Core Insights - The aging population in China is accelerating, leading to a diversified demand for elderly care services, with a focus on the development of inclusive and intelligent elderly finance [1][2] - Artificial intelligence (AI) is being integrated into the entire elderly finance chain, addressing issues such as high service thresholds, narrow coverage, and weak data support [1][2] Group 1: Demographics and Market Needs - By the end of 2024, the elderly population aged 60 and above in China is projected to reach 31.03 million, accounting for 22.0% of the total population, while those aged 65 and above will be 22.02 million, making up 15.6% [2] - The demand for specialized and precise elderly finance services is increasing as the aging population grows [2] Group 2: Role of AI in Elderly Finance - AI can lower the cost and threshold of elderly finance services, allowing for a broader reach to small and medium enterprises and flexible employment groups [2][3] - AI enhances the transparency and adaptability of elderly finance products, fostering consumer trust and engagement [3] - AI can integrate multi-source data for risk assessment and demand forecasting, optimizing product design and service delivery [3][4] Group 3: Challenges in AI Application - The application of AI in elderly finance faces challenges such as insufficient depth of use, unclear boundaries for data privacy protection, scarcity of high-quality financial data, and inadequate computational support [4][5] - Data sharing issues exist, with public data often fragmented and non-public data circulation being inefficient [4][5] Group 4: Collaborative Efforts Required - The development of elderly finance is a long-term endeavor that requires collaboration among government, market, society, and families [6][7] - There is a need for top-level design and institutional supply to drive the cross-sector development of AI in elderly finance [7] - Expanding public data sharing and establishing a national public database are essential for maximizing the value of data in elderly finance [7][8] Group 5: Technological Integration and Service Innovation - Companies are encouraged to build unified platforms that integrate health records, care documentation, and financial assets to provide personalized services [8] - The use of IoT and smart devices in various scenarios, such as health management and safety monitoring, is being promoted to enhance service efficiency and quality of life for the elderly [8]

在刘振龙看来，AI还能提升养老金融产品透明度与适配性，增强消费者的信任和参与感。他表示："依托大数据和机器学习，一方面企业和个人客户能够借 助AI更好地了解产品的运作和表现，另一方面也可以利用AI工具，根据不同的风险偏好、收入、年龄等要素，针对长住、旅居等多元的养老场景，精准为 客户提供'一人一策'的养老规划或者资产配置建议，提高整体的感受体验。" 养老金融不仅是简单的资金积累，更是将资金和技术作为纽带，整合金融、医疗、健康、科技、养老服务等多方资源，构建涵盖养老规划、健康管理、服务 对接等全生命周期的综合性服务的生态。浙大城市学院副教授林先平在接受北京商报记者采访时提到，AI能够整合多源数据（如健康记录、消费行为、金 融历史等），通过机器学习模型进行风险评估和需求预测，为养老金融产品设计、定价及服务优化提供科学依据，解决传统数据碎片化问题，优化资源配 置，提升服务体验。 应用深度不足、高质量数据稀缺 尽管AI为养老金融带来诸多想象空间，但其进一步应用仍面临现实瓶颈。刘振龙指出，智能化AI在养老金融领域的应用仍面临不少挑战，一是深度不足， 当前多数机构仅将AI用于智能客服，在风控、运营等核心业务环节的渗透率仍 ...