Keywords
Machine Learning
banking
Bibliometric analysis
Chatbots
conversational
How to Cite
Abstract
Abstract: This paper offers a systematic bibliometric review of how research on AI and ML driven chatbots has developed within the banking sector. Drawing on the Biblioshiny interface of the bibliometrix R package, we map the intellectual structure, conceptual evolution, and thematic development of this body of work through performance analysis and science mapping. Scholarly output has grown noticeably since 2020, which seems to track closely with the broader push toward digital transformation in financial services and the rising uptake of generative and conversational AI. The United States, India, and China stand out as the most prolific contributors, and a handful of high-impact journals bridge the gap between artificial intelligence, decision support systems, and financial applications. Thematic and factorial mapping point to machine learning and intelligent systems as the motor themes anchoring the field, with natural language processing (NLP) serving as the indispensable underlying technology. Sentiment analysis, generative AI, large language models, and advanced conversational architectures are all gaining traction as emerging research threads. Across these findings, a consistent pattern appears: the field has been moving away from rigid, rule-based automation toward adaptive, learning-capable chatbot systems now embedded in customer service, financial advisory, and operational decision-making. By charting the intellectual, conceptual, and thematic landscape of this field, the study pulls together scholarship that has so far remained scattered across disciplines, and puts forward a future research agenda built around explainability, responsible AI deployment, and interdisciplinary collaboration. The review should be of interest to researchers, practitioners, and policymakers who want to make sense of where conversational AI in financial services currently stands and where it might be headed.
References
1. Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7
2. Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2(7), 100006. https://doi.org/10.1016/j.mlwa.2020.100006
3. Araujo, T. (2018). (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85(2), 183–189. https://doi.org/10.1080/00913367.2003.10639130
4. 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
5. Artificial Intelligence in Society. (2019). OECD Publishing. https://doi.org/10.1787/eedfee77-en
6. Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management & Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0368
7. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Openai, D. A. (2020). Language Models are Few-Shot Learners.
8. Bughin, J., Brussels, |, Seong, J., Shanghai, |, Manyika, J., Francisco, S., Chui, M., Joshi, R., & Stockholm, |. (n.d.). NOTES FROM THE AI FRONTIER MODELING THE IMPACT OF AI ON THE WORLD ECONOMY. Retrieved February 19, 2026, from www.mckinsey.com/mgi.
9. 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 chemsitry. Scientometrics, 22(1), 155–205. https://doi.org/10.1007/BF02019280
10. Davenport, T. H., Ronanki, R., Wheaton, J., & Nguyen, A. (n.d.). FEATURE ARTIFICIAL INTELLIGENCE FOR THE REAL WORLD 108 HARVARD BUSINESS REVIEW.
11. 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(5), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
12. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57(7), 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
13. 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. The FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492LSF
14. Henk F. Moed. (2005). Citation Analysis in Research Evaluation.
15. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science, mksc.2019.1192. https://doi.org/10.1287/mksc.2019.1192
16. 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). https://doi.org/10.1111/ijcs.12695
17. 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
18. Sources of information on specific subjects 1934. (1985). Journal of Information Science, 10(4), 176–180. https://doi.org/10.1177/016555158501000407
19. Ulrich Gnewuch, Stefan Morana, & Alexander Maedche. (2017). Towards Designing Cooperative and Social Conversational Agents for Customer Service. Proceedings of the 25th European Conference on Information Systems (ECIS).
20. van Eck, N. J., & Waltman, L. (2014). Visualizing Bibliometric Networks. In Measuring Scholarly Impact (pp. 285–320). Springer International Publishing. https://doi.org/10.1007/978-3-319-10377-8_13
21. Zupic, I., & Čater, 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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