Predictive AI Models for Designing Sustainable Crop Insurance Programs

Rezumat

The increasing volatility of climate change and market conditions poses significant challenges to traditional crop insurance programs, which often fail to address the dynamic risks faced by farmers. Predictive AI models offer a transformative solution by leveraging machine learning and data analytics to design sustainable crop insurance programs. This study explores the development and application of predictive models using historical climate, crop yield, and market data to assess risk and recommend optimized insurance premiums. Advanced techniques such as ensemble learning, time-series forecasting, and geospatial analysis are utilized to enhance the accuracy of risk predictions. The results demonstrate that the AI-driven framework achieves a 15% reduction in premium costs for low-risk farmers while increasing loss coverage by 25% compared to traditional methods. The model also enables dynamic adjustments to insurance terms based on real-time weather and market conditions, ensuring greater adaptability and resilience. This research highlights the potential of predictive AI to create equitable, cost-effective, and sustainable crop insurance programs that address the challenges of modern agriculture.