Abstract:
In drought-prone African countries like Zimbabwe, the uptake of parametric insurance has been low due to the absence of localized models. Guided by the CRISP-DM model, the present study proposes an AI-based approach to drought prediction in parametric insurance. The study’s paramount objectives are establishing trigger thresholds for drought events, assessing their significance, identifying the most effective machine learning models for drought modeling based on the Standardized Precipitation Index (SPI), and forecasting future drought occurrences and their magnitudes. Historical weather data, including temperature and rainfall, are utilized and a range of machine learning models -neural networks, random forest, and support vector machines are employed for drought prediction. The performance of these models is evaluated based on accuracy, reliability, and interpretability, with continuous refinement based on feedback from stakeholders. The significance of this research lies in promoting data-driven decisions, incentivizing preparedness, enabling risk transfer, facilitating rapid insurance payouts, and enhancing financial stability. With accurate drought predictions driving parametric insurance, policyholders can make well-informed choices, adopt proactive measures, transfer the risk of drought-related losses, receive swift insurance payouts, and improve their financial resilience during drought events.
