arXiv:2606.03829v1 Announce Type: new Abstract: Financial-research answers are decision-relevant only when another analyst can audit how they were produced: which source was chosen, which period and accounting definition were used, which assumptions were made, and how the calculation was performed. Existing finance benchmarks largely evaluate isolated subskills or final answers, leaving the auditable derivation itself under-measured. We introduce BigFinanceBench, a 928-item expert-authored benchmark of open-ended financial-research tasks in which each item pairs a ground-truth reference answer
The proliferation of advanced AI models has highlighted the need for more robust, auditable, and workflow-grounded benchmarks, especially in high-stakes domains like finance.
This benchmark provides a critical tool for developing and evaluating AI agents capable of performing complex, auditable financial research, moving beyond isolated subtasks to functional workflow automation.
The standard for financial AI will shift from simple output verification to auditable process transparency, demanding more sophisticated and reliable agentic systems.
- · AI agent developers
- · Financial institutions adopting advanced AI
- · AI auditing and verification services
- · Researchers developing AI for financial workflows
- · Legacy financial research processes
- · AI models lacking strong auditability features
BigFinanceBench will accelerate the development of AI agents capable of end-to-end, transparent financial research.
Increased adoption of such agents could lead to significant efficiency gains and cost reductions in financial analysis, while also raising new regulatory questions around AI accountability.
The enhanced auditability could foster greater trust in AI-driven financial insights, potentially increasing the speed and volume of capital allocation decisions across markets.
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Read at arXiv cs.AI