Artificial Intelligence and Financial Performance of Listed Deposit Banks in Nigeria

This study examines the relationship between Artificial Intelligence (AI) adoption and the financial performance of Nigeria’s top-tier deposit money banks over a ten year period (2015–2024). Against the backdrop of Nigeria’s accelerating digital transformation, the research examines how AI-driven innovations enhance profitability within the banking sector. The analysis focuses on five Tier-1 institutions namely; First Bank, UBA, GTBank, Access Bank, and Zenith Bank collectively known as the FUGAZ banks. Financial performance is measured using Return on Assets (ROA), while AI adoption is proxied through three major applications: Automated Chatbot Systems (ACBS), Deep Learning Models in credit risk management (DLM), and I-Comply fraud detection systems (I-COMP). Data were obtained from the audited annual reports of the five listed banks and validated through a supplementary survey of bank IT and operations managers. Each AI variable was operationalized as a binary indicator (1 = adopted by the bank in a given year; 0 = otherwise) and aggregated into a balanced panel dataset.
Data were tested for stationarity and cointegration using Levin–Lin–Chu and Pedroni tests. Based on the confirmation of long-run equilibrium relationships, a Panel Fully Modified Ordinary Least Squares (FM-OLS) regression was employed, supported by robustness checks using fixed-effects estimation. The results reveal that ACBS, DLM, and I-COMP each have a positive and statistically significant association with ROA (p < 0.05), with an overall model explanatory power (R²) of 0.71. The Durbin–Watson statistic of 1.82 indicates no serial correlation, and diagnostic tests confirm model adequacy.
The findings imply that AI adoption significantly enhances profitability through improved customer interaction, efficient credit assessment, and proactive fraud prevention. The study contributes to empirical literature on digital transformation in emerging markets and recommends that Nigerian banks deepen their AI integration strategies to strengthen competitiveness and shareholder value in an increasingly data-driven economy.

Artificial intelligence, Banking sector, Panel FM-OLS

1. Abusalma, A. (2021). The effect of implementing artificial intelligence on job performance in commercial banks of Jordan.Management Science Letters , 11(7), 2061-2070. [Google Scholar] [Crossref]

2. Adeyemo, F. S., & Okoronkwo, G. Artificial Intelligence and Operational Efficiency of Deposit Money Banks in Lagos State, Nigeria. [Google Scholar] [Crossref]

3. Advani, Vaishali. 2021. What is Artificial Intelligence? How does AI work, Types and Future of it? October 19. https://www.mygreatlearning.com/blog/what-is-artificial-intelligence/. [Google Scholar] [Crossref]

4. Aishwaryal, S., Akalya, A., &Suresh, K. (2020).Impact and challenges of artificial intelligence in banking. Journal of Information and Computational Science 10(1), 15-48 [Google Scholar] [Crossref]

5. Alzaidi, A. A. (2018). Impact of artificial intelligence on performance of banking industry in Middle East. International Journal of Computer Science and Network Security, 18(10), 140-148. [Google Scholar] [Crossref]

6. Andre, F. (2008).Business mathematics &statistics (6thed.).England: TJ International Birmingham. [Google Scholar] [Crossref]

7. Antonescu, D., Iozsa, D., Antonescu, O., & Fratila, G. (2018). Modeling and simulation of the specific mechanisms used in convertible automobiles. Proceedings of the 4th International Congress of Automotive and Transport Engineering (AMMA 2018). DOI: 10.1007/978-3-319-94409-8_1. [Google Scholar] [Crossref]

8. Avrim, B., (2017). Machine learning theory. Pennsylvania: Elsevier Pittsburgh. [Google Scholar] [Crossref]

9. Borana, J. (2016). Applications of artificial intelligence & associated technologies. In Proceeding of International Conference on Emerging Technologies in Engineering, Biomedical, Management and Science (pp. 64–67). [Google Scholar] [Crossref]

10. Dehee, C & Kyungho L. (2018).An Artificial Intelligence Approach to Financial Fraud Detection under IoT Environment: A Survey and Implementation. CIST, Korea University, https://doi.org/10.1155/2018/5483472 [Google Scholar] [Crossref]

11. Deloitte US. 2021. Artificial Intelligence: Transform the future of banking. Accessed November 3, 2021. https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data. [Google Scholar] [Crossref]

12. Elegunde, A. F., & OSAGIE, R. (2020). Artificial intelligence adoption and employee performance in the Nigerian banking industry. International Journal of Management and Administration, 4(8), 189-205. [Google Scholar] [Crossref]

