Cognitive modeling in bank investment risk management

Abstract

In the conditions of globalization and digital transformation of financial markets, the bank's investment risk management becomes especially relevant. Traditional methods of risk assessment do not always provide sufficient prediction accuracy, which necessitates the use of innovative approaches, in particular cognitive modeling. The relevance of cognitive modeling research in bank investment risk management is due to the growing complexity of financial markets and the need to develop effective risk forecasting tools. Traditional methods of investment risk management often do not take into account the nonlinearity of financial processes and the complex interrelationships between macro- and microeconomic factors, which reduces their effectiveness in unstable conditions. Cognitive modeling, based on the principles of artificial intelligence, fuzzy logic and cognitive maps, opens up new opportunities for advanced analysis and forecasting of risks in banking. The purpose of the article is the development and application of cognitive modeling to increase the efficiency of investment risk management of banking institutions, which will improve the accuracy of risk forecasting and increase the financial stability of the bank. Methods. The work uses the method of fuzzy logic to assess uncertain factors that may affect the bank's financial risks. In addition, a multifactor analysis was performed, which made it possible to determine the most significant risk factors and their impact on the effectiveness of investment decisions. As a result of the research, a cognitive model of the bank's investment risk management is proposed, which includes dynamic interrelationships between macroeconomic, market and internal banking factors. The use of cognitive modeling makes it possible to increase the efficiency of the bank's investment risk management due to the integration of modern methods of data analysis and artificial intelligence. Conclusions. The proposed models can be used to improve the risk management system of banking institutions, which will contribute to reducing the probability of financial losses and increasing the bank's competitiveness. The practical implementation of the research results is recommended for banks operating in conditions of high market volatility and seeking to increase the accuracy of risk assessment by using innovative methods of analysis.