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All Journal Majalah Ilmiah Warta Dharmawangsa Ilomata International Journal of Management Ilomata International Journal of Management Dynamic Management Journal Jurnal Ekonomi dan Bisnis GROWTH (JEBG) Jurnal Akuntansi, Manajemen Dan Ekonomi Islam (JAM-EKIS) Jurnal Comparative : Ekonomi Dan Bisnis Jurnal Mahasiswa Entrepreneur Jurnal Bisnis dan Manajemen West Science Journal of Economics, Assets, and Evaluation Manajemen : Jurnal Ekonomi Ektasi : Jurnal Ekonomi, Akutansi dan Organisasi Jurnal Pengabdian Masyarakat Manage JURNAL MANAJEMEN DAN BISNIS INDONESIA
Wibowo, Yohanes Gunawan
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Humanities Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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Implementation of Artificial Intelligence in Fraud Detection and Prevention Through a Systematic Literature Review and Its Implications for the Financial Sector Satoto, Eko Budi; Wibowo, Yohanes Gunawan
Ilomata International Journal of Management Vol. 7 No. 1 (2026): January 2026
Publisher : Yayasan Sinergi Kawula Muda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61194/ijjm.v7i1.1919

Abstract

The increasing complexity of financial fraud in the Digital Era requires more advanced and adaptive detection methods. This study examines the implementation of Artificial Intelligence (AI) in fraud detection and prevention through a Systematic Literature Review (SLR), addressing a critical issue in financial technology that remains highly relevant to both academic and professional communities. Although AI-based fraud detection has been widely studied, this research provides a distinct contribution by integrating technical effectiveness with regulatory alignment. The SLR systematically analyzes studies from major academic databases such as Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar to identify key trends, challenges, and implications for the financial sector. The PRISMA framework is used to screen and evaluate relevant literature, ensuring a comprehensive and structured analysis. VOSviewer is applied to visualize key research trends and topic relationships in AI-based fraud detection. The findings indicate that machine learning and deep learning techniques significantly enhance fraud detection accuracy, surpassing traditional rule-based approaches. Natural Language Processing (NLP) has shown effectiveness in analyzing fraud-related documents, while big data analytics facilitates real-time fraud monitoring. However, challenges persist, including data imbalance, regulatory compliance, and data privacy concerns, which must be addressed for successful AI implementation. This study concludes that an integrated AI framework that combines technological advancements with strong regulatory alignment is crucial for effective fraud detection. Future research should explore empirical case studies and real-world applications to validate these theoretical findings.
Co-Authors Achmad Hasan Hafidzi AHMAD FAISOL Ahmad Izzuddin Alarni, Khalifah Hikmah Anggraheni, Dyah Ajeng Anugrah, Sella Nanda Azhad, Muhamad Naely Bayu Wijayantini, Bayu Damayanti, Jessica Putri Dewi, Bunga Roes Mala Dewi, Sofia Rhosma Diansari Reskiputri, Tatit Dwi Budi Santoso Eko Budi Satoto Feti Fatimah Hady, Intan Ferial Haris Hermawan, Haris Izzudin, Ahmad Kuncoro, Muh Alfan Maheni Ika Sari Mathew, Raisun Moh. Halim Mubarok, Muhammad Umam Ningsih, Ila Irmawati Norita Citra Yuliarti, Norita Citra Nursaidah Nurul Huda Oktavilia, Diah Ajeng Prabatwati, Titis Alvita Prastyo, Rival Bagus Priatmaja, Cantika Rahmadhantri Qurotul Aini Rahmatullah, Febri Ratnasari, Sevita Riya, Nila Rosa Romadhon, Fathan Sevita Ratnasari, Sevita Sumowo, Seno Sunarto, Danar Aldova Tatit Diansari Reskiputri, Tatit Diansari Trias Setyowati, Trias Wahyu Eko Setianingsih Wardiyah, Silvi Wicaksono, Deni Arto Yulinartati Zelarsianti, Asri Andira