Internal Control of Financial and Economic Activities in the Military Unit
Abstract
Purpose. To substantiate effective mechanisms for organizing internal control of financial and economic activities in military units through the integration of modern digital technologies and machine learning methods.
Method: The study employs a systems approach, comparative analysis, and generalization, complemented by elements of mathematical modeling. To enhance the robustness of managerial decision-making, decision tree analysis and the Random Forest machine learning algorithm are applied for risk assessment and forecasting of financial outcomes.
Findings. The study reveals that the existing internal control system in military units is characterized by formalism, insufficient automation, and limited use of a risk-based approach. A comprehensive internal control model is proposed, combining traditional procedures with digital data analysis tools. The results demonstrate that the application of the Random Forest algorithm significantly improves the accuracy of financial risk detection and enhances the effectiveness of management decisions.
Theoretical implications. The study contributes to the development of scientific approaches to internal control by integrating risk management concepts with machine learning techniques in the military domain.
Practical implications. The proposed approaches can be implemented in the activities of financial and economic services of military units to improve transparency, financial discipline, and the efficiency of budget resource utilization.
Paper type. Applied research.
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