FORECASTING LIQUIDITY AND SOLVENCY INDICATORS BASED ON ARTIFICIAL INTELLIGENCE

This article examines the issues of forecasting enterprises’ liquidity and solvency indicators based on artificial
intelligence. The main objective of the study is to compare the effectiveness of traditional methods and artificial intelligence
models in assessing financial stability. The article analyzes the possibilities of predicting the future state of liquidity
indicators using neural networks. Particular attention is paid to identifying complex and nonlinear relationships in the
forecasting process. The research results demonstrate that artificial intelligence models provide high accuracy in the early
identification of financial risks. The conclusions obtained have practical significance for improving enterprises’ financial
management systems. The scientific novelty of the article lies in the comprehensive application of artificial intelligence
approaches to forecasting liquidity and solvency.