Forensic Accounting Technologies and Occupational Fraud Mitigation in the Nigerian Maritime Sector
Article
This study examined how forensic accounting technologies influence the effectiveness of occupational fraud detection in the Nigerian maritime sector. Specifically, it investigated the effects of Artificial Intelligence-driven forensic analytics, blockchain-based transaction traceability, cybersecurity resilience, regulatory and audit enforcement, and organisational technology readiness on occupational fraud detection effectiveness. A quantitative research approach, grounded in the positivist paradigm, was adopted, using a cross-sectional survey design. The study conducted a census of 118 maritime organisations regulated by the Nigerian Maritime Administration and Safety Agency (NIMASA) and the Nigerian Shippers' Council, yielding 100 valid responses via a structured questionnaire. Data were analysed using descriptive statistics, Pearson Product-Moment Correlation, and multiple linear regression in SPSS at a 5% significance level. The findings revealed that all five dimensions of forensic accounting technologies had positive and statistically significant effects on occupational fraud detection effectiveness. Artificial intelligence-driven forensic analytics emerged as the strongest predictor (β = 0.391, p < 0.001), followed by blockchain-based transaction traceability, cybersecurity resilience, regulatory and audit enforcement, and organisational technology readiness. The regression model explained 63.0% of the variation in occupational fraud detection effectiveness (R² = 0.630). The study concludes that the integrated adoption of advanced forensic accounting technologies substantially strengthens fraud detection, transparency, accountability, and organisational governance within the Nigerian maritime sector. The study contributes to knowledge by integrating the Fraud Diamond Theory and Agency Theory into a unified, technology-enabled framework and provides empirical evidence to support digital anti-fraud policies and governance reforms across the Nigerian maritime industry.
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References
-
Abdullahi, R. B., & Mansor, N. (2018). Fraud prevention initiatives in the Nigerian public sector. Journal of Financial Crime, 25(2), 527–544. https://doi.org/10.1108/jfc-02-2015-0008
-
Abrahams, T. O., Ewuga, S. K., Kaggwa, S., Uwaoma, P. U., Hassan, A. O., & Dawodu, S. O. (2024). Mastering compliance: A comprehensive review of regulatory frameworks in accounting and cybersecurity. Computer Science & IT Research Journal, 5(1), 120–140. https://doi.org/10.51594/csitrj.v5i1.709
-
Adepoju, O. O. (2024). Analysis of constraints against efficiency of seaport-hinterland logistics in Nigeria. Periodica Polytechnica Transportation Engineering, 52(2), 199–208. https://doi.org/10.3311/pptr.23287
-
Ali, A., Razak, S. A., Othman, S., Eisa, T., Al-Dhaqm, A., Nasser, M., Elhassan, T., Elshafie, H., & Saif, A. (2022). Financial fraud detection based on machine learning: A systematic literature review. Applied Sciences, 12(19), 9637. https://doi.org/10.3390/app12199637
-
Amaka Anagor-Ewuzie. (2023, August 30). Job creation: NIMASA puts 11,956 Nigerian seafarers on Cabotage vessels. BusinessDay. https://businessday.ng/maritime/article/job-creation-nimasa-puts-11956-nigerian-seafarers-on-cabotage-vessels/
-
Aros, L. H., Molano, L. X. B., Gutierrez-Portela, F., Hernandez, J. J. M., & Barrero, M. S. R. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11, 1130. https://doi.org/10.1057/ s41599-024-03606-0
-
Bonrath, A., & Eulerich, M. (2024). Internal auditing’s role in preventing and detecting fraud: An empirical analysis. International Journal of Auditing, 28(4), 615-631. https://doi.org/10.1111/ijau.12342
-
Cheng, D., Zou, Y., Xiang, S., & Jiang, C. (2024). Graph neural networks for financial fraud detection: A review. Frontiers of Computer Science, 19, 1-17. https://doi.org/10.1007/s11704-024-40474-y
-
Daraojimba, R. E., Farayola, O. A., Olatoye, F. O., Mhlongo, N., & Oke, T. T. (2023). Forensic accounting in the digital age: A U.S. perspective: Scrutinizing methods and challenges in digital financial fraud prevention. Finance & Accounting Research Journal, 5(11), 342–360. https://doi.org/10.51594/farj.v5i11.614
-