13. Giovanni, A., Pavel, L., Edgardo, M., Wissam, M., Luis, B., Athanasios, V., Grammatopoulos, & Fabio, Di (2023).The Role of Machine Learning in Cyber security. 8 https://doi.org/10.1145/3545574 [Google Scholar] [Crossref]

14. Iqbal, H., (2020) Machine Learning for Intelligent Data Analysis and Automation in Cyber security: Current and Future Prospects. Annals of Data Science doi.org/10.1007/s40745-022-00444-2 [Google Scholar] [Crossref]

15. Jillian, M., Nooshin, Y., Di, Xu.,& Dmitry, E.(2020). Sequential deep learning for credit risk monitoring with tabular financial data. New York: American Express. [Google Scholar] [Crossref]

16. Keith, D. (2022). A brief history of artificial intelligence. California: Tanya Lewis. [Google Scholar] [Crossref]

17. KPMG (2021).Changing customer, changing priorities: Nigeria banking industry customer experience survey. Retrieved from 2021-kpmg-nigeria-banking-industry-cx-survey-v2.pdf2021 or KPMG Nigeria Banking Industry Customer Experience - KPMG Nigeria (home.kpmg) [Google Scholar] [Crossref]

18. Luka, V., & Lei, Y. (2019).Machine learning and credit risk modeling. England: S&P Global. [Google Scholar] [Crossref]

19. Mahdi, S., Mehran, S., Amin, H., &Aryana, D. (2022).Fraud Detection in Supply Chain with Machine Learning. IFAC Papers Online. ELSEVIER, UK, London. [Google Scholar] [Crossref]

20. Martin, A., Michael, W., & Alexander, B. (2020).AI-based chat bots in customer service and their effects on user compliance. Germany: Springer Inc. [Google Scholar] [Crossref]

21. McCarthy, John. 2007. WHAT IS ARTIFICIAL INTELLIGENCE? Stanford University. [Google Scholar] [Crossref]

22. McKinsey. 2020. AI-bank of the future: Can banks meet the AI challenge? September 19. Accessed November 7, 2021. https://www.mckinsey.com/industries/financial-services/our-insights/ai-bank-of-the-future-can-banks-meet-the-ai-challenge. [Google Scholar] [Crossref]

23. Medium. 2020. The 5 Primary Areas of Robotics. July. Accessed October 29, 2021. https://rancholabs.medium.com/the-5-primary-areas-of-robotics-6aff30f2cf39. [Google Scholar] [Crossref]

24. Mogaji, E., & Nguyen, N.(2021). Managers’ understanding of artificial intelligence in relation to marketing financial services: Insights from a cross-country study. International Journal of Bank Marketing, 2(4), 40-51.DOI: 10.1108/IJBM-09-2021- [Google Scholar] [Crossref]

25. Mor, S., & Gupta, G. (2021). Artificial intelligence and technical efficiency: The case of Indian commercial banks. Strategic Change, 30(3), 235-245. [Google Scholar] [Crossref]

26. Nilsson, Nils. 1998. Introduction Machine Learning. Stanford: Stanford University. [Google Scholar] [Crossref]

27. Prentice, C., Dominique Lopes, S., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management, 29(7), 739-756. [Google Scholar] [Crossref]

28. PWC. 2020. AI Predictions 2021. October https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html. [Google Scholar] [Crossref]

29. PwC. 2020. Artificial Intelligence for reporting: Proof-of-Concept to explore use of AI to improve reporting speed and consistency. Accessed November 7, 2021. https://www.pwc.com/sg/en/consulting/assets/artificial-intelligence-for-reporting.pdf. [Google Scholar] [Crossref]

30. Richman, A. (2019). Artificial intelligence in Nigeria financial sector. Journal of Electronics & Information Engineering, 11(1), 5-15. DOI: 10.6636/IJEIE.201909 11(1).05) [Google Scholar] [Crossref]

31. Stamford, Conn. 2020. Gartner. October 01. https://www.gartner.com/en/newsroom/press-releases/2020-10-01-gartner-survey-revels-66-percent-of-orgnizations-increased-or-did-not-change-ai-investments-since-the-onset-of-covid-19 [Google Scholar] [Crossref]

• Impact of AI Tools on Student Learning
• Repogent: An Autonomous Multi-Agent System for End-To-End Repository Maintenance
• Trust-Centric Federated Learning Using Blockchain-Enabled Smart Contracts: An Analysis of Data Privacy, Model Integrity, and Decentralized AI Governance
• Can AI Care? Rethinking Ethics, Clinical Integrity, and Professional Responsibility in the Digital Age of Mental Healthcare
• AI-Based Smart Irrigation System Using IoT and Machine Learning for Precision Agriculture