A MECHANISM FOR CYCLIC-DYNAMIC SCREENING OF PRIMARY FINANCIAL MONITORING ENTITIES UNDER WARTIME CONDITIONS

This study aims to develop a mechanism for cyclic-dynamic screening of the activities of primary financial monitoring entities (PFMEs) under wartime conditions. The proposed mechanism identifies atypical behavioural patterns, enhances responsiveness to rapidly evolving risks, and improves the quality of financial offence detection. The study constructed additive time series models for two distinct periods: pre-war (2011–2019) and wartime (2021–2024). Smoothing techniques, time series decomposition, seasonal component estimation, and analytical trend modelling were applied. In the first period, the model demonstrated high accuracy (coefficient of determination R² = 90,87%), while in the second period, the accuracy remained acceptable (R² = 65,80%), reflecting increased volatility in the financial environment. The results highlight the importance of analyzing fluctuations and trends to detect suspicious financial transactions on time and to improve the effectiveness of primary financial monitoring procedures during wartime. The proposed mechanism enhances the accuracy of identifying anomalous operations, enables adaptive risk response, and strengthens the monitoring and control of financial flows. The practical implementation of this methodology contributes to ensuring national financial stability and security, which is critically important for maintaining the integrity of the state's financial system under wartime conditions.