Core Viewpoint - Jiangnan Rural Commercial Bank is actively transforming its risk management approach by leveraging digitalization to create a multi-dimensional, layered, and full-cycle intelligent risk warning system, aiming to shift from passive response to proactive defense in risk management [1] Group 1: Digital Transformation and Data Integration - Before digital transformation, the bank relied on manual surveys and reports for risk warning, which led to delays in identifying credit risks [2] - The bank has established a credit risk warning system that integrates internal and external data sources, including credit systems from the People's Bank, tax departments, and third-party data providers, creating a multi-dimensional data system [2] - A data cleaning and preprocessing mechanism has been implemented to standardize data formats and improve data quality and usability [2] Group 2: Intelligent Warning Model Development - The bank has developed a matrix of intelligent warning models using machine learning and artificial intelligence, combining expert experience with data patterns [3] - Pre-loan models identify potential high-risk clients at the entry point, while in-loan models monitor client behavior and credit status in real-time [3] - Post-loan models detect abnormal fund flows and potential risk contagion, allowing for early warning and intervention [3] Group 3: Risk Signal Management - A four-level classification and response mechanism for risk signals has been designed to prevent "alarm fatigue" from excessive alerts [5] - The classification includes: - Red alerts for severe expected losses requiring urgent action [6] - Orange alerts for significant loss potential needing immediate measures [7] - Yellow alerts for developing risks requiring proactive safety measures [8] - Blue alerts for notifying managers to investigate potential risks [9] Group 4: System Integration and Process Embedding - The risk warning system has been seamlessly integrated with various business processes, creating a comprehensive risk prevention network [10] - Risk signals are embedded in decision-making processes, enhancing the effectiveness of risk management during loan approvals and post-loan monitoring [10] Group 5: Core Management Mechanisms - The bank has established five core management mechanisms to ensure the efficient and secure operation of the warning system, including: - A blacklist mechanism for high-risk clients [11] - A risk supervision mechanism for significant risk signals [11] - A penetration mechanism for associated risks [11] - An emergency mechanism for major risk signals [11] - An information sharing and confidentiality mechanism to enhance collaborative risk management [11] - The intelligent risk warning system represents a comprehensive transformation in risk management philosophy, organization, and culture, aiming for a more intelligent, precise, and agile risk management ecosystem [11]