Group 1 - The core issues in financial software development include the completeness and accuracy of business models, and the need to address the "hallucination" problem in industry models during training [2] - The future of software development is envisioned as a collaboration between human modelers and AI programmers, enabling all business personnel to directly generate software [2] - AI technology is seen as a revolutionary opportunity to overcome bottlenecks in core banking systems, allowing for personalized services and driving banks to return to their service essence [3] Group 2 - AI-driven evolution in credit business is proposed to address data, efficiency, and risk gaps in traditional lending models, focusing on intelligent risk control and process reengineering [3] - The dual-track evolution strategy for electronic channels aims to differentiate retail and corporate banking approaches, promoting a shift from function-oriented to value-oriented banking [4] - The establishment of a new generation of core systems for regional banks aims to enhance competitiveness and integrate technology standards with industry practices [6] Group 3 - Data governance and its transformation into valuable assets are highlighted as critical, with a focus on high-quality supply and the integration of AI technologies [6] - The development of a dynamic management system for asset-liability management is essential for banks to transition from passive to proactive strategies in response to narrowing interest margins [7] - The emphasis on enhancing digital capabilities is crucial for banks to withstand challenges and drive high-quality development in the industry [7]