The Impact of AI-Driven Digital Transformation on Customer Experience in FUGAZ Banks in Nigeria
Authors
Lincoln International Business School, University of Lincoln (United Kingdom)
Article Information
DOI: 10.47772/IJRISS.2026.100700868
Subject Category: Computer Science
Volume/Issue: 10/7 | Page No: 12860-12867
Publication Timeline
Submitted: 2026-08-02
Accepted: 2026-08-07
Published: 2026-08-14
Abstract
Artificial intelligence (AI) has become central to the digital transformation strategies of Nigeria's five most systemically significant deposit money banks: First Bank, United Bank for Africa, GTBank, Access Bank, and Zenith Bank, collectively known as FUGAZ. These institutions have invested substantially in AI-driven technologies including chatbots, predictive analytics, fraud detection systems, and automated customer service platforms, with the stated ambition of improving customer experience and service delivery. Yet the relationship between these investments and their actual customer experience outcomes remains poorly understood and, in the existing literature, insufficiently examined. Operational metrics: transaction speeds, fraud detection rates, system uptime; have been measured with some rigour, but whether AI-driven transformation has made banking genuinely better for Nigerian customers, in the full sense of that phrase, has not.
This study argues that AI-driven digital transformation in Nigerian banking has delivered efficiency without consistently delivering empathy. The efficiency gains are real: AI has made routine banking transactions faster, more available, and more personalised for a significant portion of FUGAZ customers. But efficiency is not the whole of customer experience. When customers encounter banking services at their most consequential moments: a frozen account, a disputed transaction, a declined loan application, what they require is not speed but responsiveness; not automation but human judgment and felt fairness. These are precisely the dimensions of customer experience that current AI systems are least equipped to provide, and their systematic absence from AI-driven service design represents the central failure of FUGAZ banks' transformation programs as evaluated from a customer experience perspective.
This research employs a qualitative secondary research methodology, structured as an embedded multiple case study of FUGAZ banks. Drawing on fifty peer-reviewed academic publications and credible institutional reports sourced from Emerald Insight, ScienceDirect, ResearchGate, and Taylor & Francis Online, a structured PRISMA-informed literature selection process was followed. The resulting evidence was examined using thematic and comparative analysis. The study is guided theoretically by the Technology Acceptance Model (TAM): extended to incorporate infrastructure reliability, institutional trust, and digital competency as mediating factors specific to the Nigerian context, Service-Dominant Logic (SDL), and a project management benefits realisation perspective. Together, these frameworks provide a more complete analytical lens than any single model could, capturing technology adoption behaviour, value co-creation dynamics, and the gap between project delivery and actual customer benefit.
The findings reveal that AI-driven transformation has improved customer experience conditionally rather than universally. For urban, digitally literate customers engaging with routine transactions, measurable improvements in speed, availability, and personalisation are documented. For customers in rural areas, those with lower digital literacy, and all customers navigating emotionally weighted or complex interactions, the evidence is far less positive and in some cases points toward active deterioration in service quality since AI displaced human interaction. Infrastructure constraints, digital literacy gaps, institutional trust deficits, and weak AI governance frameworks are identified as the primary conditions that determine whether AI investment translates into genuine customer value. Across all five FUGAZ institutions, a systematic gap is observed between what AI systems have delivered technically and what customers have experienced in practice: a benefits realisation failure that cannot be resolved through further technical investment alone.
This research work concludes that FUGAZ banks must move beyond an efficiency-centred conception of AI's value to their customers. Sustainable improvement in customer experience through AI requires hybrid service models that preserve human interaction pathways for complex and emotionally weighted situations; targeted investment in customer digital literacy as a precondition for AI value delivery; transparent AI governance frameworks that address the accountability and redress gaps currently undermining institutional trust; and infrastructure-sensitive design that ensures AI services function reliably across Nigeria's diverse socioeconomic geography. From a project management perspective, AI initiatives in Nigerian banking should be evaluated not on deployment metrics but on whether their intended customer experience benefits are being realized and for whom.
