Paper: arXiv 2602.02607

Authors: Tatsuru Kikuchi

Abstract

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents a rigorous empirical investigation into the productivity and systemic risk implications of Generative AI in the U.S. banking sector. Combining novel SEC 10-Q text data with regulatory filings, it uses advanced econometric tools—Dynamic Spatial Durbin Models and Synthetic Difference-in-Differences—to identify causal effects and network spillovers, resolving the AI productivity paradox through the lens of implementation costs.

Research Flowchart

  flowchart TD
    A["Research Goal: Assess GenAI Impact on Banking Productivity & Systemic Risk"] --> B["Data Integration (2018–2025): SEC 10-Q Filings + Fed Regulatory Data (809 Banks)"]
    
    B --> C["Causal & Network Modeling"]
    
    C -->|"Exogenous Shock: Nov 2022 ChatGPT Release"| D["Synthetic Difference-in-Differences (SDID)"]
    C -->|"Network & Peer Spillovers"| E["Dynamic Spatial Durbin Models (DSDM)"]
    
    D --> F["Implementation Tax Identified:<br/>Direct ROE drops by 428 bps<br/>(Small Banks: -517 bps vs Large: -129 bps)"]
    E --> G["Systemic Algorithmic Coupling:<br/>Positive Spillovers (θ = 3.13 for Large Banks ROE)<br/>Heightened Contagion Risk"]
    
    F --> H["Outcome: Productivity Paradox & Scalable Systemic Vulnerability"]
    G --> H