Core Viewpoint - The introduction of large models provides a new engine and opportunities for the restructuring of the securities industry paradigm [1][2]. Digital Transformation - Accelerating digital transformation is a crucial task for securities companies in the new era, with a historical trend of high integration between technology and business [4]. - The industry is undergoing a profound transformation from digitalization to intelligence, focusing on enhancing customer service and information decision-making efficiency through data and AI [4][5]. Value Creation - The key to transforming large models into a new productive force in the securities industry lies in achieving a qualitative change from efficiency improvement to value creation [5][6]. - The true value of AI is not merely in cost reduction but in providing intelligent products and services that seamlessly integrate into business systems, enhancing customer experience and driving revenue growth [5][6]. Practical Applications - Three areas for integrating large models with business are identified: reshaping service experience, innovating products, and empowering risk control [6]. - The development of a smart document review system addresses long-standing issues in information disclosure quality, achieving a semantic error detection rate of 90% and a grammar/spelling error detection rate of 85% [7]. Implementation Strategy - The core of applying AI in the securities industry is summarized as "value-oriented, scenario-driven" [9]. - A 12-character "secret" for selecting application scenarios is proposed: "small cuts, large depth, high density, and integration" [9]. - Emphasis is placed on integrating large models into existing workflows to enhance user experience and create an "integrated product" [9]. Future Outlook - AI is expected to create multiplier effects, generating new economic and social value, with the vision of equipping every client with an AI advisor and every employee with a digital twin assistant [10].