THE ROLES OF AI ON PREDICTIVE ANALYTICS IN MANAGING CLIMATE CHANGE RISKS IN REINSURANCE

Abstract

This study examined the roles of AI on predictive analytics in managing climate change risks in reinsurance. The study mentioned that the advent of artificial intelligence (AI) has revolutionized numerous industries, including reinsurance and climate risk management. In the context to carry out this research, numerous subheads were taken into consideration some of which included: concept of artificial intelligence, concept of predictive analytics and concept of climate change. The study mentioned the types of climate change risks to include: physical risks, water scarcity/agriculture and biodiversity loss. Furthermore, the study highlighted the roles of AI on predictive analytics in managing a climate change risk in reinsurance to include among others: improved risk assessment/modeling, advancements in climate forecasting and development of parametric insurance products. The study also mentioned the strategic ways of leveraging business analytics to model a financial impact of climate related events and design sustainable coverage solution to include: climate risk modeling/predictive analytics and parametric insurance/innovative coverage solutions to mention but a few. In the same vein, the strategic ways insurance and reinsurance used business analytics to model financial impact of climate related events as mentioned in the study included: climate risk modeling/predictive analytics, dynamic pricing models and parametric insurance solutions. Moreover the study mentioned parametric insurance for climate resilience and climate risk modeling among others as sustainable coverage solution used by insurers and reinsurers to mitigate climate related events. The study concluded that AI has revolutionized predictive analytics in managing climate change risks within the reinsurance sector by enhancing risk assessment, loss forecasting, and decision-making capabilities. The study recommended that reinsurance companies should prioritize investments in advanced AI infrastructure, such as machine learning platforms and data analytics tools, to process large volumes of climate data and generate accurate risk models.