arXiv:2605.22664v2 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions. To meet enterprise needs, frontier AI labs have developed agents that can construct entire spreadsheets from scratch. This is especially relevant in finance, where core workflows such as financial modeling, forecasting, and scenario analysis are commonly conducted through spreadsheets. Yet, existing spreadsheet benchmarks do not measure this advanced capability, focusing instead on question-answering or singl
The proliferation of advanced LLMs is naturally leading to their application in complex, multi-step workflows, addressing the demand for automation in high-value enterprise tasks like financial modeling.
This development signifies a substantial leap in AI agent capabilities, moving from question-answering to end-to-end task execution, particularly in critical financial domains.
AI is no longer just assisting with data interpretation but is now capable of autonomously constructing and manipulating complex financial artifacts, threatening to automate significant portions of traditional white-collar finance roles.
- · Frontier AI labs
- · Financial institutions adopting AI agents early
- · Productivity software developers (LLM-integrated)
- · Junior financial analysts
- · Spreadsheet software vendors without strong AI integration
- · Traditional BPO providers
Increased automation of financial modeling and forecasting streamlines operations and reduces human error.
A significant re-skilling challenge for the financial sector as human roles shift from execution to oversight and strategic input.
Potential for new financial instruments and analysis methods enabled by hyper-efficient AI-driven scenario planning, leading to faster market dynamics.
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Read at arXiv cs.AI