Improving Rainfall Index Insurance: Evaluating Effects of Fine-Scale Data and Interactive Tools in the PRF-RI Program

Since its inception, the Pasture, Rangeland, and Forage Rainfall Index (PRF-RI) insurance program has issued a total of $8.8 billion in payouts. Given the program's significance, this thesis investigates methodologies to help improve it. For the first part, we evaluated the impact of finer-scale precipitation data on insurance payouts by comparing how the payout distribution differs between the program's current dataset and the finer-scale precipitation dataset by creating a simulated scenario where all parameters are constant except the rainfall index computed by the respective dataset. The analysis for Texas in 2021 revealed that using the finer-scale dataset to compute the rainfall index would result in payouts worth $27 million less than the current dataset. The second part of the research involved the development of two interactive decision-support tools: the "Next-Gen PRF" web tool and the "AgInsurance LLM" chatbot. These tools were designed to help users understand complex insurance parameters and make informed decisions regarding their insurance policies. User studies for the "Next-Gen PRF" tool measured usability, comprehension decision-making efficiency, and user experience, showing that it outperforms traditional methods by providing insightful visualizations and detailed descriptions. The findings suggest that using fine-scale precipitation data and advanced decision-support technologies can substantially benefit the PRF-RI program by reducing spatial basis risk and promoting user education, thus leading to higher user engagement and enrollment.