The list of the included 94 studies has been attached in the Appendix 2.
* Akartuna, E. A., & Manning, M. G. (2026). An artificial intelligence-driven temporal network analysis of Myanmar’s cyber scam ecosystem. Asian Journal of Criminology, 21(1), 10. https://doi.org/10.1007/s11417-025-09474-0
Google Scholar
* 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
Google Scholar
* Ayoola, V. B., Ugochukwu, U. N., Adeleke, I., Michael, C. I., Adewoye, M. B., & Adeyeye, Y. (2024). Generative AI-driven fraud detection in health care enhancing data loss prevention and cybersecurity analytics for real-time protection of patient records. International Journal of Scientific Research and Modern Technology, 3(11), 89-107. https://doi.org/10.38124/ijsrmt.v3i11.112
Google Scholar
* Bahnsen, A. C., Aouada, D.,Stojanovic, A., & Ottersten, B. (2016). Feature engineering strategies for credit card frauddetection. E xpert Systems with Applications, 51, 134-142. https://doi.org/10.1016/j.eswa.2015.12.030
* Bilal, D., He, J., & Liu, J. (2025). Guest editorial: AI in education: Transforming teaching and learning. Information and Learning Sciences, 126(1-2), 1-7. https://doi.org/10.1108/ils-01-2025-268
Google Scholar
* Braithwaite, J. (2021). Street-level meta-strategies: Evidence on restorative justice and responsive regulation. Annual Review of Law and Social Science, 17(17, 2021), 205-225. https://doi.org/10.1146/annurev-lawsocsci-111720-013149
Google Scholar
* Carcillo, F., Le Borgne, Y. A., Caelen, O., Kessaci, Y., Oblé, F., & Bontempi, G. (2019). Combining unsupervised and supervised learning in credit card fraud detection. Information Sciences, 557, 317-331. https://doi.org/10.1016/j.ins.2019.05.042
Google Scholar
* Chang, Y. C., & Aïmeur, E. (2024, December). Chat or trap? Detecting scams in messaging applications with large language models. In 2024 8th Cyber Security in Networking Conference (CSNet) (pp. 92-99). IEEE.
* Chen, A., Liu, L., & Zhu, T. (2024). Advancing the democratization of generative artificial intelligence in healthcare: A narrative review. Journal of Hospital Management and Health Policy, 8, 1-18. https://jhmhp.amegroups.org/article/view/8842
Google Scholar
* Cheng, M., Edwards, D., Darcy, S., & Redfern, K. (2018). A tri-method approach to a review of adventure tourism literature: Bibliometric analysis, content analysis, and a quantitative systematic literature review. Journal of Hospitality & Tourism Research, 42(6), 997-1020. https://doi.org/10.1177/1096348016640588
Google Scholar
* Corcoran, J., & Zahnow, R. (2022). Weather and crime: A systematic review of the empirical literature. Crime Science, 11, 16. https://doi.org/10.1186/s40163-022-00179-8
Google Scholar
* Dal Pozzolo, A., Caelen, O., Borgne, L., Waterschoot, Y. A., S., & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 41(10), 4915-4928. https://doi.org/10.1016/j.eswa.2014.02.026
Google Scholar
* 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
Google Scholar
* Ferrara, E. (2024). GenAI against humanity: Nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, 7(1), 549-569. https://doi.org/10.1007/s42001-024-00250-1
Google Scholar
* Fu, K., Cheng, D., Tu, Y., Zhang, L. (2016). CreditCard Fraud Detection Using Convolutional Neural Networks. In: Hirose, A., Ozawa, S., Doya, K.,Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing (pp. 483-490). Cham: SpringerInternational Publishing. https://doi.org/10.1007/978-3-319-46675-0_53
* Gan, R., Zhou, L., Wang, L., Qin, K., & Lin, X. (2024). Defialigner: Leveraging symbolic analysis and large language models for inconsistency detection in decentralized finance. In 6th Conference on Advances in Financial Technologies (AFT 2024) (pp. 7 - 1). Schloss Dagstuhl-Leibniz-Zentrum für Informatik.
* Gehrmann, S., Huang, C., Teng, X., Yurovski, S., Bhorkar, A., Thomas, N., Doucette, J., Rosenberg, D., Dredze, M., & Rabinowitz, D. (2025). Understanding and mitigating risks of generative AI in financial services. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3715275.3732168
* Gregory, G., & Vito, L. (2024). ChatGPT: A Canary in the coal mine or a Parrot in the echo chamber? Detecting fraud with LLM: The case of FTX. Finance Research Letters, 70, 106349. https://doi.org/10.1016/j.frl.2024.106349
Google Scholar
* Gupta, M., Wasi, A., Verma, A., & Awasthi, S. (2021). Document clustering and topic classification using latent dirichlet allocation. In 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India.
