数据要素对商业银行信贷效率的影响——基于文本挖掘的实证研究

The Influence of Data Factors on the Credit Efficiency of Commercial Banks—An Empirical Study Based on Text Mining

摘要: 数字经济时代，数据成为与土地、劳动力、资本、技术并列的生产要素，正深刻改变着各行业的生产方式和社会治理方式。商业银行是数据密集型行业，数据要素作为推动自身转型发展的关键抓手受到了极大的重视。本文通过文本挖掘的方法，选取2013~2021年全国34家上市商业银行年报中的原始词频数据，构建了数据要素指数并提出了研究假设，然后对商业银行要素指数与信贷效率之间的关系进行了实证检验。在此基础上，以中国工商银行为案例，分析了数据要素运用程度对信贷效率的影响。

Abstract: In the context of the digital economy, data has emerged as a key factor of production alongside traditional inputs such as land, labor, capital, and technology. Its influence is profoundly changing the mode of production and social governance of various industries. Commercial banks, in particular, are data-intensive industries, underscoring the importance of data elements as a key driver for their transformation and growth. This paper constructs a data factor index and puts forward research hypotheses by selecting raw word frequency data in the annual reports of 34 listed commercial banks in China from 2013 to 2021 through text mining. The empirical testing of the relationship between the factor index and credit efficiency of commercial banks is then conducted. On this basis, the impact of the degree of data factor utilization on credit efficiency is analyzed with Industrial and Commercial Bank of China as a case study.