浦发银行薛宏立：银行要做全要素生产率革命的“催化剂”

Core Viewpoint - The core viewpoint emphasizes that commercial banks must adapt to a new growth paradigm focused on enhancing total factor productivity through technological innovation and optimized resource allocation, transitioning from traditional financing for expansion to providing support for innovation and transformation [1][2]. Group 1: Top-Level Design - The strategic goals of commercial banks should align deeply with national priorities, moving beyond traditional financial metrics to establish a comprehensive strategic goal system that supports high-quality development [1]. - Resource allocation must shift from serving tangible assets to enhancing intangible assets, creating a new resource allocation system that integrates technology, scenarios, and value [1][2]. - Organizational structures need to adapt to functional transformations, fostering agility, collaboration, and professionalism to enable a closed-loop operation driven by industry research and customer needs [2]. Group 2: Business Innovation - Commercial banks should focus on three main lines: breakthroughs in technological innovation, upgrading industrial structures, and enhancing openness, creating a diversified and comprehensive financial service system [2]. - In the field of technology finance, banks need to develop a toolbox of products that align with the innovation lifecycle, offering tailored financial products for startups, growth-stage companies, and mature enterprises [2]. - The approach to industrial finance should transition from serving individual enterprises to supporting entire industrial chains, ensuring financial services effectively reach all levels of the industry [2]. - In cross-border finance, banks should expand their global service networks to support enterprises' integration into global production chains, innovating products and services for cross-border settlements, financing, and currency risk hedging [2]. Group 3: Risk Control - The risk landscape for the real economy has changed significantly, necessitating the development of a risk control model that aligns with quality enhancement [3]. - A data-driven intelligent risk control system should be established, shifting focus from tangible assets to intangible assets like patents and intellectual property, and evolving from post-event identification to preemptive warning [3]. - A differentiated risk management approach is essential, avoiding a one-size-fits-all strategy and tailoring risk control standards to different industries, scales, and development stages [3]. - Strengthening risk control capabilities through technology integration, such as knowledge graphs and machine learning, is crucial for real-time risk assessment and management [3].