“AI+金融”系列专题研究（二）：应用场景打开，AI助推金融机构内部效率与外部价值双升

Investment Rating - The report suggests a positive investment outlook for the AI and financial services sector, highlighting the potential for significant advancements and cost reductions due to the release of DeepSeek R1 in 2025, which is expected to be a turning point for localized AI deployment in financial institutions [7]. Core Insights - AI applications are rapidly penetrating core business areas and back-office functions of various financial institutions, enhancing both internal efficiency and external value [1][7]. - The report identifies that most financial institutions are currently in the exploration and accumulation phase of AI application, with deep application being an inevitable trend [14]. - AI is expected to transform financial business processes and organizational structures, ushering in a new era of digital intelligence in finance [7]. Summary by Sections Investment Recommendations - The report recommends focusing on several sectors within the financial industry, including: 1. Financial information services with key stocks like Tonghuashun, Jiufang Zhitu Holdings, and Guiding Compass [8]. 2. Third-party payment services, recommending stocks such as Newland and Newguodu, with related stocks like Lakala [9]. 3. Banking IT, with recommended stocks including Yuxin Technology, Jingbeifang, and Guodian Yuntong [9]. 4. Securities IT, recommending stocks like Hengsheng Electronics and Jinzhen Shares [10]. 5. Insurance IT, with recommended stocks including Xinzhi Software and Zhongke Software [11]. Application Stages - Financial institutions' AI applications are categorized into three stages: 1. Initial exploration of large model applications. 2. Development of certain model application capabilities with data accumulation. 3. Achieving deep application of large models [14]. Application Value - AI applications provide value through: 1. Internal cost reduction and efficiency improvement, optimizing operational management and core business processes [21]. 2. External value extraction, enhancing marketing and customer service to improve sales conversion and customer value [21]. Application Pathways - Different types of financial institutions exhibit varied pathways for AI application deployment: 1. Large institutions leverage strong self-research capabilities for deep AI application penetration. 2. Smaller institutions focus on cost-effective solutions, utilizing lightweight models and integrated systems for agile development [26]. AI Empowerment in Banking - AI is enhancing front-office quality and efficiency, optimizing back-office processes across various banking functions [43]. - In credit risk management, AI models can analyze financial data to identify potential risks and improve decision-making processes [47]. AI Empowerment in Securities - The number of securities firms exploring large models is rapidly increasing, with applications extending across various business functions, including investment advisory and research [58][59].