Forecasting and management of non performing loans in the state banking sector of Ukraine

Abstract

Relevance of the study is determined by the persistently high level of non-performing loans (NPLs) in Ukrainian state-owned banks, which for a long time has remained one of the key challenges to financial stability. Under conditions of ongoing military conflict and economic uncertainty, the analysis of NPL dynamics and the search for effective instruments of their management acquire particular importance. The purpose of the article is to develop a holistic approach to analyzing the trends and risks of non-performing loans in state-owned banks of Ukraine, to identify possible scenarios of their evolution, and to substantiate directions for systemic management of problem assets. Research methods. The study applies methods of economic analysis, linear regression, and scenario forecasting. Regression models are used to evaluate the temporal dynamics of NPLs in state-owned banks, while scenario analysis outlines alternative trajectories of development ranging from optimistic to pessimistic. Research results. The current state of non-performing loans in Ukrainian state-owned banks for the period 2022–2026 has been examined. The findings demonstrate that the NPL level tends to decline gradually, though at different rates across banks, indicating heterogeneity in the processes of portfolio recovery. Constructed regression models justify the possibility of forecasting changes in NPL shares and identifying the most problematic segments of loan portfolios. Scenario analysis has enabled the formation of optimistic, baseline, and pessimistic development paths, which help assess potential risks to financial stability. Conclusions. A system of managing problem assets in state-owned banks has been substantiated on the basis of regression modeling and scenario analysis. Proposed management directions include early risk detection, planning of portfolio restructuring, and the use of scenario analysis for managerial decision-making. This approach contributes to minimizing the likelihood of pessimistic outcomes and maintaining resilience of the banking sector. Prospects for further research lie in expanding modeling frameworks by incorporating additional macroeconomic and institutional factors that influence NPL dynamics.