A pricing model for agricultural insurance based on big data and machine learning

Abstract

Agricultural insurance is a crucial element of policies that promote and protect agriculture. It protects agriculture from risk and distributes agricultural hazards. The rural economy’s stabilization has been a significant stabilizer function. But as agriculture insurance has quickly advanced, a number of issues have unavoidably come to light. Agricultural insurance still offers a wide range of products and services available today. Big data will play a significant supporting role in the pressing need to innovate and improve goods and services. Other information supporting agricultural insurance includes agricultural data connected to it. The two previously most often utilized agricultural index insurances are regional yield insurance and weather index insurance. They struggle with risk pricing mostly due to a lack of appropriate empirical data, complicated dependence linkages between various hazards, and the prevalence of basis risk. A comprehensive study and review of pertinent research findings are carried out by modelling regional yield risk, building weather indicators and their distribution fitting, modelling agricultural dependence risk, and measuring and reducing basis risk. This article highlights the flaws in the current pricing models as well as the problems that need to be addressed in future studies. The need to further develop agricultural index insurance’s risk modelling techniques and increase the objectivity and precision of the pricing outcomes cannot be overstated in terms of their practical importance.