Explainable AI in Corporate Finance: A Bibliometric Analysis of Research Trends and Future Directions

Explainable Artificial Intelligence, XAI, Bibliometric Analysis, Corporate Finance, Interpretable Machine Learning, Credit Risk, Financial Distress

Explainable artificial intelligence has been on a critical methodology innovation in corporate finance, but the intellectual structure of this emerging field is poorly understood. This paper attempts a bibliometric analysis of the field by exploring 83 articles found in the Scopus database between years 2015 and March 2026. The search terms were based on key constructs of XAI, and the main themes related to corporate finance. The analysis was performed with the help of VOSviewer (version 1.6.20) and Bibliometrix package in R. The findings suggest that Computational Economics, Journal of Risk and Financial Management, and Finance Research Letters are the journals that have published the most relevant research articles. China and Italy are the countries that have made the most contributions to the field. In terms of keywords clustering, SHAP values, XGBoost, credit risk, and financial distress prediction seem to be the most common ones in the field, but there is a growing interest in such topics as ESG, peer-to-peer lending, and new regulations related to AI. Conclusively, this paper provides a list of reasons why explainable artificial intelligence has become popular among financial researchers, potential areas of applications, and research directions. To my knowledge, this bibliometric analysis was not done before, and it can help explore this field further with the help of updated literature.