Exploring cognitive patterns in credit default risk management

Abstract

The significance of the chosen research topic arises from the growing complexity of economic exchanges, stricter requirements for banking supervision, and the increasing necessity to enhance risk management. In the context of global financial uncertainty and the accelerated flow of information, traditional methods of assessing banking risk may no longer be adequate. This study explores innovative frameworks capable of autonomously processing large datasets, predicting potential hazards, and providing effective mitigation strategies. The research is grounded in general scientific methods such as analysis, synthesis, classification, and bibliographic review. The findings suggest that incorporating cognitive models into banking risk management signifies a shift from traditional practices toward more adaptive and predictive approaches. Although these models show considerable potential for improving banking risk practices, they remain underutilized in the financial sector. The cognitive framework proposed in this study may significantly enhance decision-making efficiency and reduce liabilities, offering practical value, particularly in dynamic market conditions.