Keywords
Artificial intelligence; digital transformation; customer experience; Nigerian banking; FUGAZ banks; Technology Acceptance Model; benefits realization
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References
1. Abdulsalam, T.A. and Tajudeen, R.B. (2024) Artificial intelligence in the banking industry: A review of service areas and customer service journeys in emerging economies. Business & Management Compass, 68(3), pp.19-43. [Google Scholar] [Crossref]
2. Agu, A.G. and Margaça, C. (2025) Digital transformation and religious entrepreneurship in Nigeria: Integrating artificial intelligence toward competitive advantage. African Journal of Economic and Management Studies, 16(2), pp.305-319. [Google Scholar] [Crossref]
3. Alaba, J.S., Ahmed, S.J. and Farida, A.P. (2025) Adoption of AI-driven fraud detection system in the Nigerian banking sector: An analysis of cost, compliance, and competency. Economic Review of Nepal, 7(2), pp.1-18. [Google Scholar] [Crossref]
4. Alakitan, M. and Makinde, E. (2025) Where are the ethical guidelines? Examining the governance of digital technologies and AI in Nigeria. Policy & Internet, 17(1), pp.1-18. [Google Scholar] [Crossref]
5. Aliyu, I. and Iheonkhian, I.S. (2025) Impact of artificial intelligence on financial services in Nigeria. Journal of Accounting and Financial Management, 11(3), pp.158-171. [Google Scholar] [Crossref]
6. Amaechi, K. (2025) Regulation of AI in Nigeria: Way forward. SSRN Electronic Journal. [Google Scholar] [Crossref]
7. Awosusi, C.T. (2025) Impact of AI on bank performance: A case study on Access Bank in Nigeria. WAUU Journal of Management Studies, 5(1), pp.30-46. [Google Scholar] [Crossref]
8. Bradley, G. (2010) Benefit Realisation Management. Farnham: Gower. [Google Scholar] [Crossref]
9. Braun, V. and Clarke, V. (2006) Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), pp.77-101. [Google Scholar] [Crossref]
10. CASP (2018) CASP Qualitative Checklist. Oxford: Critical Appraisal Skills Programme. [Google Scholar] [Crossref]
11. Central Bank of Nigeria (CBN) (2023) Financial Stability Report. Abuja: Central Bank of Nigeria. [Google Scholar] [Crossref]
12. Central Bank of Nigeria (CBN) (2025) Fintech Assessment Report. Abuja: Central Bank of Nigeria. [Google Scholar] [Crossref]
13. Davis, F.D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), pp.319-340. [Google Scholar] [Crossref]
14. Dewi, N.R. and Vidyasari, R. (2025) The influence of service quality, trust, and security on customer satisfaction using Bank Riau Kepri Syariah mobile. Proceeding of ICETEA. [Google Scholar] [Crossref]
15. Ekwe, M. (2025) Developing an AI-powered sales framework for the digital transformation of B2B banking in Nigeria. University of Eastern Finland Repository. [Google Scholar] [Crossref]
16. Eyo-Udo, N.L., Apeh, C.E., Bristol-Alagbariya, B. and Udeh, C.A. (2025) Digital banking in Africa: A review of recent developments and challenges. ResearchGate. [Google Scholar] [Crossref]
17. Fatokun, B.O. (2023) Customers' affective responses towards the key factors influencing e-commerce adoption: Extended technology acceptance model approach. Liverpool John Moores University Repository. [Google Scholar] [Crossref]
18. Floridi, L. and Cowls, J. (2021) A unified framework of five principles for AI in society. Harvard Data Science Review, 3(1). [Google Scholar] [Crossref]
19. Isaac, O.I. and Jimoh, D. (2025) Effect of artificial intelligence on the financial performance of deposit money banks in Nigeria. International Journal of Economics, Business and Management Research, 9(1), pp.112-125. [Google Scholar] [Crossref]
20. Jonnalagadda, S. (2023) Bank of Things (BoT): Digital transformation of banks using IoT to enhance customer experience. ProQuest Dissertations Publishing. [Google Scholar] [Crossref]
21. Kaondera, P.R., Chikazhe, L. and Munyimi, T.F. (2023) Buttressing customer relationship management through digital transformation: Perspectives from Zimbabwe's commercial banks. Cogent Social Sciences, 9(1), 2191432. [Google Scholar] [Crossref]
