Trends and Evolution of Data-Driven Financial Management: A Bibliometric Analysis of Scientific Publications and Their Influence on Financial Decision Making

Abstract

The field of data-driven financial management has witnessed a dynamic evolution, marked by interdisciplinary collaborations, technological advancements, and a surge in research output. This bibliometric analysis explores the landscape of scholarly publications, clustering works into thematic groups, identifying influential authors, and examining prevalent terms. Clusters reveal diverse focuses, including business models, artificial intelligence, optimization, and predictive modeling. Influential works by McAfee et al., Wu et al., and Gómez-Bombarelli et al. underscore the intersection of big data, analytics, and innovative applications in chemistry. The distribution of publication years indicates a recent upswing in research activity, aligning with the rapid advancements in data science. Term occurrences highlight the central role of "data" and the methodological diversity captured by "approach" and "model." Synthesizing findings underscores the multidimensional nature of data-driven financial management, urging future research to embrace interdisciplinary collaboration, address ethical considerations, and foster explainable AI in finance. The abstract provides a concise overview of the bibliometric analysis, offering insights into the current state and future directions of data-driven financial management research.