Reducing the probability of losses through a systematic approach to risk management

Abstract

Introduction. Against the backdrop of ongoing changes in the financial and economic environment, growing uncertainty, digital transformation, and globalization, risk management is emerging as a key factor for business resilience and competitiveness. Traditional risk management approaches often prove insufficiently flexible in current turbulent conditions, underscoring the need for a transition towards comprehensive and adaptive systems capable of rapid response to new challenges. Goal. The article aims to develop and substantiate the advantages of using the Dynamic Risk Integration Model (DRIM) in organization risk management system. The banking sector was identified as the main application area for the model, though it demonstrates broad adaptation potential across other economic sectors and management levels. The key objective of the model is to establish an integrated risk management system for financial institutions and real-sector enterprises, improving the precision of strategic risk assessment. Materials and methods. The research was based on a comparative analysis of various risk management techniques and standards, including COSO ERM, ISO 31000, FAIR, System Dynamics, PMBOK, and SCRUM. By integrating the core principles and practices of these methodologies, the DRIM model was developed. It combines quantitative and qualitative analytical approaches, strategic planning, and operational flexibility. Results and discussion. The effectiveness of comprehensive approaches is confirmed by the practices of leading Russian companies. DRIM represents a holistic model incorporating advanced practices from modern risk management standards. The model enables not only the identification and assessment of risks but also the integration of analysis results into the strategic goals of the company. It demonstrates versatility and potential for adaptation to various management levels and industry sectors, including financial services and information technology. Conclusion. DRIM combines modern risk management methodologies, including adaptability, strategic planning, and quantitative analysis. It optimizes the overall system for managing banking risks, enhancing the resilience and competitiveness of banks in an unstable environment. DRIM encompasses methods for identifying, assessing, and managing banking risks, considers qualitative and quantitative aspects, and can be based on the use of machine learning, AI, and big data processing, making the system flexible to changes.