FinanceBench
FinanceBench is an open-source benchmark designed to evaluate large language models (LLMs) on open-book financial question answering.
Publisher review
FinanceBench is an open-source benchmark designed to evaluate large language models (LLMs) on open-book financial question answering. Created by a multi-disciplinary team from Patronus AI, Contextual AI, and Stanford University, it targets enterprises and researchers who need to assess LLM performance in high-stakes financial tasks. The benchmark comprises 10,231 questions about publicly traded companies, each with corresponding answers and evidence strings, covering numerical reasoning, information retrieval, logical reasoning, and world knowledge. It serves as a minimum performance standard for financial QA, aiming to detect hallucinations and other unexpected LLM behaviors in a scalable way.
FinanceBench works by testing model configurations on a sample of 150 cases, with manual review of 2,400 answers. It evaluates 16 state-of-the-art model setups, including GPT-4-Turbo, Llama2, and Claude2, using vector stores and long context prompts. The questions are ecologically valid, covering diverse scenarios like calculating ROA for Tesla in 2025, which requires the model to reason that ROA = net income / total assets and invoke separate retrieval steps. The benchmark is available open-source, allowing others to replicate tests and track model capabilities over time.
As a first-of-its-kind benchmark for financial QA, FinanceBench occupies a unique position in the market. It directly addresses the gap in evaluating LLMs for finance-specific tasks, where few alternatives exist. While it does not compete with financial data terminals like Bloomberg Terminal or AlphaSense, it provides a critical evaluation layer for LLM-based financial tools. The benchmark's findings highlight that even top models fail significantly: GPT-4-Turbo with a retrieval system fails 81% of the time, and Llama2 with retrieval also fails 81% of the time. Long context improvements reduce failure rates to 21% for GPT-4-Turbo and 24% for Claude-2, but these are impractical for enterprise settings due to increased latency.
Trade-offs are clear: FinanceBench exposes severe weaknesses in LLMs for financial reasoning, but its sample set (150 cases) may not fully represent real-world complexity. The benchmark focuses on clear-cut questions, not ambiguous or multi-document synthesis tasks. Long context windows improve accuracy but are unrealistic for large financial documents due to latency. Additionally, the benchmark does not address pricing or integration costs for enterprise deployment, leaving users to infer operational overhead from the reported failure rates.
How it works
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Financial QA evaluation
Tests LLMs on 10,231 open-book questions about publicly traded companies with answers and evidence strings.
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Multi-model testing
Evaluates 16 configurations including GPT-4-Turbo, Llama2, and Claude2 with vector stores and long context prompts.
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Hallucination detection
Identifies incorrect or refused answers, with GPT-4-Turbo failing 81% of the time on retrieval-based setups.
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Open-source benchmark
Available for public use, enabling replication and tracking of model performance over time.
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Ecologically valid questions
Covers numerical reasoning, information retrieval, logical reasoning, and world knowledge from real financial scenarios.
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Manual review process
Manually reviews 2,400 answers from a sample of 150 cases to ensure accuracy and detect unexpected behaviors.
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Minimum performance standard
Designed as a clear-cut baseline to assess whether LLMs are suitable for high-stakes financial use cases.
Strengths and trade-offs
Strengths
- First-of-its-kind benchmark for financial question answering, filling a critical gap in LLM evaluation for the finance domain.
- Detects hallucinations and other unexpected LLM behavior in a scalable way, with manual review of 2,400 answers.
- Enables evaluation of 16 state-of-the-art model configurations, including GPT-4-Turbo, Llama2, and Claude2.
- Helps identify where LLMs fail in real-world financial use cases, such as numerical reasoning and handling large documents.
Trade-offs
- LLMs fail spectacularly on a sample set of questions from FinanceBench, with GPT-4-Turbo and Llama2 failing 81% of the time with retrieval systems.
- Long context windows are impractical for enterprise settings due to increased latency and cannot support larger financial documents.
- Models struggle with numerical reasoning and handling large financial documents, limiting their suitability for enterprise use.
