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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**: _to be added_
---
## 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:
1. **Inducing workflow types and instances** from real collaborative context in **enterprise email threads** (Enron Corpus: 500,000 emails from 150 executives and employees).
2. **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.
3. **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**:
```json
{
"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:** finworkbench@gmail.com
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