Generative Artificial Intelligence and Large Language Models on Retail Banking Effectiveness: Evidence from Hong Kong

This study examines the impact of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) on retail banking effectiveness in Hong Kong. Drawing on an integrated theoretical framework combining the Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and Resource-Based View (RBV), the research adopts a mixed-methods approach using a convergent parallel design. Quantitative data were collected from 100 retail banking users using structured questionnaires, while qualitative insights were obtained from semi-structured interviews with 50 banking employees.
The quantitative findings reveal that both GenAI usage and LLM integration have significant positive effects on retail banking effectiveness, with LLM integration emerging as the stronger predictor. The model explains a substantial proportion of variance in performance outcomes, particularly in operational efficiency, customer satisfaction, and decision accuracy. Complementary qualitative findings indicate that AI technologies enhance banking operations through automation, improved customer service, and data-driven decision-making. However, key challenges such as system integration, implementation costs, and concerns regarding transparency and reliability persist.
The study contributes to the literature by providing empirical evidence from a major financial hub and demonstrating how technological, organisational, and task-related factors jointly influence AI-driven performance. The findings offer practical implications for financial institutions seeking to leverage AI for competitive advantage while addressing ethical and operational challenges.

Generative AI, LLMs, Retail Banking

1. Abdulsalam, Y., & Tajudeen, F. P. (2024). Artificial intelligence in the banking industry: A review of service areas and customer service journeys. Journal of Financial Services Marketing, 29(1), 45–60. [Google Scholar] [Crossref]

2. Ajili Ben Youssef, A., Lamouchi, R., & Hadhri, W. (2025). Artificial intelligence, ethics, and financial services: A systematic review and research agenda. Journal of Business Research, 172, 114–126. [Google Scholar] [Crossref]

3. Ali, O., Abdelbaki, W., Shrestha, A., & Varma, S. (2025). A systematic literature review of artificial intelligence in banking: Opportunities and challenges. Financial Innovation, 11(1), 1–25. [Google Scholar] [Crossref]

4. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. [Google Scholar] [Crossref]

5. Bhatia, A., Bhatia, R., & Sood, S. (2023). Artificial intelligence in banking: A systematic literature review. Journal of Financial Services Marketing, 28(2), 123–138. [Google Scholar] [Crossref]

6. Bhatia, A., Bhatia, R., & Sood, S. (2023). Artificial intelligence in banking: A systematic literature review. Journal of Financial Services Marketing, 28(2), 123–138. [Google Scholar] [Crossref]

7. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. [Google Scholar] [Crossref]

8. Dhake, D., Patil, S., & Kulkarni, P. (2024). Applications of generative AI and large language models in banking and finance. Journal of Internet and E-Business Studies, 2024, 1–12. [Google Scholar] [Crossref]

9. Dhake, D., Patil, S., & Kulkarni, P. (2024). Applications of generative AI and large language models in banking and finance. Journal of Internet and E-Business Studies, 2024, 1–12. [Google Scholar] [Crossref]

10. Dwivedi, Y. K., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on generative AI. International Journal of Information Management, 71, 102642. [Google Scholar] [Crossref]

11. Dwivedi, Y. K., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on generative AI. International Journal of Information Management, 71, 102642. [Google Scholar] [Crossref]

12. Dwivedi, Y. K., et al. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on generative AI. International Journal of Information Management, 71, 102642. [Google Scholar] [Crossref]

13. Fares, D., Ashta, A., & Benlahcene, A. (2022). Efficiency in banking: A review of frontier approaches and operational performance. Journal of Banking and Finance, 134, 106–215. [Google Scholar] [Crossref]

14. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. [Google Scholar] [Crossref]

15. Hong Kong Institute for Monetary and Financial Research. (2025). Artificial intelligence in the financial services sector in Hong Kong. Hong Kong Monetary Authority. [Google Scholar] [Crossref]

16. Hoque, M. R., Bao, Y., & Sorwar, G. (2025). Investigating the impact of artificial intelligence on organisational performance in financial services. Journal of Enterprise Information Management, 38(2), 345–362. [Google Scholar] [Crossref]

17. Huang, M.-H., & Rust, R. T. (2021). Artificial intelligence in service. Journal of Service Research, 24(1), 3–15. [Google Scholar] [Crossref]

18. Kikuchi, T. (2026). Artificial intelligence and the productivity paradox: Evidence from financial institutions. Economic Modelling, 130, 106–120. [Google Scholar] [Crossref]

19. Kristiana, Y. (2024). Improving banking operational efficiency through artificial intelligence and machine learning: Strategy implementation and impact. International Journal of Financial Studies, 12(3), 78–95. [Google Scholar] [Crossref]

20. Kshetri, N. (2023). The economics of artificial intelligence in banking. IT Professional, 25(1), 34–40. [Google Scholar] [Crossref]

21. Kumar, R. (2025). Generative AI and large language models in banking: A technical roadmap. Journal of Financial Technology and Innovation, 3(1), 45–60. [Google Scholar] [Crossref]

22. Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. A. (2022). Exploring the relationship between big data analytics capability and competitive performance. British Journal of Management, 33(2), 428–454. [Google Scholar] [Crossref]

23. Mogaji, E. (2025). Generative AI in banking: Empirical insights on adoption, opportunities, and challenges. International Journal of Bank Marketing, 43(4), 871–890. [Google Scholar] [Crossref]

24. Moro-Visconti, R., Rambaud, S. C., & Pascual, J. L. (2026). Artificial intelligence adoption in banking: Organisational transformation and performance implications. Technological Forecasting and Social Change, 189, 122–135. [Google Scholar] [Crossref]

25. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection. Decision Support Systems, 50(3), 559–569. [Google Scholar] [Crossref]

26. Prajapati, V., & Baheti, M. (2025). Transforming digital banking with artificial intelligence: Enhancing customer experience and financial inclusion. International Journal of Bank Marketing, 43(2), 215–232. [Google Scholar] [Crossref]

27. Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. [Google Scholar] [Crossref]

28. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2022). Artificial intelligence in banking: Challenges and opportunities. Journal of Enterprise Information Management, 35(4/5), 1458–1478. [Google Scholar] [Crossref]

29. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2022). Artificial intelligence in banking: A review and future research agenda. Journal of Enterprise Information Management, 35(4/5), 1458–1478. [Google Scholar] [Crossref]

30. Vuković, D., Papić Blagojević, N., & Kostić, M. (2025). Artificial intelligence and operational efficiency in banking: Evidence from emerging markets. Sustainability, 17(2), 1123. [Google Scholar] [Crossref]

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