Mapping Two Decades of Artificial Intelligence and MachineLearning in Credit Scoring and Loan Restructuring:A Bibliometric and Network Analysis of Global Research (2000–2025)

The past twenty-five years have witnessed a paradigm shift in credit-risk evaluation, driven ‎by the integration of artificial intelligence (AI) and machine learning (ML) into financial ‎decision-making systems. To capture the evolution and intellectual structure of this fast-growing domain, this study conducts a comprehensive bibliometric and network analysis of ‎‎87 Scopus-indexed publications published between 2000 and 2025. Using VOSviewer ‎v1.6.20, R-Studio v4.3.1 (bibliometrix 4.1.2), and Python (NetworkX 3.3), the research ‎examines publication growth, geographical distribution, citation impact, co-citation linkages, ‎bibliographic coupling, and keyword co-occurrence patterns.‎ The results show a strong upward publication trend after 2016, reaching its peak in 2024, ‎coinciding with the surge of fintech and explainable AI adoption in banking and credit ‎analytics. The geographical analysis highlights India, China, and the United States as ‎dominant contributors, collectively producing over 60 % of total publications. Citation ‎analysis reveals that a small group of foundational works—particularly Datta (2016) on ‎algorithmic transparency and Ma et al. (2018) on predictive loan modelling—anchor the ‎field’s influence. Author co-citation networks identify three major clusters focused on ‎statistical foundations, credit scoring, and financial distress prediction, while bibliographic ‎coupling uncovers tight cross-linkages among predictive analytics, fintech integration, and ‎explainable ML.‎ Keyword mapping demonstrates a mature conceptual structure with three recurring themes: (i) ‎theoretical development in AI algorithms, (ii) applied ML models in fintech risk management, ‎and (iii) hybrid modelling approaches for credit scoring and interpretability. Together, these ‎findings confirm that the field has evolved from exploratory experimentation to a stage of ‎methodological consolidation and regulatory awareness. The study concludes that future ‎research should emphasize transparency, bias control, and model governance, integrating ‎performance, fairness, and stability as joint evaluation criteria for next-generation credit-risk ‎systems‎.