Group 1: Introduction and Challenges - Internal audit plays an irreplaceable role in the internal control system of securities companies, impacting governance structure, operational stability, and value growth [1] - The digital transformation in auditing is driven by the integration of artificial intelligence (AI) technology, transitioning from a "workshop" model to an "intelligent factory" model [1] - Challenges in the intelligent transformation of auditing include data processing difficulties due to explosive data growth and a lack of risk identification capabilities in traditional auditing methods [1][2] Group 2: Transformation: AI Empowering Securities Audit - The theoretical foundation for AI in auditing includes information processing theory, synergy effect theory, pattern recognition theory, and machine learning theory, which support efficient data handling and risk identification [3] - AI can support the entire lifecycle of audit operations, from risk assessment and data integration to generating clear audit reports and tracking rectification processes [4][5] Group 3: Knowledge Management and Inheritance - The introduction of AI can help build an audit knowledge base, systematically organizing fragmented knowledge and improving retrieval efficiency for auditors [6] - Machine learning algorithms can analyze data within the knowledge base to provide decision support and continuously update the knowledge system to keep pace with industry developments [7] Group 4: Outlook: Future of Securities Audit - The future of securities audit will focus on enhancing the business logic modeling capabilities of large models, developing specialized models for specific sectors, and improving model interpretability [8] - A dynamic learning mechanism will be established to optimize models based on practices from various branches while ensuring data privacy, leading to a more efficient, intelligent, and trustworthy audit process [8]