license: cc-by-3.0
tags:
- agent
- workflow
- multimodal
- spreadsheet
- pdf
- image
- code
- finance
- accouning
modalities:
- text
- spreadsheet
- pdf
- image
- code
configs:
- config_name: Finch_Dataset_All
data_files:
- split: test
path:
- finch_workflows_test.jsonl
Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows
This repository contains the dataset for Finch, an enterprise-grade benchmark for evaluating an agent’s ability to work like a skilled finance & accounting expert (work IQ) on real-world professionel workflows.
- Paper: to be added
- Project Page: https://huggingface.co/datasets/FinWorkBench/Finch
- Code: https://github.com/FinWorkBench
Dataset Description
Finch focuses on messy and long-horizon finance & accounting workflows that span:
data entry/import, structuring/formatting, web search, cross-sheet/file retrieval, calculation, financial modeling, validation, translation, visualization, and reporting.
The workflows are derived from real-world enterprise workspaces (primarily Enron, as well as corporations in the EUSES Corpus, investment and securities companies, World Bank, Canadian/British government agencies, and more), including:
- Enterprise email threads where collaborators naturally describe, discuss, and track workflows
- Large and messy spreadsheets with multimodal artifacts including text, tables, formulas, charts, pivots, images, etc
- Interlinked PDFs and documents that provide additional business context
We adopt a three-step workflow labeling process:
- Inducing workflow types and instances from real collaborative context in enterprise email threads (Enron Corpus: 500,000 emails from 150 executives and employees).
- Deriving concrete workflow instances by analyzing changes across spreadsheet versions (15,000 versioned spreadsheets from Enron and EUSES) and designing workflows based on high-quality artifacts from investment and securities companies, World Bank, Canadian/British government agencies, WideSearch, Dabstep, and more.
- Conductin meticulous expert annotation of task instructions, input files, and reference outputs, involving hundreds of hours of expert work.
This process yields 172 enterprise-grade workflows—primarily multi-task composite, involving 1,710 spreadsheets and 27 million cells, capturing the intrinsic compositional, messy, multimodal, and collaborative nature of real-world finance & accounting work. In this release, we provide full annotations for the first 72 workflows, with the remaining 100 to be released in a subsequent update.
Experiment results show that even frontier agents (GPT 5.1 Pro and Claude Sonnet 4.5 Pro) solve fewer than 40% of the workflows, revealing a substantial performance gap for real-world enterprise scenarios.
📁 Dataset Structure
The instruction-tuning corpus is released in JSONL format.
Each line corresponds to one workflow-centric example:
{
"id": "<workflow identifier>",
"instruction_en": "<English task instruction for a finance & accounting workflow>",
"source_files": ["<input file name>", "..."],
"source_files_urls": ["<input file download URL>", "..."],
"reference_outputs": {
"files": ["<reference output file name>"],
"text": "<textual reference output>"
},
"reference_file_urls": ["<reference output file download URL>"],
"task_type": "<task category (e.g., reporting, modeling)>",
"business_type": "<business domain (e.g., budgeting, trading)>"
}
📣 Feedback & Issues
If you find any issues with the dataset or have suggestions, please open a discussion in the Community tab — we value your feedback!
📧 Contact: [email protected]