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AI and Financial Fraud Prevention: Mapping the Trends and Challenges Through a Bibliometric Lens

Author

Listed:
  • Luiz Moura

    (Faculdade de Administração e Ciências Contábeis (FACC), Universidade Federal do Rio de Janeiro (UFRJ), Av. Pasteur, 250–sala 242, Praia Vermelha, Urca, Rio de Janeiro 22290-240, Brazil)

  • Andre Barcaui

    (Faculdade de Administração e Ciências Contábeis (FACC), Universidade Federal do Rio de Janeiro (UFRJ), Av. Pasteur, 250–sala 242, Praia Vermelha, Urca, Rio de Janeiro 22290-240, Brazil)

  • Renan Payer

    (Departamento de Engenharia de Produção (TEP), Universidade Federal Fluminense (UFF), Rua Passo da Pátria, 156, Bloco D–sala 306, Campus da Praia Vermelha, Niterói 24210-240, Brazil)

Abstract

This study systematically reviews academic research on artificial intelligence (AI) in financial fraud prevention. Employing a bibliometric approach, we analyzed 137 peer-reviewed articles published between 2015 and 2025, sourced from Scopus, Web of Science, and ScienceDirect. Using Bibliometrix, we mapped the field’s intellectual structure, collaboration patterns, and thematic clusters. Research interest has surged since 2019, led mainly by China and India, though the literature is mostly technical, with limited social science engagement. Three main themes emerged: AI-based fraud detection models, blockchain and fintech integration, and big data analytics. Despite growing output, international collaboration and focus on ethical, regulatory, and organizational issues remain limited. These insights provide a foundation for advancing both research and practical AI-driven fraud mitigation.

Suggested Citation

  • Luiz Moura & Andre Barcaui & Renan Payer, 2025. "AI and Financial Fraud Prevention: Mapping the Trends and Challenges Through a Bibliometric Lens," JRFM, MDPI, vol. 18(6), pages 1-24, June.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:6:p:323-:d:1677116
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    References listed on IDEAS

    as
    1. Ahmed, Shamima & Alshater, Muneer M. & Ammari, Anis El & Hammami, Helmi, 2022. "Artificial intelligence and machine learning in finance: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 61(C).
    2. Yuanbao Wang & Guangliang Zhu, 2024. "Construction of Accounting Fraud and Its Audit Countermeasure Model Based on Computer Technology," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 23(04), pages 1-17, August.
    3. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    4. Philippe Mongeon & Adèle Paul-Hus, 2016. "The journal coverage of Web of Science and Scopus: a comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 213-228, January.
    5. Aghaei Chadegani, Arezoo & Salehi, Hadi & Md Yunus, Melor & Farhadi, Hadi & Fooladi, Masood & Farhadi, Maryam & Ale Ebrahim, Nader, 2013. "A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases," MPRA Paper 46898, University Library of Munich, Germany, revised 18 Mar 2013.
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