* Hasanov, I., Virtanen, S., Hakkala, A., & Isoaho, J. (2024). Application of large Language models in cybersecurity: A systematic literature review. IEEE Access : Practical Innovations, Open Solutions, 12, 176751-176778. https://doi.org/10.1109/ACCESS.2024.3505983
Google Scholar
* Heaslip, E. (2025). What’s the difference between traditional and generative AI? CO — by U.S. Chamber of Commerce. https://www.uschamber.com/co/run/technology/traditional-ai-vs-generative-ai
* Heiding, F., Schneier, B., Vishwanath, A., Bernstein, J., & Park, P. S. (2024). Devising and detecting phishing emails using large Language models. IEEE Access, 12, 42131-42146. https://doi.org/10.1109/ACCESS.2024.3375882
Google Scholar
* Hewett, J., & Leeke, M. (2022). Developing a GPT-3-based automated victim for advance fee fraud disruption. In 2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC) (pp. 205-211). IEEE.
* Irvin-Erickson, Y. (2024). Identity fraud victimization: A critical review of the literature of the past two decades. Crime Science, 13, 3. https://doi.org/10.1186/s40163-024-00202-0
Google Scholar
* Jiang, L., Wang, J., Wang, Y., Yang, H., Kong, L., Wu, Z., Shen, A., Huang, Z., & Jiang, Y. (2025). Bibliometric and LDA analysis of acute rejection in liver transplantation: Emerging trends, immunotherapy challenges, and the role of artificial intelligence. Cell Transplantation, 34, 09636897251325628. https://doi.org/10.1177/09636897251325628
Google Scholar
* Joshi, S. (2025). The transformative role of agentic GenAI in shaping workforce development and education in the US. Iconic Research and Engineering Journals, 8(8), 199-206. https://www.irejournals.com/paper-details/1707138
Google Scholar
* Joshi, R., Pandey, K., & Kumari, S. (2025). Generative AI: A transformative tool for mitigating risks for financial frauds. In Generative Artificial intelligence in finance: Large Language models, interfaces, and industry use cases to transform accounting and finance processes (pp. 125-147). https://doi.org/10.1002/9781394271078.ch7
* Khan, H., Sayyed, N., Sulthana, R., & Yaseen, S. (2025). Transforming fin-tech: The role of generative AI in Indian startups. In Generative AI for business analytics and strategic decision making in service industry (pp. 341-358). https://doi.org/10.4018/979-8-3693-7026-1.ch013
* Koletsis, P., Gemos, P. K., Chronis, C., Varlamis, I., Efthymiou, V., & Papadopoulos, G. T. (2024). Entity extraction from high-level corruption schemes via large language models. In 2024 IEEE International Conference on Big Data (BigData) (pp. 2753-2761). IEEE.
* Korkanti, S. (2024). Enhancing financial fraud detection using LLMs and advanced data analytics. In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS) (pp. 1328-1334). IEEE.
* Kruhlov, V., Bobos, O., Hnylianska, O., Rossikhin, V., & Kolomiiets, Y. (2024). The role of using artificial intelligence for improving the public service provision and fraud prevention. Pakistan Journal of Criminology, 16(2), 913-928.
Google Scholar
* Kumar, K., & Bhushan, B. (2023). Augmenting cybersecurity and fraud detection using artificial intelligence advancements. In 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 1207-1212). IEEE.
* Kurshan, E., Mehta, D., & Balch, T. (2024). AI versus ai in financial crimes & detection: GenAI crime waves to co-evolutionary AI. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 745-751).
