FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912955235303424 |
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| author | Lu, Jiaxuan Wang, Kong Wang, Yemin Tang, Qingmei Zeng, Hongwei Chen, Xiang Pi, Jiahao Deng, Shujian Chen, Lingzhi Fu, Yi Yang, Kehua Sun, Xiao |
| author_facet | Lu, Jiaxuan Wang, Kong Wang, Yemin Tang, Qingmei Zeng, Hongwei Chen, Xiang Pi, Jiahao Deng, Shujian Chen, Lingzhi Fu, Yi Yang, Kehua Sun, Xiao |
| contents | The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While general-purpose tool learning has witnessed a surge in benchmarks, the financial sector, characterized by high stakes, strict compliance, and rapid data volatility, remains critically underserved. Existing financial evaluations predominantly focus on static textual analysis or document-based QA, ignoring the complex reality of tool execution. Conversely, general tool benchmarks lack the domain-specific rigor required for finance, often relying on toy environments or a negligible number of financial APIs. To bridge this gap, we introduce FinToolBench, the first real-world, runnable benchmark dedicated to evaluating financial tool learning agents. Unlike prior works limited to a handful of mock tools, FinToolBench establishes a realistic ecosystem coupling 760 executable financial tools with 295 rigorous, tool-required queries. We propose a novel evaluation framework that goes beyond binary execution success, assessing agents on finance-critical dimensions: timeliness, intent type, and regulatory domain alignment. Furthermore, we present FATR, a finance-aware tool retrieval and reasoning baseline that enhances stability and compliance. By providing the first testbed for auditable, agentic financial execution, FinToolBench sets a new standard for trustworthy AI in finance. The tool manifest, execution environment, and evaluation code will be open-sourced to facilitate future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08262 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use Lu, Jiaxuan Wang, Kong Wang, Yemin Tang, Qingmei Zeng, Hongwei Chen, Xiang Pi, Jiahao Deng, Shujian Chen, Lingzhi Fu, Yi Yang, Kehua Sun, Xiao Artificial Intelligence The integration of Large Language Models (LLMs) into the financial domain is driving a paradigm shift from passive information retrieval to dynamic, agentic interaction. While general-purpose tool learning has witnessed a surge in benchmarks, the financial sector, characterized by high stakes, strict compliance, and rapid data volatility, remains critically underserved. Existing financial evaluations predominantly focus on static textual analysis or document-based QA, ignoring the complex reality of tool execution. Conversely, general tool benchmarks lack the domain-specific rigor required for finance, often relying on toy environments or a negligible number of financial APIs. To bridge this gap, we introduce FinToolBench, the first real-world, runnable benchmark dedicated to evaluating financial tool learning agents. Unlike prior works limited to a handful of mock tools, FinToolBench establishes a realistic ecosystem coupling 760 executable financial tools with 295 rigorous, tool-required queries. We propose a novel evaluation framework that goes beyond binary execution success, assessing agents on finance-critical dimensions: timeliness, intent type, and regulatory domain alignment. Furthermore, we present FATR, a finance-aware tool retrieval and reasoning baseline that enhances stability and compliance. By providing the first testbed for auditable, agentic financial execution, FinToolBench sets a new standard for trustworthy AI in finance. The tool manifest, execution environment, and evaluation code will be open-sourced to facilitate future research. |
| title | FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.08262 |