Artificial Intelligence and Machine Learning for Financial Fraud Detection: A Bibliometric and Thematic Review Using SPAR-4-SLR

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Published: December 26, 2025

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Authors

Priyanka Chugh and Devansh Gupta

DOI Number
https://doi.org/10.15415/jtmge/2025.161010

Keywords
Artificial intelligence, Machine learning, Anomaly detection, Fraud analytics, Financial fraud

Abstract

Purpose: The study aims to map and evaluate the intellectual, conceptual, and thematic evolution of research on Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in financial fraud detection. It seeks to uncover dominant knowledge structures, methodological shifts, and emerging frontiers that define the progression of this rapidly expanding domain.

Methods: A systematic bibliometric and thematic analysis was conducted on a corpus of 316 Scopus-indexed publications from 2020–2025. Using the SPAR-4-SLR protocol and the Biblioshiny (R-Bibliometrix) package, the study employed co-word analysis, thematic mapping, and conceptual structure modelling through Louvain clustering and fractional counting to identify key research patterns and trends.

Results: The analysis revealed four primary thematic clusters supervised deep learning models, unsupervised anomaly detection, AI-integrated real-time monitoring, and explainable fraud analytics. The field has evolved from rule-based and static models toward scalable, interpretable, and context-aware architectures such as graph neural networks and federated learning. Emerging studies emphasise explainability, compliance, and ethical deployment.

Implications: The findings guide researchers, practitioners, and regulators toward designing transparent, adaptive, and policy-aligned AI systems for financial fraud prevention and risk management.

Originality: This paper provides the first comprehensive, SPAR-4-SLR–driven bibliometric synthesis of AI and DL applications in financial fraud detection, integrating performance analysis with thematic and conceptual evolution to highlight future research pathways.

