16] Alexandropoulos, S. A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019). Data preprocessing in predictive data mining. The Knowledge Engineering Review, 34, e1.
[17] Centeno, V., Georgiev, I. R., Mihova, V., & Pavlov, V. (2019, October). Price forecasting and risk portfolio optimization. In AIP conference proceedings (Vol. 2164, No. 1, p. 060006). AIP Publishing LLC.
[18] Wang, W. (2007). An adaptive predictor for dynamic system forecasting. Mechanical Systems and Signal Processing, 21(2), 809-823.
[19] Karri, N. (2021). AI-Powered Query Optimization. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 2(1), 63-71. https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P108
[20] Akbar, A., Kousiouris, G., Pervaiz, H., Sancho, J., Ta-Shma, P., Carrez, F., & Moessner, K. (2018). Real-time probabilistic data fusion for large-scale IoT applications. Ieee Access, 6, 10015-10027.
[21] Wang, C., Yu, Q., Law, K. H., McKenna, F., Yu, S. X., Taciroglu, E., ... & Cetiner, B. (2021). Machine learning-based regional scale intelligent modeling of building information for natural hazard risk management. Automation in Construction, 122, 103474.
