Artificial Intelligence in Banking Systems: A Bibliometric Mapping of Applications, Gaps, and Strategic Research Pathways (1996–2024)

Abstract

This study conducts a descriptive bibliometric analysis of artificial intelligence (AI) applications in banking systems, synthesizing 622 peer-reviewed journal articles published between 1996 and 2024. Drawing from the Web of Science Core Collection, the dataset was screened to include only ABS-listed or Scopus-indexed journals. The analysis applies keyword co-occurrence, citation profiling, Latent Dirichlet Allocation (LDA), and SCOR-style process classification to identify thematic clusters and research gaps. Findings show that scholarly output has increased significantly since 2019, with machine learning and predictive models dominating the methodological landscape. Most studies focus on credit scoring and fraud detection, while compliance, investment advisory, and prescriptive analytics remain marginally addressed. Five thematic research clusters were identified: model evaluation, fintech integration, credit classification, organizational transformation, and decision support. Journals such as the International Journal of Bank Marketing and Annals of Operations Research were among the most prolific sources. Despite progress, the literature remains imbalanced favouring technical outputs over behavioural, ethical, or institutional dimensions. This paper offers a structured research agenda emphasizing decision-oriented AI models, compliance analytics, human-AI collaboration, and strategic integration. The results inform scholars and banking professionals seeking to align AI innovations with financial governance, digital transformation, and sustainable operational design.