FinQA: A Dataset of Numerical Reasoning over Financial Data

TL;DR

FinQA introduces a large-scale dataset for financial question answering that emphasizes complex numerical reasoning and explainability, highlighting current model limitations in understanding financial data.

Contribution

The paper presents the first dataset of its kind, FinQA, with expert-annotated reasoning programs for financial QA, enabling new research in complex numerical reasoning in finance.

Findings

Pre-trained models underperform compared to experts in financial reasoning.

The dataset facilitates research into multi-step numerical reasoning.

Models struggle with the complexity of financial data and reasoning.

Abstract

The sheer volume of financial statements makes it difficult for humans to access and analyze a business's financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this work, we focus on answering deep questions over financial data, aiming to automate the analysis of a large corpus of financial documents. In contrast to existing tasks on general domain, the finance domain includes complex numerical reasoning and understanding of heterogeneous representations. To facilitate analytical progress, we propose a new large-scale dataset, FinQA, with Question-Answering pairs over Financial reports, written by financial experts. We also annotate the gold reasoning programs to ensure full explainability. We further introduce baselines and conduct comprehensive experiments in our dataset. The results demonstrate that popular, large, pre-trained models fall far short of expert humans in acquiring finance knowledge and in complex multi-step numerical reasoning on that knowledge. Our dataset -- the first of its kind -- should therefore enable significant, new community research into complex application domains. The dataset and code are publicly available\url{https://github.com/czyssrs/FinQA}.