邮储、建行、工行集体出手！

Core Viewpoint - The article discusses the advancements in the deployment of the DeepSeek open-source large model by major banks in China, highlighting its role in enhancing financial services through intelligent upgrades and operational efficiencies [2][6][10]. Group 1: Deployment and Adoption - As of March 8, Industrial and Commercial Bank of China (ICBC) has completed the private deployment of the latest DeepSeek model, integrating it into its "ICBC Intelligent Surge" model matrix to enhance financial business scenarios [2][6]. - Over 20 banks have adopted the DeepSeek model, with major state-owned banks like Postal Savings Bank and China Construction Bank also initiating their deployments [3][8]. Group 2: Focus Areas of Application - Banks are focusing on four main areas for the application of DeepSeek: intelligent customer service upgrades, business process optimization, intelligent decision-making and risk management, and intelligent marketing and customer insights [4][12]. - DeepSeek is expected to replace repetitive tasks and enhance cognitive capabilities, driving business process optimization and innovation [4][16]. Group 3: Specific Implementations - ICBC has empowered over 20 major business areas with the DeepSeek model, implementing more than 200 practical scenarios, including a smart dialogue trading product and a remote banking assistant that improves service efficiency by reducing call durations by approximately 10% [6][12]. - Postal Savings Bank has integrated DeepSeek models to enhance its "Little Postal Assistant," improving service efficiency and customer experience through advanced logical reasoning capabilities [9][13]. Group 4: Future Implications - The integration of DeepSeek into banking services signifies a shift from "informationization" to "cognition" in financial services, indicating a transformative phase in how banks interact with customers and manage operations [16][17]. - The technology is expected to reshape the banking industry's approach to AI applications, focusing on personalized customer interactions and efficient resource allocation [17][19].