FORECASTING INSURANCE CLAIMS RELATED TO WASPS

Many insurance companies offer coverage for damages caused by wasps to living environments. The frequency of insurance claims related to wasps varies yearly, and anticipating the exact frequency is a complex task. By forecasting insurance claims, companies can optimize their resource allocation and management. The objective of this thesis is to forecast the frequency of insurance claims related to wasps using weather data collected over time and space by developing probabilistic models within the Bayesian framework. Weather data is used as it is assumed to capture environmental conditions that affect wasp behavior, and conditions that increase the chances of damages caused by wasps being detected. The Bayesian framework is employed as it offers an efficient way to model uncertainty by treating parameters as random. Twelve models were fitted and their predictive performance for June, July, and August were evaluated during 2022 and 2023. For June, a Negative Binomial model incorporating a spatial adjustment component with a CAR prior, and weather covariates, demonstrated the highest predictive performance. For July, a model incorporating an autoregressive parameter and the weather effect three weeks preceding the insurance claim performed best. For August, a model incorporating only weather covariates outperformed the others. The differing results show that the models capture different underlying processes in the months.