The Adaptive Money Flow Management of Banks in the Era of Uncertainty and Risk

The study focuses on the development of adaptive money flow management scenarios for a commercial bank, integrated into the risk management system. The need to transition from reactive to proactive and adaptive money flow management is substantiated by sharp liquidity fluctuations, an increase in non-performing assets, and stricter requirements from the NBU for stress-testing and recovery planning. The aim of the article is to develop theoretical and methodological foundations and practical recommendations for creating flexible, multi-level money flow management scenarios capable of dynamically responding to changes in the risk environment. To achieve this aim, the article proposes a conceptual three-dimensional model of adaptive money flow management, which integrates the following components: key scenarios, dynamic models, and risk minimization strategies. As part of the study, a multi-level classification of risk events was developed across three main directions with clearly defined triggers for activating the corresponding management level; a simulation model of the bank’s money flows was created based on the system dynamics methodology, which quantitatively describes the interactions between key indicators and financial outcomes, allowing for the assessment of the impact of endogenous and exogenous factors. The simulation results demonstrated the bank’s profit is critically sensitive to increases in deposit interest rates, emphasizing the importance of finely tuning the passive policy. It was also found that proactive management of lending rates is an efficient tool for restoring financial balance. Detailed, scenario-based risk mitigation strategies were developed. They include recommendations on diversifying liabilities, implementing AI/ML models for proactive scoring and customer monitoring, and optimizing the maturity of assets and liabilities to reduce the structural liquidity gap. Implementing the proposed scenarios will enable the bank to operate proactively and sustainably, improve the accuracy of liquidity forecasts, and enhance capital efficiency under conditions of systemic uncertainty.