Unveiling the potential of artificial intelligence in fraud detection: Trends and insights from a bibliometric perspective
Keywords:
Fraud Detection, Artificial Intelligence, Machine Learning, Bibliometric Analysis, Financial Technology.Abstract
Digital transaction fraud is a growing concern that requires scalable detection solutions. Artificial Intelligence (AI) provides practical tools for uncovering complex fraud patterns through large-scale data analysis. This study conducted a bibliometric analysis of publications from 2013 to 2024, following the PRISMA protocol. Relevant literature was retrieved from major academic databases, with a focus on AI applications in fraud detection. The data were analyzed using VOSviewer to identify keyword trends, research clusters, country-level contributions, and collaboration networks. The findings reveal a significant rise in scholarly interest in recent years, with the financial and banking sectors as the primary application areas. Research is primarily dominated by developed countries, especially the United States, with universities acting as key research and funding centers. However, challenges remain, including limited access to quality datasets, the complexity of AI models, and ethical concerns related to data privacy and algorithmic fairness. AI shows strong potential in enhancing fraud detection; however, further research needs to address the technical and ethical obstacles to its broader implementation.Downloads
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Widyantoro, T., & Arief, M. (2022). The current and future research on environmental, social, and governance (ESG) performance: A bibliometric analysis. Proceedings of the 2nd Indian International Conference on Industrial Engineering and Operations Management.
Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
ACFE, I. C. (2019). Survei fraud Indonesia 2019.
Alatawi, I. A., Ntim, C. G., Zras, A., & Elmagrhi, M. H. (2023). CSR, financial and non-financial performance in the tourism sector: A systematic literature review and future research agenda. In International Review of Financial Analysis (Vol. 89). Elsevier Inc. https://doi.org/10.1016/j.irfa.2023.102734
Behl, A., Gaur, J., Pereira, V., Yadav, R., & Laker, B. (2022). Role of big data analytics capabilities to improve sustainable competitive advantage of
MSME service firms during COVID-19 – A multi-theoretical approach. Journal of Business Research, 148, 378–389. https://doi.org/10.1016/j.jbusres.2022.05.009
Borovykh, A., Bohte, S., & Oosterlee, C. W. (2018). Conditional time series forecasting with convolutional neural networks. http://arxiv.org/abs/1703.04691
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Colloquium on the Sociological Analysis of Scientific and Technical Research: Social Science Information, 22, 191–235.
Castro-Espin, C., & Agudo, A. (2022). The role of diet in prognosis among cancer survivors: A systematic review and meta-analysis of dietary patterns and diet interventions. Nutrients, 14(2). https://doi.org/10.3390/nu14020348
Choithani, T., Chowdhury, A., Patel, S., Patel, P., Patel, D., & Shah, M. (2024). A comprehensive study of artificial intelligence and cybersecurity on bitcoin, crypto currency and banking system. Annals of Data Science, 11(1), 103–
135. https://doi.org/10.1007/s40745-022-00433-5
Cholakov, G., & Stoyanova-Doycheva, A. (2024). Extending fraud detection in students exams using AI. TEM Journal, 13(4), 3068–3078. https://doi.org/10.18421/TEM134-41
Desai, V. S., Crook, J. N., & Overstreet, G. A. (1996). A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research, 95(1), 24–37. https://doi.org/10.1016/0377-2217(95)00246-4
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Garza-Reyes, J. A. (2015). Lean and green-a systematic review of the state of the art literature. Journal of Cleaner Production, 102, 18–29. https://doi.org/10.1016/j.jclepro.2015.04.064
Harsanto, B., & Firmansyah, E. A. (2023). A twenty years bibliometric analysis (2002–2021) of business economics research in ASEAN. Cogent Business and Management, 10(1).
https://doi.org/10.1080/23311975.2023.2194467
Heersmink, R., Hoven, J. van den, Eck, N. J. van, & Berg, J. van den. (2011). Bibliometric mapping of computer and information ethics. Ethics and Information Technology, 13(3), 241–249. https://doi.org/10.1007/s10676-011-9273-7
Husnaningtyas, N., & Dewayanto, T. (2023). Financial fraud detection and machine learning algorithm (unsupervised learning): Systematic literature review. Jurnal Riset Akuntansi Dan Bisnis Airlangga, 8(2), 2023. https://e- journal.unair.ac.id/jraba
Nawaz, K., Saeed, H. A., & Sajeel, T. A. (2020). Covid-19 and the state of research from the perspective of psychology. International Journal of Business and Psychology, 2(1), 35–44.
Pathak, D., Sharma, J. P., & Patnaik, S. (2022). Twenty years of entrepreneurship and innovation research: A bibliometric analysis. Journal of Operations and Strategic Planning, 5(1).
Putra, E. F., & Kosala, R. (2011). Application of artificial neural networks to predict intraday trading signals. Proceedings of the 10th WSEAS International Conference on E-Activities, 174–179.
Sharifani, K., & Amini, M. (2023). Machine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, 10(7). https://ssrn.com/abstract=4458723
Sood, P., Sharma, C., Nijjer, S., & Sakhuja, S. (2023). Review the role of artificial intelligence in detecting and preventing financial fraud using natural language processing. International Journal of System Assurance Engineering and Management, 14(6), 2120–2135. https://doi.org/10.1007/s13198-023-02043-7
Tautiva, J. A. D., Rivera, F. I. R., Celume, S. A. B., & Rivera, S. A. R. (2024). Mapping the research about organisations in the latin american context: A bibliometric analysis. Management Review Quarterly, 74(1), 121–169. https://doi.org/10.1007/s11301-022-00296-3
Teng, H. W., & Lee, M. (2019). Estimation procedures of using five alternative machine learning methods for predicting credit card default. Review of Pacific Basin Financial Markets and Policies, 22(3). https://doi.org/10.1142/S0219091519500218
Widyantoro, T., & Arief, M. (2022). The current and future research on environmental, social, and governance (ESG) performance: A bibliometric analysis. Proceedings of the 2nd Indian International Conference on Industrial Engineering and Operations Management.
Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629
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2025-07-31
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Unveiling the potential of artificial intelligence in fraud detection: Trends and insights from a bibliometric perspective. (2025). Proceeding of International Conference on Economics, Technology, Management, Accounting, Education, and Social Science (ICETEA), 1, 859-879. https://conference.unita.ac.id/index.php/icetea/article/view/430
