Indicative assessment of the risk of unbalanced liquidity as a tool to increase the stability of the Russian banking sector in a crisis

this article presents an analysis of the risk of unbalanced liquidity as a key tool for improving the stability of the Russian banking sector in a crisis. The study is based on an extensive review of scientific publications and practical cases, focusing on the dynamic nature of liquidity and its impact on the financial stability of banks. Special attention is paid to the relationship between the level of debt, prices of goods and services, as well as the money supply as factors affecting liquidity. The risks arising from non-payment of loans or withdrawal of deposits, which makes banks vulnerable to financial shocks, are considered. The contradiction between high liquidity and profitability is noted: the desire to increase profits can lead to a decrease in liquidity due to investing in riskier assets. The interaction between liquid and illiquid assets is investigated, which emphasizes the need for balanced management to minimize risks associated with a shortage or excess of liquidity. Based on the analysis, it is concluded that effective liquidity management is important, which requires the use of methods such as the regulatory method, stress testing, GAP analysis and asset and liability management (ALM). A detailed analysis of the most common methods is proposed, as well as the development of an integrated model for assessing the risk of unbalanced liquidity, taking into account internal and external factors. The model includes a multi-level data collection system, multidimensional analysis and the creation of adaptive stress scenarios using machine learning for early warning of potential risks. This work highlights the need for an integrated approach to liquidity management, which will increase the stability of the banking sector in conditions of economic uncertainty and ensure its sustainable development.

adaptive scenarios, banks, crisis, macroeconomic factors, machine learning, unbalanced liquidity, regulatory method, liquidity risk, stress testing, GAP analysis, sustainability, financial indicators