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, 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 (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

References

1. [1] Hawley, D. D., Johnson, J. D., & Raina, D. (1990). Artificial Neural Systems: A New Tool for Financial Decision-Making. Financial Analysts Journal, 46(November/December), 63-72.
2. [2] Wilson, R. L., & Sharda, R. (1994). Bankruptcy Prediction Using Neural Networks. Decision Support Systems, 11, 545-557.
3. [3] Titterington, D.M. Bayesian methods for neural networks and related models. Stat. Sci. 2004, 19, 128–139.
4. [4] Kitchens F., Harris T. (2015). Genetic Adaptive Neural Networks for Prediction of Insurance Claims, International Journal of Engineering and Advanced Research Technology, 1(6), 27-30.
5. [5] Bayır F. (2006). An Application on Artificial Neural Networks and Predictive Modeling (Yapay Sinir Ağları ve Tahmin Modellemesi Üzerine Bir Uygulama), Master Thesis, Istanbul University, Istanbul.
6. [6] Dogan G. (2010). Portfolio Evaluation in a Private Insurance Company in Turkey Using Artificial Neural Networks (Yapay Sinir Ağları Kullanılarak Türkiye’deki Özel Bir Sigorta Şirketinde Portföy Değerlendirmesi), Master Thesis, Hacettepe University, Ankara.
7. [7] Uslu Ç. S. (2011). Comparison of artificial neural network estimations with time series analysis (Zaman Serisi Analizi İle Yapay Sinir Ağları Kestirimlerinin Karşılaştırılması), Master Thesis, Mimar Sinan Fine Arts University, Istanbul.
8. [8] Bahia I.S.H. (2013). Using Artificial Neural Network Modeling in Forecasting Revenue: Case Study in National Insurance Company/Iraq, International Journal of Intelligence Science, 3, 136-143.

9. [9] Sakthivel K.M., Rajitha C.S. (2017). Artificial Intelligence for Estimation of Future Claim Frequency in Non-Life Insurance, Global Journal of Pure and Applied Mathematics, 13,6.
10. [10] Çetinkaya T., (2019). Life insurance primary production comparing methods forecasting primary production for future years (Hayat Sigortası Prim Üretimlerini Tahminleme Yöntemlerini Karşılaştırarak Gelecek Yıllar Prim Üretimini Tahminleme), Master Thesis, Marmara University, Istanbul.
11. [11] Høysæter D., Larsplass E. (2020). Predictive modelling of customer claims across multiple insurance policies, Master’s thesis in Business Analytics MSc in Economics & Business Administration, Norwegian School of Economics.
12. [12] Werbos P.J. (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences.
13. [13] Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representations by error propagation. Doi:10.1016/B978-1-4832-1446-7.50035-2
14. [14] Elman J.L. (1990) Finding Structure in Time, Cognitive Science, Vol.14, pp.179-211.