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Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study

Author

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  • Ionuț Nica

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

  • Camelia Delcea

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

  • Nora Chiriță

    (Department of Economic Informatics and Cybernetics, Bucharest University of Economic Studies, 0105552 Bucharest, Romania)

Abstract

In this study, we explored the dynamic field of fuzzy logic and artificial intelligence (AI) in financial analysis from 1990 to 2023. Utilizing the bibliometrix package in RStudio and data from the Web of Science, we focused on identifying mathematical models and the evolving role of fuzzy information granulation in this domain. The research addresses the urgent need to understand the development and impact of fuzzy logic and AI within the broader scope of evolving technological and analytical methodologies, particularly concentrating on their application in financial and banking contexts. The bibliometric analysis involved an extensive review of the literature published during this period. We examined key metrics such as the annual growth rate, international collaboration, and average citations per document, which highlighted the field’s expansion and collaborative nature. The results revealed a significant annual growth rate of 19.54%, international collaboration of 21.16%, and an average citation per document of 25.52. Major journals such as IEEE Transactions on Fuzzy Systems , Fuzzy Sets and Systems , the Journal of Intelligent & Fuzzy Systems , and Information Sciences emerged as significant contributors, aligning with Bradford’s Law’s Zone 1. Notably, post-2020, IEEE Transactions on Fuzzy Systems showed a substantial increase in publications. A significant finding was the high citation rate of seminal research on fuzzy information granulation, emphasizing its mathematical importance and practical relevance in financial analysis. Keywords like “design”, “model”, “algorithm”, “optimization”, “stabilization”, and terms such as “fuzzy logic controller”, “adaptive fuzzy controller”, and “fuzzy logic approach” were prevalent. The Countries’ Collaboration World Map indicated a strong pattern of global interconnections, suggesting a robust framework of international collaboration. Our study highlights the escalating influence of fuzzy logic and AI in financial analysis, marked by a growth in research outputs and global collaborations. It underscores the crucial role of fuzzy information granulation as a mathematical model and sets the stage for further investigation into how fuzzy logic and AI-driven models are transforming financial and banking analysis practices worldwide.

Suggested Citation

  • Ionuț Nica & Camelia Delcea & Nora Chiriță, 2024. "Mathematical Patterns in Fuzzy Logic and Artificial Intelligence for Financial Analysis: A Bibliometric Study," Mathematics, MDPI, vol. 12(5), pages 1-35, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:782-:d:1352332
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    References listed on IDEAS

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    2. Hong Wang & Samyabrata Bhattacharjee & Nasreen Kausar & Ardashir Mohammadzadeh & Dragan Pamucar & Nasr Al Din Ide & Mukesh Soni, 2023. "Financial Performance Assessment by a Type-2 Fuzzy Logic Approach," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-8, May.
    3. Jaime Fabián Díaz Córdova & Edisson Coba Molina & Paúl Navarrete López, 2017. "Lógica difusa y el riesgo financiero. Una propuestade clasificación de riesgo financiero al sectorcooperativo," Contaduría y Administración, Accounting and Management, vol. 62(5), pages 31-32, Diciembre.
    4. Bakır, Mahmut & Özdemir, Emircan & Akan, Şahap & Atalık, Özlem, 2022. "A bibliometric analysis of airport service quality," Journal of Air Transport Management, Elsevier, vol. 104(C).
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