The article examines the problem of early detection of financial risks in the shipbuilding industry, which is characterized by high capital intensity, long production cycles and increased sensitivity to macroeconomic instability. It analyzes the potential of intelligent systems based on machine learning methods for proactive monitoring of managerial and accounting data. The role of adaptive algorithms in identifying hidden patterns, anomalies and pre-crisis states emerging long before their actual manifestation is emphasized. The article discusses architectural principles for designing a multi-layer modular platform, issues of integration with corporate information systems and the algorithmic support of the analytical layer. The practical significance of intelligent early warning systems for improving forecasting accuracy, stabilizing financial flows and enhancing the effectiveness of crisis management in shipbuilding enterprises is substantiated.
Bordusenko Dmytro (Fri,) studied this question.