ARTIFICIAL INTELLIGENCE IN THE PUBLIC DEBT MANAGEMENT SYSTEM

Анотація

The study is relevant due to the increasing complexity of public debt management amid macrofinancial instability, high borrowing costs, heightened fiscal risks, and the rapid digital transformation of public finances. In such conditions, state institutions need tools that allow them to process large volumes of debt, budget, macroeconomic, and market data more quickly, assess alternative scenarios, and identify threats to debt sustainability in a timely manner. The purpose of the article is to substantiate the opportunities, risks, and directions of using artificial intelligence to increase the analytical capacity of the public debt management system within the digital transformation of public finances. The object of the study is the public debt management system, and the subject is the theoretical, methodological, and applied aspects of integrating AI into the processes of forecasting, scenario analysis, risk management, and management decision support. The methodological basis of the study is a systematic approach, analysis and synthesis, comparative analysis, classification, grouping, scenario approach, risk analysis, scientific abstraction, and graphical modeling. As a result of the study, 12 areas of AI application in public debt management were systematized, namely: forecasting debt dynamics; assessing debt sustainability; modeling currency, interest, and refinancing risks; conducting scenario analysis; optimizing the structure of borrowings; and identifying anomalies in financial data. 8 groups of possibilities for using AI, 10 groups of risks, and corresponding safeguards to minimize them were also identified. An AI-based digital architecture was proposed for a debt management system, comprising 6 functional layers: data layer, analytical layer, decision–support layer, institutional layer, governance layer, and feedback layer. As a result, a conceptual model of AI integration into the public debt management system was developed, integrating the institutional framework, digital infrastructure, high-quality data, AI analytics, scenario calculations, management decisions, risk control, and debt sustainability monitoring. The practical value of the results lies in their potential use by public finance authorities to develop debt strategies, improve fiscal forecasting, enhance the transparency of analytical procedures, and inform approaches to the responsible use of AI in line with the human-in-the-loop principle.

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