- The benchmark's sample of 150 cases may not fully represent the complexity and ambiguity of real-world financial questions.
Pricing context
Not specified in sources; the benchmark is open-source and freely available.
Getting started with FinanceBench
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Clone the repository
Clone the FinanceBench GitHub repository to your local machine. This gives you access to the benchmark code, question set, and evaluation scripts needed to run tests.
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Install dependencies
Install required Python packages listed in the repository's requirements file. Use a virtual environment to isolate dependencies and ensure compatibility with your LLM evaluation pipeline.
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Configure your LLM setup
Set up the model configuration you want to test, such as GPT-4-Turbo or Llama2. Choose between a retrieval system (vector store) or long context prompt, and provide API keys or model paths as needed.
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Run the sample evaluation
Execute the evaluation script on the provided sample of 150 cases. This generates answers from your model configuration and saves them for comparison against the benchmark's ground truth answers and evidence strings.
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Review and analyze results
Manually review the model's answers against the evidence strings for the 2,400 manually reviewed questions. Identify failure patterns like hallucinations or incorrect numerical reasoning to assess your model's financial QA performance.
Frequently Asked Questions
What is FinanceBench?
FinanceBench is an open-source benchmark for evaluating large language models on financial question answering. It was created by researchers from Patronus AI, Contextual AI, and Stanford University to test LLMs on 10,231 questions about publicly traded companies.
How does FinanceBench evaluate LLMs?
FinanceBench tests LLMs using a sample of 150 cases with manual review of 2,400 answers. It evaluates 16 model configurations, including GPT-4-Turbo, Llama2, and Claude2, using vector stores and long context prompts to assess numerical reasoning and retrieval.
What are the main features of FinanceBench?
FinanceBench features financial QA evaluation on 10,231 questions, multi-model testing across 16 setups, hallucination detection, and ecologically valid questions covering numerical reasoning, information retrieval, logical reasoning, and world knowledge from real financial scenarios.
How well do LLMs perform on FinanceBench?
Top models fail significantly on FinanceBench. GPT-4-Turbo with a retrieval system fails 81% of the time, and Llama2 with retrieval also fails 81%. Long context improvements reduce failure rates to 21% for GPT-4-Turbo and 24% for Claude-2.
Is FinanceBench suitable for enterprise use?
FinanceBench exposes severe LLM weaknesses, but its sample of 150 cases may not reflect real-world complexity. Long context windows improve accuracy but are impractical for enterprises due to increased latency, limiting suitability for large financial documents.
How does FinanceBench compare to other financial tools?
FinanceBench is a first-of-its-kind benchmark for financial QA, filling a gap in LLM evaluation. It does not compete with data terminals like Bloomberg Terminal or AlphaSense but provides a critical evaluation layer for LLM-based financial tools.
Alternatives
How FinanceBench compares
Direct head-to-head against 2 competitors. Picked by 7wData.
FinanceBench
- Pricing
- Not specified in sources; the benchmark is open-source and freely available.
- Target
- FinanceBench is an open-source benchmark designed to evaluate large language models (LLMs) on open-book financial question answering.
- Strength
- First-of-its-kind benchmark for financial question answering, filling a critical gap in LLM evaluation for the finance domain.
- Watch for
- LLMs fail spectacularly on a sample set of questions from FinanceBench, with GPT-4-Turbo and Llama2 failing 81% of the time with retrieval systems.
Monarch Money
- Pricing
- $14.99/month or $99/year
- Target
- Personal finance planners
- Deployment
- Cloud SaaS
- Strength
- Customizable dashboards for goal tracking
- Watch for
- Limited investment tracking features
YNAB
- Pricing
- $14.99/month or $99/year
- Target
- Zero-based budgeting adherents
- Deployment
- Mobile/desktop apps
- Strength
- Real-time sync across devices
- Watch for
- Steep learning curve for new users
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Sources
Reporting on this tool draws on these publicly available sources.