肖远企：关注AI对整个金融结构变化的潜在影响

Core Insights - The potential impact of artificial intelligence (AI) on the financial structure is significant and requires ongoing observation to determine whether it leads to marginal changes, incremental reforms, or fundamental disruptions [1][2] - The interaction between finance and technology has historically been complementary, with AI now emerging as a leading application in the financial sector [1] Summary by Categories AI Applications in Finance - AI is primarily utilized in the financial industry to optimize business processes and enhance external services, focusing on three main areas: back-office operations, customer interactions, and financial product offerings [1] - In back-office operations, AI is widely applied within financial institutions, covering data collection, processing, information identification, and customer assessment [1] - AI enhances customer relationship management by improving marketing, maintenance, and problem-solving capabilities [1] - The application of AI in financial products yields dual benefits: internally, it reduces costs and increases efficiency; externally, it allows for more personalized and precise financial products and services [1] Talent and AI Limitations - Talent remains the most valuable asset in the financial sector, and despite the rapid development and widespread application of AI, its role is still supportive and cannot replace human decision-making in critical areas such as credit, insurance pricing, and actuarial tasks [2] Risks Associated with AI - The risks associated with AI in finance are still difficult to define, with historical technological revolutions primarily introducing incremental and marginal risks without fundamentally altering core risks like credit, market, liquidity, and operational risks [2] - From a micro perspective, individual financial institutions face new or incremental risks related to model stability and data governance [2] - From a macro perspective, the financial industry encounters concentration risk and decision convergence risk, with the potential for increased market concentration due to reliance on a few strong technology providers [2][3] - Decision convergence risk arises from the standardization and centralization of models and data, which may lead to homogeneous decision-making across the industry, potentially causing a "resonance" effect if convergence is too high [3]