Large-Language-Model Copilots on the Trading Floor: Impacts on Price Discovery, Conduct Governance, and Desk Productivity
Major sell-side institutions have begun embedding large-language-model (LLM) “desk copilots” such as Bank of America’s Maestro and Goldman Sachs’ GS AI Assistant into sales-and-trading workflows to synthesize internal research, client flow data, and market-microstructure signals in real time (Financ...
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| Published in | International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences Vol. 13; no. 4 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
31.07.2025
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| Online Access | Get full text |
| ISSN | 2349-7300 2349-7300 |
| DOI | 10.37082/IJIRMPS.v13.i4.232668 |
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| Summary: | Major sell-side institutions have begun embedding large-language-model (LLM) “desk copilots” such as Bank of America’s Maestro and Goldman Sachs’ GS AI Assistant into sales-and-trading workflows to synthesize internal research, client flow data, and market-microstructure signals in real time (Financial News London, 2024; Reuters, 2024). This review paper surveys the emerging body of academic, regulatory, and practitioner literature on generative-AI trade assistants (GATAs), framing their potential to reshape pre-trade analytics across equities, foreign exchange, and derivatives markets. We synthesize findings on three core dimensions—information asymmetry, order-routing efficiency, and conduct-risk controls—and propose a conceptual evaluation framework to guide regulators and market participants. The paper concludes by identifying open research questions around model governance, fairness, and systemic risk propagation. |
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| ISSN: | 2349-7300 2349-7300 |
| DOI: | 10.37082/IJIRMPS.v13.i4.232668 |