Developing Risk Management Strategies in the Russian Credit System Based on Agent-Based Modelling

One of the important risk factors in the banking system is the "too big to fail" (TBTF) problem, which remains relevant today. This problem is related to the fact that large systemically important banks can take on excessive risks, relying on government support in case of financial difficulties. This work uses game-theoretic and agent-based models to develop strategies aimed at managing the risks of TBTF banks in the Russian financial system. The main goal is to create recommendations for the central bank to prevent destabilizing failures in the work of large banks. By modeling the complex interactions between banks using an agent-based model, this study validates various regulatory policies of the central bank and innovative approaches to overcoming the TBTF problem, seeking to establish a reliable approach that is relevant to the financial context of Russia and to develop effective recommendations. The results of the study showed that the Russian banking system is quite resilient to shocks. However, to further reduce risks and solve the TBTF problem, it is necessary to focus on improving the quality of management of state-owned banks. This will help reduce the level of moral hazard associated with the expectation of state support in case of financial difficulties, which is especially pronounced in the case of state-owned banks. This work emphasizes the multifaceted nature of the moral hazard problem in the context of TBTF in the banking environment and offers various techniques to address this problem.