Research on the Application of Knowledge Graphs in Bank Risk Management

In the era of globalization and digitalization, the banking industry plays a crucial role in economic development through its stability and efficiency but faces various traditional and emerging risks. The development of artificial intelligence has brought revolutionary changes to bank risk management. Knowledge graph technology, which constructs a graph structure of entities and their relationships, provides new perspectives for risk identification and analysis. This study explores the application of knowledge graph technology in bank risk management using Neo4j, demonstrating its advantages in risk identification, assessment, and prediction. By leveraging the interconnected nature of data in a graph database, banks can uncover hidden patterns and relationships that traditional methods might overlook. This approach enables a more comprehensive and dynamic understanding of risk factors, allowing for proactive management and mitigation. Additionally, the use of Neo4j's advanced querying capabilities facilitates real-time analysis and visualization of complex risk scenarios, further enhancing decision-making processes in the banking sector. The integration of machine learning with knowledge graphs can also predict future risks with higher accuracy, making it an invaluable tool for modern risk management practices.