Identifying the recent trends, voids, and opportunities of sustainable finance via artificial intelligence, big data and analytics

Abstract

Sustainable finance refers to the integration of environmental, social, and governance (ESG) factors into financial decision-making processes. The objective of this research paper is to conduct a scientometric analysis of sustainable finance with the use of artificial intelligence (AI) and text analytics. Abstract and title text data was extracted from a sample of 317 research articles retrieved from ProQuest database until 22 August 2023. The study uses one of the important artificial intelligence techniques known as text analytics, which is a systematic approach to analyzing and extracting information from unstructured text data. The results of the study are divided into two parts: (1) title and abstract text data and (2) author/publication related information. Based on title and abstract data, word frequency analyses the most common words in these studies through wordcloud. Contrary to these, least common words were identified through term frequency-inverse document frequency (TF-IDF). Correlations between the words were estimated, displayed through correlation graphs, and additionally correlation was also shown between keywords with other words. For topic modeling, important themes were created through latent Dirichlet allocation (LDA) graphs. The second part of the results is related to author/publication information, such as influential authors through wordcloud of authors, collaborating authors through authors’ correlation graphs, the origin of countries, how many papers are published each year, the place of publications, and top journals related to sustainable finance. This study provides valuable insights into the current state of research; identifies critical trends, voids, and opportunities in sustainable finance research; and provides insight into the future of sustainable finance research.