Risk Modeling of Banking Activities

The article is devoted to the study of the issue of banks’ risk management. Risk management is a complex but necessary process to ensure the financial stability of banks. Research and assessment of banks’ risks helps banks understand and manage risks, make more informed decisions about granting loans, investing and other operations. Risk modeling is an important part of risk management in banks, so the article proposes improving the methodology for assessing the level of risks of banking activities, which, on the basis of forecasting methods and simulation modeling, allows to improve the validity and quality of managerial decisions in the field of management of financial activities of a bank. The task of risk assessment is a central element of the risk management system in banks. In order to improve the methodological base, the article proposes a conceptual scheme of modeling the risks of banking activity, which includes four blocks: development of currency risk assessment models; development of credit risk assessment models; stress testing of banking risks; making decisions to stabilize the financial condition of the bank in each of the proposed scenarios of modeling the risks of banks’ activity. In accordance with this, on the basis of the carried out study of the bank’s financial statements and methods of assessment and analysis of risks of banking activity, the following simulation models have been developed: a model for assessing the level of currency risk; a model for assessing credit risk and determining the amount of its reserve according to national and international standards; a complex risk assessment model. After building and examining the models, the methods of system dynamics were used, namely: simulation modeling, stress testing, and sensitivity analysis. Simulation modeling has become an effective tool for studying the risks of banking activities, as it made it possible to create virtual models of banking operations and test them on various risk scenarios. Stress testing allowed to study the impact of shock events on the financial stability of the bank. Sensitivity analysis was used to identify the most sensitive parameters of the model and to determine how changing these parameters affects the model results.