Usage of intellectual analysis of ecological-economic data in the pension insurance system and for forecasting expenditures on social protection and social security

• Дата: 2024
• Автор: Зарудний, О.Б.
• Формат: Стаття
• Мова: Ukrainian
• Опубліковано: Kyiv National University of Construction and Architecture 2024
• Теми: expenses for social protection and social security; air pollution; Bayesian network; principal component analysis; the system of actuarial calculations
• Онлайн доступ: https://es-journal.in.ua/article/view/314099
• Назва журналу: Environmental safety and natural resources

The paper is devoted to an actual scientific and applied problem – the development of methodology for applying mathematical models and data mining methods for actuarial calculations in mandatory state pension insurance system. The paper describes methodology for modeling changes in the number of pension recipients taking into account the impact of environmental factors, in particular air pollution. The basis of the proposed method is a multi-model approach, characterized by combination of data mining and probabilistic models in the form of Bayesian network, which are appropriate in conditions of statistical, parametric and structural uncertainty.The proposed approach describes the change in number of pension recipients, in particular for disability and breadwinner loss, under influence of air pollution from organic and inorganic compounds. The scientific novelty of the paper is in the use of an ensemble of models including probabilistic and statistical models in the form of Bayesian network and regression models, in the system of actuarial calculations of mandatory state pension insurance.The paper considers several scenarios for the impact of pollutants on the growth of number of pension recipients. The indicator of the share of expenditures on social protection and social security of the population in the gross national product was chosen as the target variable of the process under study. Mathematical models were found to be adequate to the modeling process, and the Bayesian network classification error is about 20%. The model structure is built in Genie 2.0 modeling system. The principal component analysis, is used to reduce the data dimension. The proposed methodology can also be applied to other tasks of forecasting social protection and social security expenditures.