Algorithmic Decision-Making and Human Autonomy in Finance: A Systematic Review of AI Ethics Research

Abstract Artificial intelligence is rapidly transforming financial decision-making across banking, lending, insurance, auditing, fraud detection, and customer-facing financial services. At the same time, its growing use has intensified ethical concerns related to fairness, accountability, transparency, privacy, trust, and human oversight. Although research on artificial intelligence (AI) in finance has expanded considerably, the literature remains fragmented across disciplinary and application-specific streams, limiting a consolidated understanding of its intellectual foundations and thematic development. This study provides a bibliometric and thematic review of research at the intersection of artificial intelligence, finance, and ethics. Drawing on Scopus-indexed journal articles and a PRISMA-guided screening process, a final sample of 338 articles published between 2000 and 2025 was analyzed using the bibliometrix package in R. The findings show that the field is young but rapidly expanding, particularly after 2019, with strong momentum in recent years. Intellectual structure analysis identifies foundational contributions centered on algorithmic fairness, accountability, explainability, and governance, while historiographic patterns reveal major developmental pathways in credit scoring, financial services, accounting and auditing, and generative AI. Conceptual and thematic analyses further show that the literature is organized into six interconnected clusters covering AI ethics and governance, algorithmic fairness, explainable AI, fraud detection, trustworthy AI, and human-in-the-loop financial decision-making. The study contributes a structured map of this emerging field and shows that AI in finance is increasingly understood not merely as a technical innovation, but as a socio-technical governance challenge requiring responsible design, institutional accountability, and sustained human oversight.