* Lin, D. (2024). Key considerations to be applied while leveraging machine learning for financial statement fraud detection: A review. IEEE Access : Practical Innovations, Open Solutions, 12, 168213-168228. https://doi.org/10.1109/ACCESS.2024.3488832
Google Scholar
* Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2019). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175-194. https://doi.org/10.1177/0312896219877678
Google Scholar
* Liu, Y. (2024). Optimization of financial fraud detection algorithm by combining variational autoencoder with generative adversarial networks. In 2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC) (pp. 1-7). IEEE. https://doi.org/10.1109/ICDSCNC62492.2024.10941350
* Liu, Y., & Gong, Z. (2025). Cycling topic graph learning for neural topic modeling. Knowledge-Based Systems, 310, 112905. https://doi.org/10.1016/j.knosys.2024.112905
Google Scholar
* Malik, K. M., Krishnamurthy, M., Alobaidi, M., Hussain, M., Alam, F., & Malik, G. (2020). Automated domain-specific healthcare knowledge graph curation framework: Subarachnoid hemorrhage as phenotype. Expert Systems with Applications, 145, 113120. https://doi.org/10.1016/j.eswa.2019.113120
Google Scholar
* Mallard, G., & Sun, J. (2024). International law, security, and sanctions: A decolonial perspective on the transnational legal order of sanctions. Annual Review of Law and Social Science, 20, 97-116. https://doi.org/10.1146/annurev-lawsocsci-042022-111630
Google Scholar
* Marella, B. C. C., & Kodi, D. (2025). Generative AI for fraud prevention: A new frontier in productivity and green innovation. In Advancing social equity through accessible green innovation (pp. 185-200). https://doi.org/10.4018/979-8-3693-9471-7.ch012
* Martineau, K. (2023). What is generative AI? IBM Research Blog. https://research.ibm.com/blog/what-is-generative-AI
* McKeown, S., & Mir, Z. M. (2021). Considerations for conducting systematic reviews: Evaluating the performance of different methods for de-duplicating references. Systematic Reviews, 10(1), 38. https://doi.org/10.1186/s13643-021-01583-y
Google Scholar
* Mhammad, A. F., Agarwal, R., Columbo, T., & Vigorito, J. (2023). Generative & responsible AI – LLMs use in differential governance.
* Mongkolrob, S., Intarasema, S., & Premchaiswadi, W. (2024). From data to decisions: Enhancing loan applications with process mining techniques. In 2024 22nd International Conference on ICT and Knowledge Engineering (ICT&KE) (pp. 1-8). IEEE.
* Mothukuri, V., Parizi, R. M., Massa, J. L., & Yazdinejad, A. (2024). An AI multi-model approach to defi project trust scoring and security. In 2024 IEEE International Conference on Blockchain (Blockchain) (pp. 19-28). IEEE.
* Naitali, A., Ridouani, M., Salahdine, F., & Kaabouch, N. (2023). Deepfake attacks: Generation, detection, datasets, challenges, and research directions. Computers, 12(10), 216. https://doi.org/10.3390/computers12100216
Google Scholar
* Olasiuk, H. K., Singh, S., & Ganushchak, S. (2023). T. Mapping research clusters of artificial intelligence for financial services using topic modelling: A machine learning insight. In 2023 global conference on information technologies and communications (GCITC), Bangalore, India.
* Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
* Palaiokrassas, G., Scherrers, S., Ofeidis, I., & Tassiulas, L. (2024). Leveraging machine learning for multichain defi fraud detection. In 2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) (pp. 678-680). IEEE.
* Papasavva, A., Lundrigan, S., Lowther, E., Johnson, S., Mariconti, E., Markovska, A., & Tuptuk, N. (2025). Applications of AI-based models for online fraud detection and analysis. Crime Science, 14, 7. https://doi.org/10.1186/s40163-025-00248-8
Google Scholar
* Patel, H., Rehman, U., & Iqbal, F. (2024). Evaluating the efficacy of large language models in identifying phishing attempts. In 2024 16th International Conference on Human System Interaction (HSI) (pp. 1-7). IEEE.
* Pavlik, G. (2025). What is generative AI (GenAI)? How does it work? Oracle. https://www.oracle.com/artificial-intelligence/generative-ai/what-is-generative-ai/
* Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Blackwell.
* Rawal, R., Sachdeva, P., & Singhvi, A. S. (2025). The significance of generative AI in enhancing fraud detection and prevention within the banking industry. In Generative artificial intelligence in finance: Large language models, interfaces, and industry use cases to transform accounting and finance processes (pp. 159-173). https://doi.org/10.1002/9781394271078.ch9
* Rortais, A., Barrucci, F., Ercolano, V., Linge, J., Christodoulidou, A., Cravedi, J. P., Garcia-Matas, R., Saegerman, C., & Svečnjak, L. (2021). A topic model approach to identify and track emerging risks from beeswax adulteration in the media. Food Control, 119, 107435. https://doi.org/10.1016/j.foodcont.2020.107435
Google Scholar
* Secinaro, S., Dal Mas, F., Brescia, V., & Calandra, D. (2022). Blockchain in the accounting, auditing and accountability fields: A bibliometric and coding analysis. Accounting Auditing & Accountability Journal, 35(9), 168-203. https://doi.org/10.1108/AAAJ-10-2020-4987
Google Scholar
* Shafik, W. (2024). The role of generative artificial intelligence in E- commerce fraud detection and prevention. In Strategies for E-commerce data security: Cloud, Blockchain, AI, and Machine Learning (pp. 430-469). https://doi.org/10.4018/979-8-3693-6557-1.ch018
* Shibli, A. M., Pritom, M. M. A., & Gupta, M. (2024). Abusegpt: Abuse of generative AI chatbots to create smishing campaigns. In 2024 12th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-6). IEEE.
