2025年迈向智能驱动新纪元，大语言模型赋能金融保险行业的应用纵览与趋势展望报告-众安信科

Group 1 - The report by Zhong An Technology and Zhong An Financial Technology Research Institute explores the application of large language models (LLMs) in the financial and insurance industries, concluding that LLMs present new opportunities but face challenges in implementation that require multi-party collaboration [1] - The development of large model technology is diversifying globally, with vertical models emerging to provide tailored industry solutions. China has made progress in computing autonomy and data optimization, leading to a trend of functional differentiation and specialization in its ecosystem [1][24] - New technologies are driving down the costs of training, operation, and inference for large models, prompting a restructuring of processes in the financial industry. Financial enterprises need to balance acquisition, inference, and operational costs while selecting appropriate deployment models and roles [1][12] Group 2 - Domestic models like DeepSeek and Tongyi Qianwen have achieved breakthroughs in cost control and inference performance, providing better technical options for insurance institutions while ensuring data security and compliance [1][15] - Insurance institutions are accelerating the integration of large models, focusing on internal efficiency improvements across the entire insurance business chain and back-office management. Caution is advised during pilot applications to address data security and AI hallucination issues [1][16] - The value of data elements is becoming more prominent, with the financial and insurance industries building high-quality datasets through horizontal, vertical, and government-enterprise collaboration mechanisms to promote intelligent transformation [1][19] Group 3 - The application of large language models in the financial and insurance sectors is transitioning from pilot exploration to systematic integration, with initial deployments focusing on low-risk, low-intervention auxiliary business scenarios such as intelligent customer service and smart claims [6][7] - The introduction of large language models is not only enhancing process efficiency but also driving a deep transformation in information processing paradigms and decision-making logic within the industry [8][9] - The rise of large language models is reshaping the operational philosophies, business logic, and value creation models of financial institutions, leading to trends such as precision financial services and cross-industry ecological collaboration [9][10] Group 4 - The evolution of large model technology is characterized by a shift from purely algorithmic breakthroughs to the construction of systemic capabilities that integrate model deployment, business processes, and system interfaces [29][30] - The deployment capabilities of large models are transitioning from "usable" to "adaptable," with future competition likely focusing on building flexible deployment mechanisms across architectures and scenarios [31] - The emergence of vertical large models is addressing the specific needs of industries like finance and healthcare, enhancing precision and efficiency in tasks such as risk assessment and compliance checks [40][41]