Decision support system for estimating and forecasting state of insurance company

Authors

  • Roman Panibratov Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine https://orcid.org/0000-0002-8604-4420

DOI:

https://doi.org/10.20535/SRIT.2308-8893.2022.1.05

Keywords:

insurance company, decision support system, binary classification, k-nearest neighbors, support vector machine, naive Bayes classifier, random forest, gradient boosting, neural networks

Abstract

The decision support system was created for estimating and forecasting the state of an insurance company according to its financial and economic indicators. The task of estimating the state of this type of an institution was considered as a problem of a binary classification: whether the company’s activity is efficient or not. During the research, six supervised machine learning methods were implemented: k-nearest neighbors, support vector machine, naive Bayes classifier, random forest, XGBoost and deep neural network. The created system allows the following: to perform correlation analysis of financial and economic indicators, to check the balance of data, to perform training of the selected model and to estimate quality of training, to predict the state of the insurance company according to the selected model. According to the best model, the future state of insurance companies in Ukraine was predicted.

Author Biography

Roman Panibratov, Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv

Roman S. Panibratov,

a graduate student at Educational and Scientific Complex "Institute for Applied System Analysis" of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv, Ukraine.

References

O.S. Oleksiuk, Financial Decision Support Systems, (in ukr.). Kyiv: Naukova Dumka, 1998, 508 p.

J. Ali, R. Khan, N. Ahmad, and I. Maqsood, “Random Forests and Decision Trees”, International Journal of Computer Science Issues (IJCSI), vol. 9, no. 3, pp. 272–278, 2012.

S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”, ICML, vol. 37, pp. 448–456, 2015.

N. Srivatsava et al., “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014.

A.A. Shirinyan and L.V. Shirinyan, “Efficiency indicators of ukrainian insurance market from the position of owners of insurance companies”, Efektyvna ekonomika, vol. 10, 2019. doi: 10.32702/2307-2105-2019.10.172

Downloads

Published

2022-04-25

Issue

No. 1 (2022)

Section

Decision making and control in economic, technical, ecological and social systems

License

This is an open access journal which means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access.