Artificial Intelligence (AI)-Driven Fraud Detection in Commercial Banking: Outcomes From Central Bank Of Nigeria (CBN) Regulatory Sandbox in Global Fintech Context Including Us Federal Reserve Pilots
DOI:
https://doi.org/10.61424/5ff9ce53Keywords:
Commercial Banking, Artificial Intelligence, Fraud DetectionAbstract
This is an empirical study on the results of the use of Artificial Intelligence (AI)-based Fraud detection systems in the Central Bank of Nigeria (CBN) Regulatory Sandbox. The study use convergent parallel mixed methods design, with 128 professionals from commercial banks and fintech firms being interviewed. The study adopted a convergent parallel mixed methods design with the professionals from commercial banks and fintech firms interviewed, totaling 128 to be observed. The quantitative analysis results were impressive, finding a 41.8% drop in fraud losses, a 92.3% increase in detection accuracy (from 74.6% to 92.3%) and average detection time reducing by 77.7%. The most significant predictor of success was participation in Sandbox. Learning from the US Federal Reserve pilots, the findings of the comparison shows the ‘agility advantage’ that the CBN sandbox offers coupled with the need to develop better model governance. The study presents innovative and original evidence of the effectiveness of Regulated AI innovation for emerging markets, along with policy recommendations for scaling the adoption and responsible use of the technology in commercial banking. 2022 is shaping up to be a pivotal year for financial technology (fintech), defined by a blend of cutting-edge innovations, burgeoning sectors, and significant regulatory shifts.It appears that 2022 could be a transformative year for financial technology (fintech), defined by a mix of exciting advancements, emerging markets, and meaningful regulatory changes.
References
Adedire-Ampitan, A. A., Logunleko, S. D., Amusa, B. O., & Onifade, H. O. (2024). Artificial Intelligence and Risk management of deposit money banks in Nigeria: empirical evidence from Guaranty Trust Bank. Umyu journal of accounting and finance research, 7(1), 178-193. Https://doi.org/10.61143/umyu-jafr.7(1)2024.012
Agumalu, S. A. (2024). Algorithmic accountability in ai driven public systems: fairness in allocation, workforce and safety. International journal of computer technology and electronics communication, 7(5), 9516-9526. Https://doi.org/10.15680/ijctece.2024.0705011
Akinnagbe, O., & Taiwo, a. (2025). The impact of machine learning on fraud detection in digital payment. Asian journal of science, technology, engineering, and art, 3, 191-209. Https://doi.org/10.58578/ajstea.v3i2.4900
Anzor, E. D., Okolie, J. I., Udeh, I. E., Mbah, P. C., Onyeka-Udeh, v., Obayi, P. M., ... & Eze, j. O. (2024). Effect of artificial intelligence (ai) on fraud detection in deposits money banks in Southeast, Nigeria. Iosr journal of humanities and social science, 29(11), 15-27.Https://doi.org/10.9790/0837-2911091527
Ayobami Gabriel Olanrewaju, Oluwaseun John Bamigboye, Ayokunmi Paul Sodamola, Idara Sebastian Bassey, Gideon Olugbenga Toriola, Augustine Udoka Obu. (2024). Ai-powered fraud detection in financial transactions: enhancing real-time risk management. Isrg journal of economics, business & management (isrgjebm), ii(iv), 209–228.Https://doi.org/10.5281/zenodo.17181455
Ayodeji, I. A. (2024). Fraud detection and prevention in the Nigerian financial industry (doctoral dissertation, Walden University). Https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=18284&context=dissertations
Bao, Y., Hilary, G., & Ke, b. (2022). Artificial intelligence and fraud detection. In innovative technology at the interface of finance and operations: volume I (pp. 223-247). Cham: springer international publishing. Https://doi.org/10.1007/978-3-030-75729-8_8
Bao, Y., Hilary, G., Ke, b. (2022). Artificial intelligence and fraud detection. In: Babich, v., Birge, J.R., Hilary, G. (eds) innovative technology at the interface of finance and operations. Springer series in supply chain management, vol 11. Springer, cham. Https://doi.org/10.1007/978-3-030-75729-8_8
