FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use

Financial AI systems need specialized evaluation beyond general benchmarks: The paper demonstrates that existing general-purpose AI tool benchmarks lack the domain-specific rigor required for finance, where high stakes, strict compliance requirements, and rapid data changes demand tailored assessment approaches.

Real-world financial tool use involves complex dimensions beyond "did it work or not": FinToolBench evaluates agents across finance-critical factors including timeliness (speed of execution), intent type (what the user actually needs), and regulatory domain alignment—showing that a technically successful action could still fail if it's too slow, misunderstands the user's goal, or violates compliance rules.

Current LLM agents struggle with the complexity of real financial tasks: The benchmark includes 760 executable financial tools and 295 real-world queries, revealing that agents face significant challenges when operating in realistic financial environments rather than simplified test scenarios—a critical finding for organizations considering deploying these systems.

Compliance and auditability must be built into financial AI from the ground up: The paper emphasizes that trustworthy AI in finance requires not just accurate results, but systems designed with regulatory alignment and auditability in mind, introducing FATR as a baseline approach that prioritizes stability and compliance alongside performance.

Standardized testing infrastructure is essential for responsible financial AI deployment: By establishing the first runnable benchmark with real financial APIs and rigorous evaluation criteria, FinToolBench provides a foundation for organizations to systematically test and compare financial AI agents before real-world use.