Mapping knowledge evolution in corporate financial distress prediction: a bibliometric and science mapping perspective

Purpose This study aims to examine the knowledge structure, thematic evolution and methodological progression of corporate financial distress prediction (FDP) research through a comprehensive bibliometric and science mapping approach. Design/methodology/approach The study analyses 1,213 Scopus-indexed publications spanning 1981 to February 2026. It uses a combination of performance analysis and science mapping techniques, including co-citation analysis, bibliographic coupling and author keyword co-occurrence networks, to systematically map the intellectual, social and conceptual structure of FDP research. VOSviewer software is used to perform network visualisation and clustering analyses. Findings The findings reveal a progressive evolution of FDP research from traditional accounting-based and statistical models to advanced data-driven approaches using machine learning, artificial intelligence and textual analytics. Thematic clustering indicates an increase in interdisciplinarity, integrating financial, computational, governance and macroeconomic perspectives. Methodological advancement occurs through integration rather than replacement, with classical financial theories continuing to underpin contemporary predictive models. Research limitations/implications The study is limited to corporate FDP literature indexed in the Scopus database. Future studies could enhance depth and coverage by incorporating broader financial distress contexts, interdisciplinary perspectives and additional databases, as well as explainable and context-sensitive FDP models to improve practical applicability. Originality/value This study offers a theory-linked, knowledge-discovery-oriented synthesis of FDP research, moving beyond descriptive bibliometric reviews. It maps the field’s intellectual and thematic structure and demonstrates how interdisciplinary integration shapes the evolution of corporate FDP research.