Actuarial Pricing of UAV Insurance for Thin Data Scenarios

Driven by both market demand and policies, the drone insurance industry is facing new development opportunities. This study focuses on exploring an innovative hybrid data integration method, which uses public datasets of drones and small manned aircraft for hybrid data integration and severity scaling, and conducts simulation tests to ensure the reproducibility of the method. A two-part hybrid model approach is adopted to separate the frequency model from the severity model, and a hierarchical modeling method is used for each part to deal with the occurrence of extreme losses. Monte Carlo simulation is performed on the fused data to calculate the net premium. Innovatively, a no-claim discount system is introduced, and the impact of operators' behaviors on claim frequency is quantified, with comprehensive consideration given to the inclusion and quantification of risk factors. The application of Tweedie GLM in total loss modeling is constructed and analyzed, and the advantages and disadvantages of different modeling methods are compared, aiming to provide more comprehensive decision-making basis for insurance companies. This report is intended to construct and evaluate a robust actuarial rate-making model for the rapidly developing drone insurance market, and to develop more accurate, fair and market-competitive drone insurance products.