Prediction of the Premium Production of Some Insurance Companies Operating in Turkey with Artificial Neural Networks

Authors : Buse ÖZGÜR, Ufuk YOLCU

Pages : 73-82

Doi:10.34110/forecasting.1223653

View : 15 | Download : 10

Publication Date : 2022-12-31

Article Type : Research Paper

Abstract :The insurance sector can be seen as a sector that directly affects the country\`s economy and development with its ability to fund financial markets and meet risks. In this respect, estimating the premium size, which is the main factor that constitutes the volume of the insurance sector, as accurately and reliably as possible, indirectly means predicting the risks that may arise in terms of the economy and development of the country and taking precautions. necessary measures. In this study, premium productions of some insurance companies operating in Turkey were estimated with different artificial neural networks and their results were evaluated comparatively. In this context, two different artificial neural networks insert ignore into journalissuearticles values(ANNs);, feed forward and feedback, were used as the estimation tools for insurance premium production. Two training algorithms and two different activation functions were run in the structure of the ANNs used. Thus, eight different estimation tools were created for insurance companies\` premium production. The estimation performances of ANNs were evaluated on test sets by using error criteria such as Root Mean Square Error, Mean Absolute Percentage Error, and Median Absolute Percentage Error. In terms of the MdAPE criterion in our best-performing algorithms, in the analysis of a total of 36 data sets, 18 quarters of 18 months in total, the predictions for only 6 data sets were estimated with an error of more than 10%, and 5 of them were around 10% or just above, which is still acceptable. have an acceptable level of error.
Keywords : Insurance premium production volume, Artificial neural networks, Prediction, Time Series, Training Algorithm, Activation Function