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Purpose This study examines the relationship between artificial intelligence (AI) and bias in banking and financial services. It identifies key themes and research patterns by analyzing publication trends, citations and author influence through bibliometric analysis, and by exploring theoretical foundations, methodologies and empirical designs through a systematic literature review. Design/methodology/approach This study adopts a two-phase mixed-method design combining bibliometric analysis with a systematic literature review to examine AI bias in banking and financial services. We analyze 65 peer-reviewed articles published between 2011 and 2025 in A or A* journals listed in the Australian Business Deans Council ranking. Findings Results from a bibliometric analysis reveal an increase in publication output in recent years, indicating the continued importance of this topic. Moreover, emerging topics such as cryptocurrency and blockchain reflect a shift toward ethical AI applications in financial decision-making. Results from the systematic literature review reveal four major themes, including AI as a strategic infrastructure for digital transformation, human–algorithm interaction and the duality of algorithmic opacity, fairness frameworks and responsible AI governance, and behavioral finance in the age of AI and machine learning (ML). Finally, the article introduces a comprehensive future research agenda outlining not only research questions but also proposed methodologies within each of the four themes. Originality/value This research is among the first to synthesize perspectives on cognitive and algorithmic bias within the context of AI adoption in banking and financial services. This study provides a comprehensive overview of the intellectual, theoretical and methodological development of the field by combining bibliometric and systematic literature review approaches. It gives a conceptual roadmap for future research on fairness-by-design, bias-aware governance and the ethical implementation of AI in financial ecosystems.
Krey et al. (Wed,) studied this question.