Bello, O. A., Folorunso, A., Onwuchekwa, J., Ejiofor, O. E., Budale, F. Z., & Egwuonwu, M. N. (2023). Analysing the impact of advanced analytics on fraud detection: a machine learning perspective. European journal of computer science and information technology, 11(6), 103-126. Https://doi.org/10.37745/ejcsit.2013/vol11n6103126
Bello, O. A., Ogundipe, A., Mohammed, D., Adebola, F., & Alonge, O. A. (2023). Ai-driven approaches for real-time fraud detection in us financial transactions: challenges and opportunities. European journal of computer science and information technology, 11(6), 84-102. Https://doi.org/10.37745/ejcsit.2013/vol11n684102
Ejiofor, o. E. (2023). A comprehensive framework for strengthening USA financial cybersecurity: integrating machine learning and ai in fraud detection systems. European journal of computer science and information technology, 11(6), 62-83. Https://doi.org/10.37745/ejcsit.2013/vol11n66283
Fernández, A. (2019). Artificial intelligence in financial services. Banco de Espana article, 3, 19. Https://dx.doi.org/10.2139/ssrn.3366846
Harry, O. O., Ighoakpo, E. A., Anyanwu, l., Obande, B. O., Ukaoha, K. C., Izevbuwa, O. G., ... & Arenvbaguehita, O. D. (2025). The impact of Artificial Intelligence (ai) on fraud detection in banks in Nigeria. Gas journal of engineering and technology (gasjet), 2(9), 26-35. Https://doi.org/10.5281/zenodo.17208854
Jejeniwa, T. O., & Owolabi, O. S. (2025). Leveraging hybrid ai for real-time fraud detection: a case study on the efficacy of graph neural networks and anomaly detection in Nigerian fintechs. Journal of digital security and forensics, 2(2), 140–146. Https://doi.org/10.29121/digisecforensics.v2.i2.2025.60
John, S. A., Shonubi,. J. A., Azuikpe, P. F., & Ologun, v. O. (2025). Adoption of ai-driven fraud detection system in the Nigerian banking sector: an analysis of cost, compliance, and competency. Https://doi.org/10.48550/arxiv.2511.00061
Musa, S. J., Success, B. E., Ibrahim, K. M., Success, D. U. & Yusuf, I. (2025). Effect of artificial intelligence (ai) on fraud prevention of listed deposit money banks in Nigeria. Mrs journal of multidisciplinary research and studies, 2(9),1-10. Https://mrspublisher.com/assets/articles/1756710192.pdf
Odufisan, O. I., Abhulimen, O. V., & Ogunti, E. O. (2025). Harnessing artificial intelligence and machine learning for fraud detection and prevention in Nigeria. Journal of economic criminology, 7, 100127. Https://doi.org/10.1016/j.jeconc.2025.100127
Oluwadare, O. E., Adekanmbi, J. A., & Omodara, B. E. (2025). Artificial intelligence and fraud prevention in Nigerian deposit money banks (dmbs). Acta Universitatis Danubius. Economica, 21(3), 128-150. Https://dj.univ-danubius.ro/index.php/audoe/article/view/3404
Osita Amaugo, Abraham Ofosu-Boateng (2025). Impact of ai-driven solutions in financial technology on service delivery: a study of fraud detection, credit risk assessment, and personalized advisory systems in insurance firms in Lagos state. 9(15), https://doi.org/10.47772/ijriss.2025.915ec00752
Patel, M., & Turner, I. (2025). Evaluating Nigeria’s regulatory sandbox as a tool for managing emerging fintech risks. Https://www.researchgate.net/profile/stephen-eteng/publication/397730455_evaluating_nigeria's_regulatory_sandbox_as_a_tool_for_managing_emerging_fintech_risks/links/691cd14bde81430982722155/evaluating-nigerias-regulatory-sandbox-as-a-tool-for-managing-emerging-fintech-risks.pdf
Sandbox/fintech. (2025). Regulatory oversight in Nigeria’s payment industry: legal gaps, enforcement trends and the innovation-compliance dilemma. Https://manifieldsolicitors.com/wp-content/uploads/2025/11/regulatory-oversight-in-nigerias-payment-industry-legal-gaps-enforcement-trends-and-the-innovation-compliance-dilemma_.pdf
Yaseen, H., & Al-Amarneh, a. A. (2025). Adoption of artificial intelligence-driven fraud detection in banking: the role of trust, transparency, and fairness perception in financial institutions in the United Arab Emirates and Qatar. Journal of risk and financial management, 18(4), 217. Https://doi.org/10.3390/jrfm18040217
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