Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

TL;DR

Finch is a comprehensive benchmark derived from authentic enterprise finance and accounting workflows, designed to evaluate AI agents' ability to handle complex, multimodal, real-world tasks.

Contribution

The paper introduces a new benchmark dataset from real enterprise environments, combining workflow construction, expert annotation, and evaluation of advanced AI systems.

Findings

GPT-5.1 passes 38.4% of workflows in human evaluation.

The benchmark includes 172 workflows with 384 tasks and 27 million spreadsheet cells.

Real-world enterprise workflows pose significant challenges for current AI agents.

Abstract

We introduce FinWorkBench (a.k.a. Finch) for evaluating AI agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is sourced from authentic enterprise workspaces from Enron (15,000 files and 500,000 emails) and other financial institutions, covering the period 2000--2025 and preserving the in-the-wild messiness of multimodal artifacts such as tables and charts across diverse domains including budgeting, trading, asset management, and operational management. We propose a workflow construction process that combines LLM-assisted mining of workflows from authentic enterprise environments with expert annotation: (1) LLM-assisted, expert-verified derivation of workflows from real-world email threads and spreadsheet version histories, and (2) meticulous annotation requiring over 700 hours of expert effort. This yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of real-world enterprise work. We conduct both human and automated evaluations of frontier AI systems, including GPT-5.1, Claude Sonnet 4.5, Claude Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. Under human evaluation, GPT-5.1 Pro spends an average of 16.8 minutes per workflow yet passes only 38.4% of workflows. Comprehensive case studies further surface the challenges that real-world enterprise workflows pose for AI agents.