INTELLIGENT FORECASTING TOOLS IN ENTERPRISE FINANCIAL MANAGEMENT: POTENTIAL AND LIMITATIONS

The article addresses the relevant scientific and practical issue of developing innovative approaches to forecasting in enterprise financial management in the context of economic digital transformation. The necessity of using intelligent forecasting tools based on modern technologies of artificial intelligence, machine learning, big data analytics, and neural networks is substantiated. The main advantages of implementing such tools are identified, including increased accuracy and speed of financial analysis, enhanced adaptability of strategic planning, and reduced subjectivity in decision-making processes. The study systematizes key models of intelligent forecasting: ARIMA, GARCH, LSTM, CNN, decision trees, clustering algorithms, hybrid approaches, and fuzzy logic. Practical examples of their application in financial management are provided, including cash flow forecasting, investment attractiveness evaluation, risk analysis, enterprise development scenario modeling, and fraud detection. It is shown that intelligent technologies play a leading role in building flexible and resilient financial management systems capable of self-learning, self-regulation, and dynamic restructuring under uncertainty. At the same time, the main limitations of implementing intelligent systems in enterprise financial operations are outlined: high requirements for the quality and structure of input data, complexity of interpreting forecast results, shortage of qualified personnel, and the need for significant computational resources. The study emphasizes the importance of developing appropriate digital infrastructure, organizational transformation, and interdisciplinary cooperation for the successful integration of such tools. International experience in the application of intelligent forecasting by leading companies in digital transformation is generalized. A classification of major categories of forecasting tools by functional purpose is proposed, and practical recommendations for their adaptation to the Ukrainian context are developed. The results obtained can be used as an analytical and methodological basis for modernizing enterprise financial management systems under the new economic reality.