生成式AI的创新税：美国银行业部门生产率悖论与系统性风险
The Innovation Tax: Generative AI Adoption, Productivity Paradox, and Systemic Risk in the U.S. Banking Sector
AI总结：
本文研究了生成式AI在银行业中的应用对生产率和系统性风险的影响，发现采用AI的银行在生产率上表现优异，但面临显著的实施税，且规模经济在AI实施中起关键作用。
摘要：
本文评估了生成式人工智能（GenAI）采用对美国银行业部门生产率和系统性风险的因果影响。利用一个将SEC 10-Q文件与联邦储备系统监管数据联系起来的新数据集，涵盖了2018年至2025年间809家金融机构，我们采用两种互补的识别策略：动态空间杜宾模型（DSDM）以捕捉网络外溢效应，以及合成双重差分法（SDID）用于因果推断，使用2022年11月的ChatGPT发布作为外生冲击。我们的发现揭示了一个显著的“生产率悖论”：虽然DSDM估计显示采用AI的银行是高绩效银行（β>0），但因果SDID分析却记录了一个显著的“实施税”——采用银行在吸收GenAI整合成本时，ROE下降428个基点。这种税 disproportionately 偏向小型机构，底四分位银行ROE下降517个基点，而大型银行仅为129个基点，表明规模经济在AI实施中提供显著优势。最关键的是，我们的DSDM分析揭示了显著的正向外溢效应（ROA的θ=0.161，p<0.01；ROE的θ=0.679，p<0.05），其中大型银行之间的外溢效应达到θ=3.13，表明美国银行业系统正在变得“算法耦合”。这种由AI驱动的决策同步创造了一个新的系统性传染渠道：广泛采用的AI模型的技术故障可能触发整个金融网络中的相关冲击。
英文摘要：
This paper evaluates the causal impact of Generative Artificial Intelligence (GenAI) adoption on productivity and systemic risk in the U.S. banking sector. Using a novel dataset linking SEC 10-Q filings to Federal Reserve regulatory data for 809 financial institutions over 2018--2025, we employ two complementary identification strategies: Dynamic Spatial Durbin Models (DSDM) to capture network spillovers and Synthetic Difference-in-Differences (SDID) for causal inference using the November 2022 ChatGPT release as an exogenous shock. Our findings reveal a striking ``Productivity Paradox'': while DSDM estimates show that AI-adopting banks are high performers ($β> 0$), the causal SDID analysis documents a significant ``Implementation Tax'' -- adopting banks experience a 428-basis-point decline in ROE as they absorb GenAI integration costs. This tax falls disproportionately on smaller institutions, with bottom-quartile banks suffering a 517-basis-point ROE decline compared to 129 basis points for larger banks, suggesting that economies of scale provide significant advantages in AI implementation. Most critically, our DSDM analysis reveals significant positive spillovers ($θ= 0.161$ for ROA, $p < 0.01$; $θ= 0.679$ for ROE, $p < 0.05$), with spillovers among large banks reaching $θ= 3.13$ for ROE, indicating that the U.S. banking system is becoming ``algorithmically coupled.'' This synchronization of AI-driven decision-making creates a new channel for systemic contagion: a technical failure in widely-adopted AI models could trigger correlated shocks across the entire financial network.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
