USE OF ARTIFICIAL INTELLIGENCE AND BIG DATA FOR FORECASTING SUSTAINABLE ECONOMIC DEVELOPMENT INDICATORS

Abstract

The article examines the peculiarities of using artificial intelligence and Big Data technologies in forecasting sustainable economic development indicators in the context of the digital transformation of the global economy. It is substantiated that modern information systems generate significant volumes of structured and unstructured data that can be used for modeling economic, social, and environmental processes. The main directions of application of machine learning algorithms, neural networks, Big Data analytics, and intelligent decision support systems in the field of sustainable development are analyzed. The study proves that artificial intelligence technologies are becoming one of the key drivers of modern economic analytics, as they provide opportunities for processing large-scale datasets in real time and identifying hidden relationships between economic indicators. The article analyzes the role of Big Data in the formation of adaptive forecasting models capable of processing information from various digital sources, including government databases, financial systems, social media, satellite monitoring systems, IoT devices, and international analytical platforms. Particular attention is paid to the formation of a system of sustainable development indicators and their forecasting using AI-based models. The study proposes a system of indicators including GDP growth, unemployment rate, CO₂ emissions, renewable energy share, ESG index, and digitalization indicators. Based on machine learning approaches and statistical data from OECD, IDC, Statista, and DESI reports, forecast scenarios for sustainable economic development until 2030 are developed. The study substantiates that the combination of Big Data analytics and artificial intelligence creates the basis for the formation of a new model of economic management focused on flexibility, adaptability, technological innovation, and sustainable long-term growth. At the same time, the article highlights a number of challenges related to cybersecurity, data quality, algorithm transparency, ethical aspects of artificial intelligence implementation, and the need for developing an appropriate regulatory framework.