2025外滩年会圆桌讨论聚集金融科技，中外嘉宾认为 “AI+金融”尚处早期 提效同时应关注风险

Core Insights - The application of AI in the financial sector is still in its early stages, with both efficiency improvements and potential risks being highlighted by industry experts [1][7] - The integration of AI into financial processes is expected to bring significant marginal changes, particularly in optimizing operations and enhancing customer service [2][4] Group 1: AI Applications in Finance - AI is being utilized primarily in three areas: back-office operations, customer relationship management, and financial product offerings [2] - In back-office operations, AI has been widely adopted for data collection, processing, information identification, and customer assessment [2] - AI enhances customer interactions by improving marketing, maintenance, and problem-solving capabilities [2] Group 2: Risks Associated with AI - The introduction of AI brings new systemic risks and channels for risk transmission, which need careful evaluation [5] - From a micro perspective, financial institutions face model stability and data governance risks, while from a macro perspective, they encounter concentration and decision convergence risks [5] - Concentration risk arises from reliance on a few strong technology providers, potentially increasing market concentration [5] - Decision convergence risk may lead to homogeneity in decision-making across the industry, which could trigger a "resonance" effect if excessive [5] Group 3: Impact on Monetary Policy - The influence of AI on monetary policy requires long-term observation, as its effects are not yet clearly defined [6][7] - AI can impact monetary policy decisions through data collection and pattern recognition, but monetary policy adjustments are typically slow and based on economic cycles [7] - The International Bank for Settlements (BIS) has discussed the potential effects of AI models on monetary policy, concluding that the impact is currently not significant [7]