Leveraging artificial intelligence for enhanced decision-making in finance: trends and future directions
[EN]This paper aims to analyze the evolution and impact of artificial intelligence (AI) in the financial industry by examining the growth of scientific publications from 1991 to 2023. Design/methodology/approach– The study employs a bibliometric analysis to quantify and visualize the evolution of AI...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universidad de Salamanca (USAL) |
| Repositorio: | GREDOS. Repositorio Institucional de la Universidad de Salamanca |
| OAI Identifier: | oai:gredos.usal.es:10366/169161 |
| Acceso en línea: | http://hdl.handle.net/10366/169161 |
| Access Level: | acceso abierto |
| Palabra clave: | Artificial intelligence Finance Machine learning Predictive modeling Financial innovation Risk management |
| Sumario: | [EN]This paper aims to analyze the evolution and impact of artificial intelligence (AI) in the financial industry by examining the growth of scientific publications from 1991 to 2023. Design/methodology/approach– The study employs a bibliometric analysis to quantify and visualize the evolution of AI research in finance. We analyze publication trends, citation patterns, and collaboration networks. Thematic keyword analysis is conducted to track the emergence of dominant research themes over time, identifying key areas where AI is influencing financial services. Findings– The study confirms an exponential growth in research output on AI applications in finance, particularly in recent years. This growth is driven by increasing interest in machine learning algorithms, big data analytics and automation in financial decision-making. Leading academic journals and institutions have played a crucial role in shaping discourse around AI-driven financial transformation. Thematic networks reveal a dual influence: AI is not only enhancing technical aspects such as fraud detection, algorithmic trading and credit scoring but is also contributing to broader strategic shifts in financial regulation, customer experience and ethical considerations. However, the rapid expansion of research has led to fragmentation, with diverse subfields developing independently. This fragmentation, along with ethical and regulatory challenges, underscores the need for interdisciplinary collaboration and policy frameworks to guide responsible AI adoption. Originality/value– The findings contribute to academic and industry debates by offering a structured understanding of AI’s impact on finance and proposing pathways for future interdisciplinary research and ethical governance. |
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