22. Kaplan, A. and Haenlein, M. (2019) Siri, Siri, in my hand: Who's the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), pp.15-25. [Google Scholar] [Crossref]
23. KPMG West Africa (2025) Competing for the Customer: 2025 West Africa Banking Industry Customer Experience Survey. Lagos: KPMG Advisory Services. [Google Scholar] [Crossref]
24. Mohammed, M.A. and Okechukwu, N.E. (2025) Impact of artificial intelligence on customer service in the banking sector: A case study of deposit money banks in Abuja. African Research Reports, 3(2), pp.25-39. [Google Scholar] [Crossref]
25. Muhaizam, M.M. and Sarah, S. (2025) Sustaining digital transformation: The human touch in AI adoption for economic resilience in Malaysia's financial sector. Journal of Business and Social Sciences, 20(2), pp.45-59. [Google Scholar] [Crossref]
26. Nashikha, A., Huda, M.Q. and Fitroh, F. (2025) Mobile banking service quality and user loyalty using MSQUAL: A systematic literature review. Sinkron, 10(1), pp.150-162. [Google Scholar] [Crossref]
27. Nguemo, O.E. and Ekokotu, R.N. (2025) Technology investment and sustainable growth of listed deposit money banks in Nigeria. International Journal of Social Sciences and Humanities Research, 13(2), pp.308-316. [Google Scholar] [Crossref]
28. Nigeria Inter-Bank Settlement System (NIBSS) (2025) Industry e-Payment Statistics 2024. Lagos: NIBSS. [Google Scholar] [Crossref]
29. Nwaobi, G.C. (2024) Nigerian firms and digital transformation: Incubations, unipoding and prospects. Munich Personal RePEc Archive. [Google Scholar] [Crossref]
30. Odufisan, O.I., Abhulimen, O.V. and Ogunti, E.O. (2025) Harnessing artificial intelligence and machine learning for fraud detection and prevention in Nigeria. Journal of Economic Criminology, 2(1), pp.15-29. [Google Scholar] [Crossref]
31. Odufuwa, F., Deen-Swarray, M. and Ahmed, A.A. (2024) Digital technology adoption by microenterprises: Nigeria report. Research ICT Africa Policy Paper. [Google Scholar] [Crossref]
32. Okeke, L. (2025) AI-powered chatbots and customer experience in Nigeria's banking sector: Opportunities and challenges. Nnadiebube Journal of Social Sciences, 7(1), pp.144-161. [Google Scholar] [Crossref]
33. Oyetunji, D.J. (2024) The role of artificial intelligence and machine learning in enhancing customer experience in Nigeria digital banks. ResearchGate. [Google Scholar] [Crossref]
34. Ozor, N., Nwobodo, C. and Onwualu, P. (2025) Policy and institutional imperatives for strengthening the Nigerian research and innovation funding ecosystem. IDRC Repository. [Google Scholar] [Crossref]
35. Pousttchi, K. and Dehnert, M. (2018) Exploring the digitalization impact on consumer decision-making in retail banking. Electronic Markets, 28(3), pp.265-286. [Google Scholar] [Crossref]
36. Russell, S. and Norvig, P. (2021) Artificial Intelligence: A Modern Approach. 4th edn. Pearson Education. [Google Scholar] [Crossref]
37. Saunders, M., Lewis, P. and Thornhill, A. (2019) Research Methods for Business Students. 8th edn. Harlow: Pearson. [Google Scholar] [Crossref]
38. Shehadeh, M. (2025) Disclosures on digital transformation strategy and financial technology in Jordanian banks: Innovations, challenges, and opportunities. Journal of Financial Reporting and Accounting. [Google Scholar] [Crossref]
39. Vargo, S.L. and Lusch, R.F. (2016) Institutions and axioms: An extension and update of service-dominant logic. Journal of the Academy of Marketing Science, 44(1), pp.5-23. [Google Scholar] [Crossref]
40. Verhoef, P.C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J.Q., Fabian, N. and Haenlein, M. (2021) Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, 122, pp.889-901. [Google Scholar] [Crossref]
41. Vial, G. (2019) Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), pp.118-144. [Google Scholar] [Crossref]
42. Yin, R.K. (2018) Case Study Research and Applications: Design and Methods. 6th edn. London: Sage. [Google Scholar] [Crossref]
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