Díaz-Arancibia, J., Hochstetter-Diez, J., Bustamante-Mora, A., Sepúlveda-Cuevas, S., Albayay, I., & Arango-López, J. (2024). Navigating digital transformation and technology adoption: A literature review from small and medium-sized enterprises in developing countries. Sustainability. 16(14), 5946. https://doi.org/10.3390/su16145946
-
Firdaus, R., Xue, Y., Gang, L., & Ali, M. S. e. (2022). Artificial intelligence and human psychology in online transaction fraud. Frontiers in Psychology, 13, 947234–947234. https://doi.org/10.3389/fpsyg.2022.947234 Gao, H., Kou, G., Liang, H., Zhang, H., Chao, X., Li, C., & Dong, Y. (2024). Machine learning in business and finance: A literature review and research opportunities. Financial Innovation, 10(86), 1-35. https://doi.org/10.1186/s40854-024-00629-z
-
Han, H., Shiwakoti, R., Jarvis, R., Mordi, C., & Botchie, D. (2023). Accounting and auditing with blockchain technology and artificial intelligence: A literature review. International Journal of Accounting Information System, 48, 100598. https://doi.org/10.1016/j.accinf.2022.100598
-
Iwuoha, V. C., Okafor, N. I., & Ifeadike, E. (2022). State regulation of Nigeria’s maritime ports: Exploring the impact of port concession on both the regulator and the operators. Politics & Policy, 50(5), 1032–1052. https://doi.org/10.1111/polp.12495
-
Lokanan, M., & Maddhesia, V. (2024). Supply chain fraud prediction with machine learning and artificial intelligence. International Journal of Production Research, 63, 286–313. https://doi.org/10.1080/00207543.2024.2361434
-
Luka, J. K., Muse, O., Popoola, J., & Abidin, S. (2025). From forensic knowledge to fraud detection performance: Mediating role of big data analytics skills in the public sector. International Journal of Applied Mathematics, 38(4s),880-902. https://doi.org/10.12732/ijam.v38i4s.277
-
Nelson, A., Lucky, O., Bukola, A. U., & Afrogha, O. (2025). Forensic accounting practice and fraud management in Nigeria public sector entities. Journal of Information Systems Engineering and Management, 10(48s), 1239-1255. https://doi.org/10.52783/jisem.v10i48s.9754
-
Nigerian Maritime Administration and Safety Agency. (2025). List of registered and licensed shipyards. https://nimasa.gov.ng/nimasa-accredits-27-registered-shipyards-for-operation-in-nigeria/
-
Odeyemi, O., Ibeh, C. V., Mhlongo, N. Z., Asuzu, O. F., Awonuga, K. F., & Olatoye, F. O. (2024). Forensic accounting and fraud detection: A review of techniques in the digital age. Finance & Accounting Research Journal, 6(2), 202–214. https://doi.org/10.51594/farj.v6i2.788
-
Qader, K., & Çek, K. (2024). Influence of blockchain and artificial intelligence on audit quality: Evidence from Turkey. Heliyon, 10,(9), e3016. https://doi.org/10.1016/j.heliyon.2024.e30166
-
Razali, F. M., Sulaiman, N., Manan, D. I. A., & Said, J. (2025). Sustainability of audit profession in digital technology era: The role of competencies and digital technology capabilities to detect fraud risk. SAGE Open, 15,(1), 1-12. https://doi.org/10.1177/21582440241304974
-
Saeed, S., Altamimi, S., Alkayyal, N., Alshehri, E., & Alabbad, D. (2023). Digital transformation and cybersecurity challenges for businesses resilience: Issues and recommendations. Sensors (Basel, Switzerland), 23(15), 6666. https://doi.org/10.3390/s23156666
-
Said, J., Alam, Md. M., Ramli, M., & Rafidi, M. (2017). Integrating ethical values into fraud triangle theory in assessing employee fraud: Evidence from the Malaysian banking industry. Journal of International Studies, 10(2), 170–184. https://doi.org/10.14254/2071-8330.2017/10-2/13
-
Santos, E. S. D., Santos, M. M. D., Castro, M., & Carvalho, J. T. (2025). Detection of fraud in public procurement using data-driven methods: A systematic mapping study. EPJ Data Science, 14(52), 1-46. https://doi.org/10.1140/epjds/s13688-025-00569-3
-
Tijjani, H., Ibrahim A. S., Ardo, A. M., & Yusuf, A. U. (2023). The impact of forensicaccounting on tax payer attitude and compliance towards tax evasion within SMEs in the North East Nigeria. Journal of Business Management and Accounting, 13(1) January, 79-105. https://doi.org/10.32890/jbma2023.13.1.4
-
Umar, I., Samsudin, R. S., & Mohamed, M. (2016). The influence of institutional and contingency factors on the adoption of forensic accounting by anti-corruption agencies: A proposed framework. Journal of Business Management and Accounting, 6(2), 1–10.