Trust in Human–AI Collaboration in Finance: A Bibliometric-Systematic Literature Review
The integration of artificial intelligence (AI) in finance, encompassing algorithmic trading, robo-advisory, and credit scoring, underscores the importance of trust in human-AI collaboration. Yet, despite its importance, trust remains conceptually fragmented and inconsistently operationalized across disciplines. To address this gap, this study presents what is, to the best of our knowledge, the first Bibliometric-Systematic Literature Review focused on trust in human-
AI collaboration in finance. Following a PRISMA-guided process, we screened 430 Scopus records and identified 114 finance-specific publications (2018–2025). Using bibliographic coupling and thematic clustering, we mapped the conceptual landscape and identified six coherent research clusters: (i) AI governance in finance, (ii) eXplainable AI for finance, (iii) anthropomorphism in financial agents, (iv) user interface design for human-AI in finance, (v) robo-advisors for financial decision making, and (vi) blockchain and supply chain. Across these clusters, trust emerges as an orthogonal yet inconsistently defined construct, framed as cognitive, affective, procedural, or infrastructural, with limited integration among dimensions. Methodologically, the field is dominated by surveys, while experimental, longitudinal, and mixed-methods approaches remain scarce. The review also identifies emerging topics and key research gaps.
Building on these findings, we propose a novel, multi-level socio-technical framework that links micro-level user perceptions and behaviors, meso-level organizational and design practices, and macro-level regulatory and infrastructural
conditions. We illustrate the framework’s practical value through four financial use cases.
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Publication: Article
1779264543271
May 20, 2026
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The integration of artificial intelligence (AI) in finance, encompassing algorithmic trading, robo-advisory, and credit scoring, underscores the importance of trust in human-AI collaboration. Yet, despite its importance, trust remains conceptually fragmented and inconsistently operationalized across disciplines. To address this gap, this study presents what is, to the best of our knowledge, the first Bibliometric-Systematic Literature Review focused on trust in human-
AI collaboration in finance. Following a PRISMA-guided process, we screened 430 Scopus records and identified 114 finance-specific publications (2018–2025). Using bibliographic coupling and thematic clustering, we mapped the conceptual landscape and identified six coherent research clusters: (i) AI governance in finance, (ii) eXplainable AI for finance, (iii) anthropomorphism in financial agents, (iv) user interface design for human-AI in finance, (v) robo-advisors for financial decision making, and (vi) blockchain and supply chain. Across these clusters, trust emerges as an orthogonal yet inconsistently defined construct, framed as cognitive, affective, procedural, or infrastructural, with limited integration among dimensions. Methodologically, the field is dominated by surveys, while experimental, longitudinal, and mixed-methods approaches remain scarce. The review also identifies emerging topics and key research gaps.
Building on these findings, we propose a novel, multi-level socio-technical framework that links micro-level user perceptions and behaviors, meso-level organizational and design practices, and macro-level regulatory and infrastructural
conditions. We illustrate the framework’s practical value through four financial use cases. - Mario Mirabile, Giovanni Emanuele Corazza, Jose Maria Alonso-Moral - 10.1007/s00146-026-03049-y
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