Artificial intelligence and machine learning in finance: A bibliometric review

Shamima Ahmed , Muneer Alshater (1) , Anis El Ammari (2) , Helmi Hammami (3)

1 Emirates College of Technology
2 UM - Université de Monastir - University of Monastir
3 ESC [Rennes] - ESC Rennes School of Business

This study reviewed the artificial intelligence (AI) and machine learning (ML) literature in the finance field. Using a bibliometric approach, we collected 348 articles published in 2011–2021 from journals indexed in the Scopus database. Multiple software (RStudio, VOSviewer, and Excel) were employed to analyze the data and depict the most active scientific actors in terms of countries, institutions, sources, documents, and authors. Our review revealed an upward trajectory in the publication trend starting from 2015 and found the application of AI and ML in bankruptcy prediction, stock price prediction, portfolio management, oil price prediction, anti-money laundering, behavioral finance, big data analytics, and blockchain. Moreover, the United States, China, and the United Kingdom were the top three contributors to the literature. Our results provide practical guidance to market participants, especially, fintech and finance companies, on how AI and ML can be used in their decision-making.

Mots clés: Finance; Artificial intelligence; Machine learning; Bibliometric

Domaines: Finance [q-fin.GN]

Shamima Ahmed, Muneer Alshater, Anis El Ammari, Helmi Hammami. Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 2022, 61, pp.101646. ⟨10.1016/j.ribaf.2022.101646⟩. ⟨hal-03697290⟩