民营银行竞渡：欲乘AI方舟先算成本账

Core Insights - The attitude of the banking industry towards the technology wave has shifted from "whether to embrace" to "how to embrace" AI, with a focus on achieving business effectiveness [1] - The successful integration of AI technology into core business scenarios is crucial for private banks to break away from traditional business models and enhance customer acquisition efficiency [1] - Despite the potential benefits, the implementation of AI technology faces obstacles such as security, cost, and effectiveness concerns [1][5] Performance Metrics - Several private banks, including Zhejiang Wangshang Bank, Jiangsu Shushang Bank, and Sichuan Xinwang Bank, have emerged as leaders, achieving significant revenue and profit figures in the first half of 2025 [2] - Zhejiang Wangshang Bank reported operating income of 100.05 billion and net profit of 20.47 billion, while Jiangsu Shushang Bank and Sichuan Xinwang Bank reported 34.12 billion and 4.86 billion, and 29.36 billion and 4.19 billion respectively [2] - In contrast, many other private banks reported lower performance, with operating income mostly below 15 billion and net profit below 4 billion [2] Industry Disparities - The performance disparity among private banks is attributed to differences in resource endowment, ecological support, and technological capabilities [3] - Leading banks like Wangshang Bank benefit from backing by large ecosystems such as Alibaba, allowing them to efficiently acquire a vast customer base and leverage big data [3] - Many mid-tier banks rely on traditional lending and deposit services, resulting in weaker risk resistance and profitability [3] Competitive Landscape - Private banks primarily focus on retail credit, serving small and micro enterprises and individual consumers, but face increasing competition as other banks also target these segments [4] - Some banks resort to high-interest deposits to attract customers, which can increase operational costs and pressure profits [4] - The industry consensus suggests that private banks should leverage financial technology to cover a broader customer base and reduce costs [4] AI Implementation Challenges - The integration of AI technology into core business remains a significant challenge, with concerns over security, cost, and effectiveness being primary obstacles [5] - Smaller banks face tighter IT budgets compared to larger banks, making it difficult to invest in comprehensive AI systems [5] - The lack of successful case studies for embedding large model technology into core business scenarios further complicates the situation [5] Recommendations for AI Adoption - Banks should focus on high-value scenarios that address specific pain points and yield quick results from AI applications [6] - Lightweight deployment strategies, such as using smaller models for customer interactions, should be prioritized [6] - Strengthening data governance and fostering ecosystem cooperation are essential for successful AI implementation [6]