FORCE-Bench: A Benchmark, Dataset, and Evaluation Harness for Agentic AI in Enterprise Finance
Quick Answer
FORCE-Bench introduces a specialized benchmark for evaluating agentic AI in finance, featuring 251 expert-annotated queries across eight dimensions.
Quick Take
Results indicate that general-purpose systems often fail to meet finance-specific quality standards, while the Finance Agent for Microsoft 365 Copilot performs reliably. The dataset and evaluation tools are released as open-source for broader application.
Key Points
- FORCE-Bench evaluates agentic systems on accuracy, citations, clarity, and more.
- The benchmark includes tasks like financial obligation research and business brief generation.
- General-purpose agents struggle under operational constraints in finance environments.
- The Finance Agent for Microsoft 365 Copilot outperforms general-purpose systems.
- All resources, including the dataset and rubrics, are available as open-source.
DeepSignal Analysis
What happened
FORCE-Bench has been introduced as a benchmark for evaluating agentic AI systems in the finance sector. It includes 251 expert-annotated queries across eight dimensions relevant to operational finance. The evaluation revealed that general-purpose AI systems often do not meet the specific quality standards required in finance, while the Finance Agent for Microsoft 365 Copilot performed reliably.
Key evidence
- FORCE-Bench evaluates agentic systems using a rubric-based framework across eight dimensions: accuracy, citations, clarity, depth, groundedness, recency, relevance, and structure.
- The benchmark assesses three task types: financial obligation research, financial entity performance research, and business brief generation, reflecting real deployment conditions.
- Results indicated that general-purpose agentic systems do not consistently meet finance-domain quality requirements, while the Finance Agent for Microsoft 365 Copilot was more reliable.
Why it matters
The introduction of FORCE-Bench addresses a gap in existing benchmarks that typically focus on general capabilities rather than the specific needs of operational finance. This specialized evaluation framework is crucial for ensuring that AI systems can provide accurate, verifiable, and rule-compliant information in financial contexts. The open-source release of the dataset and evaluation tools allows for broader application and adaptation, potentially improving the deployment of AI in finance.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Recent advances in large language models have accelerated deployment of agentic systems in operational finance. Existing benchmarks emphasize measuring general capabilities, instruction following, or safety, but few directly address the operational finance workflows that agentic systems are now being deployed to automate. Finance professionals require agents to not only provide factually sound and properly grounded information, but also ensure that this information is verifiable and consistently adheres to rules and constraints of the operational finance domain. We introduce FORCE-Bench, which contains 251 expert-annotated queries and evaluates responses using a rubric-based framework calibrated to the requirements of the operational finance domain, across eight dimensions: accuracy, citations, clarity, depth, groundedness, recency, relevance, and structure. FORCE-Bench assesses agentic systems on three task types: financial obligation research (querying ERP systems for accounts receivable and payable data), financial entity performance research (answering time-bound questions from public filings and market data), and business brief generation (synthesising multi-source company intelligence reports). To reflect real deployment conditions, we evaluate our purpose-built agent, as well as the general-purpose agentic systems, under common tool access and latency-bounded settings. Results show that general-purpose agentic systems do not consistently meet finance-domain quality requirements under operational constraints, while the purpose-built Finance Agent for Microsoft 365 Copilot is more reliable across dimensions. We release the dataset, rubrics, harness, and analysis code as open-source to support reproducible comparison and adaptation to other enterprise finance environments.
| Comments: | 22 pages, 10 figures |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.19409 [cs.AI] |
| (or arXiv:2607.19409v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19409 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wolfgang Pauli [view email]
[v1]
Sat, 11 Jul 2026 01:16:31 UTC (2,786 KB)
— Originally published at arxiv.org
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