  Large Language Models (LLMs) have demonstrated impressive capabilities across
a wide range of tasks. However, their proficiency and reliability in the
specialized domain of financial data analysis, particularly focusing on
data-driven thinking, remain uncertain. To bridge this gap, we introduce
\texttt{FinDABench}, a comprehensive benchmark designed to evaluate the
financial data analysis capabilities of LLMs within this context.
\texttt{FinDABench} assesses LLMs across three dimensions: 1)
\textbf{Foundational Ability}, evaluating the models' ability to perform
financial numerical calculation and corporate sentiment risk assessment; 2)
\textbf{Reasoning Ability}, determining the models' ability to quickly
comprehend textual information and analyze abnormal financial reports; and 3)
\textbf{Technical Skill}, examining the models' use of technical knowledge to
address real-world data analysis challenges involving analysis generation and
charts visualization from multiple perspectives. We will release
\texttt{FinDABench}, and the evaluation scripts at
\url{https://github.com/cubenlp/BIBench}. \texttt{FinDABench} aims to provide a
measure for in-depth analysis of LLM abilities and foster the advancement of
LLMs in the field of financial data analysis.
