  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}.
