pretty_name: FinancialAuditBench
license: mit
language:
- en
tags:
- benchmark
- financial-audit
- agents
- evaluation
configs:
- config_name: completion
default: true
data_files:
- split: test
path: data/completion.parquet
- config_name: review
data_files:
- split: test
path: data/review.parquet
FinancialAuditBench
FinancialAuditBench evaluates AI agents on financial statement audit tasks. It contains 90 tasks across 6 synthetic audit engagements in manufacturing and staffing services. Agents use supporting documents to complete or review spreadsheet workpapers.
Task settings
| Configuration | Tasks | Starting workpaper | Goal |
|---|---|---|---|
completion |
45 | Blank template | Perform the specified procedures and document findings. |
review |
45 | Completed model workpaper | Review the workpaper and correct errors. |
Each configuration has a test split with one row per task. Review tasks correspond to
completion tasks and use the same supporting binder. Select either configuration in the
viewer’s subset dropdown.
Getting started
Install datasets and huggingface_hub. While the dataset is private, sign in with
hf auth login using an account with access.
from datasets import load_dataset
from huggingface_hub import snapshot_download
# Download the task tables, workbooks, and supporting documents.
data_dir = snapshot_download(
"modusaudit/FinancialAuditBench",
repo_type="dataset",
revision="tasks-2026-09-24",
local_dir="benchmark_tasks",
allow_patterns=["README.md", "data/**", "tasks/**", "review_tasks/**", "inputs/**"],
)
# Choose "completion" or "review".
tasks = load_dataset(data_dir, "completion", split="test")
print(tasks[0]["instruction"])
Dataset structure
tasks/: completion instructions, blank workbooks, task configurations, and rubrics.review_tasks/: review instructions, completed starting workbooks, task configurations, rubrics, and blank grading templates.inputs/: supporting documents shared by tasks from the same engagement.data/: completion and review task tables in Parquet format.
Each row includes task_id, base_task_id, binder_id, industry, workpaper, and
instruction, together with links and relative paths to its starting workbook, binder,
and rubric. base_task_id connects each review task to its completion task.
See the benchmark code for setup, execution, and grading instructions.
Citation
To reference this dataset release:
@misc{huang2026financialauditbenchbenchmarkconstructiondifferential,
title={FinancialAuditBench: Benchmark Construction under Differential Privacy Using Real-World Priors},
author={Jerry Huang and Sarvesh Babu and Matt Van Buren and Alexander Wang and Pranav Pillai and Arush Jain and James P. Burton and Julia Hockenmaier},
year={2026},
eprint={2609.32835},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2609.32835},
}