USING ARTIFICIAL INTELLIGENCE TO FORECAST SOCIAL SECURITY NEEDS

The article examines the use of artificial intelligence methods to forecast the need for social security, which is due to the growing complexity of socioeconomic processes and the need to optimize the distribution of resources in the social sphere. Modern approaches are considered, including the integration of traditional statistical methods with machine and deep learning algorithms, as well as the use of hybrid models that can take into account multifactorial relationships and dynamic changes in input data. Particular attention is paid to the development of adaptive architectures based on ensemble algorithms, transfer learning and Bayesian methods, which ensures high accuracy, stability and interpretability of predictive models. The article provides a comprehensive analysis of data preparation and processing problems, including methods of normalization, dimensionality reduction and missing value synthesis, and describes modern approaches to estimating forecast uncertainty.