Core Viewpoint - The public fund industry is facing a transformative wave driven by AI, which is seen as a necessary evolution rather than a mere trend, with AI becoming a critical factor for long-term industry development [1][2]. Group 1: Internal Demand Driving AI Adoption - The push for AI adoption in public funds is driven by the urgent need to address deep-seated industry pain points, with firms like Tianhong Fund emphasizing that AI is not just a trend but a means to break through existing challenges [2]. - The asset management industry has a significant demand for advanced AI technologies due to its reliance on data analysis and information processing, as highlighted by various fund companies [2]. - Traditional business models are in urgent need of digital transformation, and AI is seen as a key to enhancing competitiveness by efficiently processing vast amounts of information and automating processes [2][3]. Group 2: AI's Role in Enhancing Research Capabilities - AI is increasingly integrated into core business lines such as research, marketing, and customer service, leading to an upgrade in fund research capabilities [4]. - In active management, AI serves as a powerful assistant, helping to identify opportunities from vast data and providing quantifiable advantages in predictive modeling [4][6]. - AI can also challenge traditional thinking among fund managers, prompting them to consider risks and opportunities that may be overlooked by human intuition [6]. Group 3: Data Security and Model Reliability Concerns - As AI becomes more embedded in the industry, data security and model reliability have emerged as critical concerns, with analysts noting risks such as data leakage and compliance issues [7]. - Fund companies are actively working to establish robust data security measures, with Tianhong Fund implementing a multi-layered control system to ensure the reliability of AI conclusions [7]. - The need for accurate and secure data is paramount, as financial data is highly sensitive and involves customer privacy, necessitating careful management of data integration across institutions [7]. Group 4: Future Outlook for AI in the Industry - The future application of AI in the public fund industry is expected to evolve, focusing on relieving professionals from repetitive tasks and allowing them to concentrate on value creation [8]. - The industry is still in the exploratory phase of AI application, with potential advancements in building specialized models and enhancing collaboration across institutions [8]. - Companies like Yifangda Fund are setting examples in AI talent development, emphasizing the integration of technology, business understanding, and compliance awareness to drive financial AI innovation [8].

Core Insights - The public fund industry is facing a significant transformation driven by the adoption of AI technologies, which are seen as essential for long-term development rather than a mere trend [1][2] - AI is increasingly viewed as both a challenger to traditional fund management practices and a means to enhance research capabilities within the industry [1][4] Group 1: Internal Demand for AI - The push for AI adoption in public funds is primarily driven by the urgent need to address deep-seated industry challenges, such as efficiency improvement and risk control [2][3] - AI technologies are crucial for processing vast amounts of data and automating workflows, which are essential for enhancing competitiveness in asset management [2][3] - Institutions like Tianhong Fund have recognized that traditional models lead to diminishing returns as scale increases, and AI enables "intelligent scaling" to convert scale advantages into service capabilities [2][3] Group 2: AI's Role in Investment Research - AI is enhancing the investment research capabilities of fund companies by serving as both an assistant and a challenger to traditional thinking [4][5] - In active management, AI tools like Tianhong's TIRD platform and deep learning models from other firms are proving effective in identifying investment opportunities from large datasets [4][5] - AI can sometimes provide contrary signals to fund managers, which can help mitigate risks and protect performance, showcasing the value of human-machine collaboration [5][6] Group 3: Data Security and Model Reliability - As AI becomes more integrated into core business functions, data security and model reliability have emerged as critical concerns for the industry [6][7] - Challenges include data leakage, compliance risks, and the need for accurate and secure data management practices [6][7] - Firms are actively developing comprehensive security frameworks to ensure the reliability of AI-driven conclusions while maintaining data privacy [7] Group 4: Future Outlook for AI in Public Funds - The future application of AI in the public fund industry is expected to evolve, focusing on reducing repetitive tasks and enhancing value creation through better decision-making [7][8] - The industry is still in the exploratory phase of AI application, with potential advancements in specialized models and cross-institutional data collaboration [7][8] - Companies like E Fund are leading in AI talent development, emphasizing the integration of technology, business understanding, and compliance awareness to drive innovation [7][8]