* Singh, C. (2025). Is generative AI (Artificial Intelligence) the next advent in the evolution of finance and navigating financial crime and regulation? Journal of Financial Crime, 32(3), 751-759. https://doi.org/10.1108/JFC-07-2024-0232
Google Scholar
* Siva, V., Gremyr, I., Bergquist, B., Garvare, R., Zobel, T., & Isaksson, R. (2016). The support of quality management to sustainable development: A literature review. Journal of Cleaner Production, 138, 148-157. https://doi.org/10.1016/j.jclepro.2016.01.020
Google Scholar
* Soltani, M., Kythreotis, A., & Roshanpoor, A. (2023). Two decades of financial statement fraud detection literature review; combination of bibliometric analysis and topic modeling approach. Journal of Financial Crime, 30(5), 1367-1388. https://doi.org/10.1108/JFC-09-2022-0227
Google Scholar
* Stănescu, G., & Oprea, S. V. (2025). Recent trends and insights in semantic web and ontology-driven knowledge representation across disciplines using topic modeling. Electronics, 14(7), 1313.
Google Scholar
* Suárez, E., Calvo-Mora, A., Roldán, J. L., & Periáñez-Cristóbal, R. (2017). Quantitative research on the EFQM excellence model: A systematic literature review (1991-2015). European Research on Management and Business Economics, 23(3), 147-156. https://doi.org/10.1016/j.iedeen.2017.05.002
Google Scholar
* Sun, J., Gu, S., & Su, R. (2026). AI-empowered responsive regulation for preventing future crimes: An empirical inquiry into the regulatory pyramid to combat future crimes in China and Southeast Asia. Asian Journal of Criminology, 21(1), 8.https://doi.org/10.1007/s11417-025-09477-x
Google Scholar
* Tiwari, M., Gepp, A., & Kumar, K. (2020). A review of money laundering literature: The state of research in key areas. Pacific Accounting Review, 32(2), 271-303. https://doi.org/10.1108/PAR-06-2019-0065
Google Scholar
* Tiwari, M., Rathore, V., & Jecklin, C. (2025a). Food crime: Deterrence of a potential money laundering typology through blockchain and generative artificial intelligence (Gen AI). European Journal on Criminal Policy and Research. https://doi.org/10.1007/s10610-025-09641-0
Google Scholar
* Tiwari, M., Zhou, Y., Childs, A., Chang, L. Y. C., & Ferrill, J. (2025b). Metaverse policing: A systematic literature review of challenges and recommendations. Computers in Human Behavior, 166, 108591. https://doi.org/10.1016/j.chb.2025.108591
Google Scholar
* Truong, L., & Samuel, B. (2025). Exposing the impact of GenAI for cybercrime: An investigation into the dark side. ArXiv. https://doi.org/10.48550/arXiv.2505.23733
* Verma, A., Sankhyayan, S., Jawanda, K., & Tandon, S. (2024). Generative artificial intelligence and cybersecurity risks: Issues and challenges. In International Conference on ICT for Sustainable Development (pp. 321-327). Singapore: Springer Nature Singapore.
* Weber, M., Domeniconi, G., Chen, J., Weidele, D., Bellei, C., Robinson, T., & Leiserson, C. (2019). Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics. ArXiv. https://doi.org/10.48550/arXiv.1908.02591
Google Scholar
* Yigit, Y., Buchanan, W. J., Tehrani, M. G., & Maglaras, L. (2024). Review of generative AI methods in cybersecurity. arXiv. https://arxiv.org/abs/2403.08701
* Zheng, X., Li, J., Lu, M., & Wang, F. Y. (2024b). New paradigm for economic and financial research with generative AI: Impact and perspective. IEEE Transactions on Computational Social Systems, 11(3), 3457-3467. https://doi.org/10.1109/TCSS.2023.3334306
Google Scholar
* Zheng, L., He, Z., & He, S. (2025). A topic model-based knowledge graph to detect product defects from social media data. Expert Systems with Applications, 268, 126313. https://doi.org/10.1016/j.eswa.2024.126313
Google Scholar
* Zhou, Y., Tiwari, M., Lee, C. S., & Dupont, B. (2026). Exploring future crimes: Technologies, digitalization, and criminal malleability. Asian Journal of Criminology, 21(1), 11. https://doi.org/10.1007/s11417-025-09471-3
Google Scholar