References

Abbasi, A., Albrecht, C., Vance, A., & Hansen, J. (2012). Metafraud: A meta-learning framework for detecting financial fraud. MIS Quarterly, 36(4), 1293–1327.
ACFE. (2020). Report to the Nations: Global Study on Occupational Fraud and Abuse. Association of Certified Fraud Examiners.
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Balani, H. (2019). Assessing the introduction of Anti-Money Laundering regulations on bank stock valuation: An empirical analysis. Journal of Money Laundering Control, 22(1), 76–88. https://doi.org/10.1108/JMLC-03-2018-0021
Brkan, M., & Bonnet, G. (2020). Artificial intelligence and the GDPR: Towards trustworthy AI. European Journal of Law and Technology, 11(1), 1–35.
Callon, M., Courtial, J. P., & Laville, F. (1991). Co-word analysis as a tool for describing the network of interactions between basic and technological research: The case of polymer chemistry. Scientometrics, 22(1), 155–205. https://doi.org/10.1007/BF02019280
Canhoto, A. (2021). The persistent challenges of AML: A review of AI-based solutions. Journal of Money Laundering Control, 24(4), 765–781.
Canhoto, A. I. (2021). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of Business Research, 131, 441–452. https://doi.org/10.1016/j.jbusres.2020.10.054
Chauhan, A., Kaur, P., & Gupta, M. (2023). AI-driven financial fraud detection: Advances and challenges. Decision Support Systems, 170, 113882.
Chauhan, V., Kaur, P., & Gupta, A. (2023). Artificial intelligence in financial services: Opportunities, challenges, and future research directions. Journal of Financial Services Marketing, 28(2), 131–144. https://doi.org/10.1057/s41264-023-00167-4
Chen, H., Zhang, Y., & Xiang, Y. (2021). Machine learning-based financial fraud detection: A survey. Expert Systems with Applications, 169, 114418.
Chen, X., Zhang, Z., & Xu, J. (2021). Artificial intelligence in finance: Applications, challenges, and future directions. Technological Forecasting and Social Change, 170, 120899.
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
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Egghe, L. (2005). Power laws in the information production process: Lotkaian informetrics. Elsevier.
European Central Bank. (2018). Fifth report on card fraud. Retrieved from https://www.ecb.europa.eu
Europol. (2021). Internet organised crime threat assessment (IOCTA). European Union Agency for Law Enforcement Cooperation.
Europol. (2023). Internet organised crime threat assessment (IOCTA) 2023. European Union Agency for Law Enforcement Cooperation. https://www.europol.europa.eu/cms/sites/default/files/documents/europol_internet_organised_crime_threat_assessment_iocta_2023.pdf
Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: Strengths and weaknesses. FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492LSF
FATF. (2018). Guidance for a risk-based approach to virtual assets and virtual asset service providers. Financial Action Task Force.
Fiore, U., Santis, A. D., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448–455. https://doi.org/10.1016/j.ins.2018.12.044
Goel, S., & Uzuner, O. (2016). Do sentiments matter in fraud detection? Estimating semantic orientation of annual reports. Intelligent Systems in Accounting, Finance and Management, 23(3), 215–239. https://doi.org/10.1002/isaf.1384
Goodfellow, I., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. International Conference on Learning Representations (ICLR).
Huang, R., Li, P., & Yang, J. (2023). Detecting financial statement fraud using transformer-based language models. Decision Support Systems, 167, 113905.
IMF. (2023). Ukraine: Financial sector stability report. International Monetary Fund.
Li, Y., Chen, H., & Zhang, Y. (2023). Transformer-based sequential modeling for financial fraud detection. Expert Systems with Applications, 219, 119619.
Moed, H. F. (2005). Citation analysis in research evaluation. Springer.
Morales, J., Gendron, Y., & Guénin-Paracini, H. (2019). The construction of the risky individual and vigilant organization: A genealogy of fraud detection. Accounting, Organizations and Society, 81, 101080.
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Paul, J., Lim, W. M., & O’Cass, A. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), O1–O16. https://doi.org/10.1111/ijcs.12695
Paul, J., Lim, W. M., O’Cass, A., Hao, A. W., & Bresciani, S. (2021). Scientific procedures and rationales for systematic literature reviews (SPAR-4-SLR). International Journal of Consumer Studies, 45(4), O1–O16.
Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
Phua, C., Lee, V., Smith, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119.
Price, D. J. de Solla. (1963). Little science, big science. Columbia University Press.
Pumsirirat, A., & Yan, L. (2018). Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine. International Journal of Advanced Computer Science and Applications, 9(1), 18–25. https://doi.org/10.14569/IJACSA.2018.090103
Purda, L., & Skillicorn, D. (2015). Accounting variables, deception, and a bag of words: Assessing the tools of fraud detection. Contemporary Accounting Research, 32(3), 1193–1223. https://doi.org/10.1111/1911-3846.12117
Roy, S., Dutta, A., & Kar, A. (2022). Emerging machine learning techniques for credit card fraud detection: A review. Financial Innovation, 8(1), 1–20.
Schreyer, M., Sattarov, T., Borth, D., Dengel, A., & Reimer, B. (2017). Detection of anomalies in large-scale accounting data using deep autoencoder networks. CoRR, abs/1709.05254. https://arxiv.org/abs/1709.05254
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
UK Finance. (2023). Annual fraud report 2023. UK Finance. https://www.ukfinance.org.uk/system/files/2023-07/UK-Finance-Annual-Fraud-Report-2023.pdf
van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice (pp. 285–320). Springer.
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.1109/TNNLS.2020.2978386
Xu, J., Chen, Y., & Kou, G. (2019). Analyzing the diffusion of blockchain technology in the financial industry. Technological Forecasting and Social Change, 141, 1–9.
Zhang, L., & Chen, X. (2022). Deep learning for financial anomaly detection: A comprehensive review. Information Sciences, 600, 1–25.
Zhang, W., & Jiang, T. (2023). Graph neural networks for fraud detection in financial transaction networks. Engineering Applications of Artificial Intelligence, 124, 106572.
Zheng, Y. J., Zhou, X. H., Sheng, W. G., Xue, Y., & Chen, S. Y. (2018). Generative adversarial network based telecom fraud detection at the receiving bank. Neural Networks, 102, 78–86. https://doi.org/10.1016/j.neunet.2018.02.010
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629

How to Cite

Priyanka Chugh and Devansh Gupta. Artificial Intelligence and Machine Learning for Financial Fraud Detection: A Bibliometric and Thematic Review Using SPAR-4-SLR. J.Technol. Manag. Grow. Econ.. 2025, 16, 118-131

Artificial Intelligence and Machine Learning for Financial Fraud Detection: A Bibliometric and Thematic Review Using SPAR-4-SLR

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