监管明确加快发展“人工智能+金融”，银行如何布局？

Core Viewpoint - The implementation plan for the high-quality development of digital finance in the banking and insurance sectors emphasizes the integration of artificial intelligence and other new technologies to enhance financial services and optimize resource allocation [1][5]. Policy Guidance - The plan builds on previous guidelines and introduces new elements such as "AI+" and "data elements ×", outlining specific requirements across six areas including governance, service empowerment, and risk prevention [5][8]. - Financial institutions are encouraged to develop enterprise-level AI platforms to enhance modeling and application capabilities, while exploring advanced technologies like quantum computing and blockchain [5][8]. - The focus is on aligning technological innovation with business needs to create a positive cycle of technology-driven business development [5][8]. Practical Exploration - Several listed banks have actively integrated AI into customer service, risk control, and marketing, with applications such as intelligent assistants and credit AI agents [6]. - For instance, China Merchants Bank reported that its AI assistant "AI Xiao Zhao" served over 61,300 corporate clients and 458,500 individuals by mid-2025 [6]. - Industrial banks like Industrial and Commercial Bank of China have developed AI-driven tools for risk control and marketing, enhancing their operational efficiency [6][7]. Technology Resource Allocation - The plan provides clear guidance on technology resource allocation, urging banks to focus on core business areas and increase R&D investment [8]. - It aims to break down barriers between technology and business departments, fostering collaboration and innovation [8]. - The plan also emphasizes the importance of building a skilled workforce in digital finance, advocating for the training of professionals in data analysis and regulatory technology [8][9]. Talent Development - The plan highlights the need for cultivating a workforce that understands both finance and technology, rather than solely pursuing top algorithm talent [9]. - It suggests that banks should prioritize data governance based on business needs to enhance model quality [9].