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...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.展开更多
Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for suc...Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.展开更多
基金funded by the National College Students'Innovation and Entrepreneurship Training Program(No.202410456025)supported by the China Center of the Serbian Academy of Sciences and Arts and the Hong Kong Institute of Humanities and Natural Sciences and Technology.
文摘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.
文摘Territory risk analysis has played an important role in the decision-making of auto insurance rate regulation.Due to the optimality of insurance loss data groupings,clustering methods become the natural choice for such territory risk classification.In this work,spatially constrained clustering is first applied to insurance loss data to form rating territories.The generalized linear model(GLM)and generalized linear mixed model(GLMM)are then proposed to derive the risk relativities of obtained clusters.Each basic rating unit within the same cluster,namely Forward Sortation Area(FSA),takes the same risk relativity value as its cluster.The obtained risk relativities from GLM or GLMM are used to calculate the performance metrics,including RMSE,MAD,and Gini coefficients.The spatially constrained clustering and the risk relativity estimate help obtain a set of territory risk benchmarks used in rate filings to guide the rate regulation process.
