ARTIFICIAL INTELLIGENCE INTERVENTION IN AUDITING AGAINST FUND EMBEZZLEMENT IN THE BANKING SECTOR OF NIGERIA

Sakiru Abiola Lawal; Victor Ajibayo ADEYEYE

doi:10.61397/mfc.v3i1.427

Authors

Sakiru Abiola Lawal Department of Banking and Finance Lagos State University of Science and Technology
Victor Ajibayo ADEYEYE Department of Business Administration Lagos State University of Science and Technology

DOI:

https://doi.org/10.61397/mfc.v3i1.427

Keywords:

Artificial Intelligence, auditing, fund embezzlement, Nigerian banking sector

Abstract

This study investigates the role of Artificial Intelligence (AI) in enhancing internal auditing mechanisms to prevent and detect fund embezzlement within the Nigerian banking sector. The primary objective is to evaluate how AI-driven audit systems contribute to mitigating fraud risks while addressing the organizational and human factors that shape their effectiveness. A qualitative research design was employed, combining semi-structured interviews with internal auditors from selected Nigerian banks and direct observations of AI-enabled audit systems. Data were analyzed thematically using coding techniques, and the findings were interpreted through the lenses of Fraud Triangle Theory, Agency Theory, and Technology Adoption frameworks. Results reveal that AI strengthens audit effectiveness by improving anomaly detection and reinforcing internal controls, thereby narrowing opportunities for fraud. However, the study highlights that pressures and rationalizations driving fraudulent behaviors remain persistent challenges beyond the scope of technology. Additionally, the adoption and effectiveness of AI are influenced by organizational readiness, auditor competence, and cultural attitudes within the banking sector. This research contributes both theoretically and practically by integrating multiple perspectives to explain fraud dynamics in developing economies, emphasizing that technology alone cannot eliminate embezzlement risks without complementary regulatory, cultural, and human resource interventions.

References

Adegbayibi, A. T. (2025). Forensic Accounting and Fraud Management of Listed Deposit Money Banks in Nigeria. Global Journal of Accounting, 11(1), 95–104.

Afolabi, J. (2022). Financial Development, Trade Openness, and Economic Growth in Nigeria (SSRN Scholarly Paper No. 4250141). Social Science Research Network. https://papers.ssrn.com/abstract=4250141

Ajayi, F. A., & Akinrinola, O. (2023). Artificial Intelligence & Internal Audit Quality of Commercial Banks in Nigeria. International Journal Of Management And Economics Invention, 9(4), 2897–2907. https://doi.org/10.47191/ijmei/v9i4.05

Akinadewo, I. S., Oke, O. E., Akinadewo, J. O., & Dagunduro, M. E. (2023). In What Way Does Artificial Intelligence Influences Audit Practice? Empirical Evidence from Southwest, Nigeria. European Journal of Accounting, Auditing and Finance Research, 12(1). https://eajournals.org/ejaafr/vol12-issue-1-2024/in-what-way-does-artificial-intelligence-influences-audit-practice-empirical-evidence-from-southwest-nigeria/

Al-Okaily, M., Alqudah, H., Al-Qudah, A. A., Al-Qadi, N. S., Elrehail, H., & Al-Okaily, A. (2022). Does financial awareness increase the acceptance rate for financial inclusion? An empirical examination in the era of digital transformation. Kybernetes, 52(11), 4876–4896. https://doi.org/10.1108/K-08-2021-0710

Braun, V., & Clarke, V. (2021). Thematic Analysis: A Practical Guide. 1–100.

Chen, C.-H. V., & Chen, Y.-C. (2021). Influence of intellectual capital and integration on operational performance: Big data analytical capability perspectives. Chinese Management Studies, 16(3), 551–570. https://doi.org/10.1108/CMS-02-2021-0037

Cressey, D. R. (1986). Why Managers Commit Fraud*. Australian & New Zealand Journal of Criminology, 19(4), 195–209. https://doi.org/10.1177/000486588601900402

Davis, F. D., & Granić, A. (2024). The Technology Acceptance Model: 30 Years of TAM. Springer International Publishing. https://doi.org/10.1007/978-3-030-45274-2

Erin, O. A., & Olojede, P. (2024). Do nonfinancial reporting practices matter in SDG disclosure? An exploratory study. Meditari Accountancy Research, 32(4), 1398–1422. https://doi.org/10.1108/MEDAR-06-2023-2054

Homer, E. M. (2020). Testing the fraud triangle: A systematic review. Journal of Financial Crime, 27(1), 172–187. https://doi.org/10.1108/JFC-12-2018-0136

Ismaeil, M. K. A. (2024). Harnessing AI for Next-Generation Financial Fraud Detection: A DataDriven Revolution. Journal of Ecohumanism, 3(7), 811–821.

Jensen, M. C., & Meckling, W. H. (2000). THEORY OF THE FIRM: MANAGERIAL BEHAVIOR, AGENCY COSTS AND OWNERSHIP STRUCTURE. Dalam Corporate Governance. Gower.

Kassem, R., & Higson, A. (2012). The new fraud triangle model. Journal of Emerging Trends in Economics and Management Sciences, 3(3), 191–195. https://doi.org/10.10520/EJC132216

Machado, M. R. R., & Gartner, I. R. (2018). The Cressey hypothesis (1953) and an investigation into the occurrence of corporate fraud: An empirical analysis conducted in Brazilian banking institutions. Revista Contabilidade & Finanças, 29, 60–81. https://doi.org/10.1590/1808-057x201803270

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

Okafor, A., Adeleye, B. N., & Adusei, M. (2021). Corporate social responsibility and financial performance: Evidence from U.S tech firms. Journal of Cleaner Production, 292, 126078. https://doi.org/10.1016/j.jclepro.2021.126078

Schuchter, A., & Levi, M. (2016). The Fraud Triangle revisited. Security Journal, 29(2), 107–121. https://doi.org/10.1057/sj.2013.1

Vasarhelyi, E., Sidhu, S. P., Somerville, L., Lanting, B., Naudie, D., & Howard, J. (2022). Static vs Articulating Spacers for Two-Stage Revision Total Knee Arthroplasty: Minimum Five-Year Review. Arthroplasty Today, 13, 171–175. https://doi.org/10.1016/j.artd.2021.10.010