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

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, predicting the premium sizes, which is the main factor that constitutes the volume of the insurance sector, as accurately and reliably as possible, indirectly means foreseeing the risks that may arise in terms of the economy and development of the country and taking the necessary measures. In this study, the premium production of some insurance companies operating in Turkey is predicted with different artificial neural networks and evaluated the results comparatively. In this context, basically, two different artificial neural networks (ANNs), feed-forward, and feed-back have been used as predictive tools for insurance premium production. Two training algorithms and two different activation functions have been operated in the structure of the ANNs used. Thus, eight different predictive tools for insurance companies' premium production have been created. The prediction performances of ANNs have been evaluated on the test sets using error criteria such as Root Mean Error Squares, Average Absolute Percentile Error, and Median Absolute Percentile Error.