Submit your papersSubmit Now
For Enquiries: [email protected]
IIARD LogoIIARD

Forensic Data Analytics and Anti-Money Laundering: Panacea for Fraud Prevention in Nigeria and United States of America

Chukwuchebem Christian Umahi

Abstract

The study examined the effect of forensic data analytics on anti-money laundering efforts and its effectiveness in preventing financial fraud in Nigeria and the United States of America (U.S.A.). Specifically, the study sought to evaluate the role of forensic data analytics in detecting suspicious financial transactions and to compare its effectiveness in enhancing anti- money laundering measures in the two countries. The study equally compared the effectiveness of forensic data analytics on anti-money laundering efforts between financial institutions in the United States and Nigeria. A survey research design was employed, targeting a population of 200 top-level bankers, with 100 respondents from Nigeria and 100 from the United States of America, selected through a snowball sampling technique. Primary data were collected using a structured electronic questionnaire. The hypotheses were tested using student t-test and Spearman’s ranked order correlation at a 5% significance level to determine the relationships between forensic data analytics, anti-money laundering, and fraud prevention. The findings revealed that: forensic data analytics did not have a significantly stronger effect on anti-money laundering in the United States compared to Nigeria (t = 1.731, p = .085); forensic data analytics positively influenced anti-money laundering in both countries (beta = 0.499, p < .001); forensic data analytics strongly enhanced fraud prevention in both countries (beta = 0.817, p < .001). Therefore, investments in forensic data analytics contribute to the development of more robust monitoring structures, improve the capacity of institutions to respond to complex financial crimes, and reinforce accountability mechanisms. The study recommended that compliance officers and risk management teams should actively incorporate advanced forensic data analytics techniques into their monitoring and reporting processes. By leveraging data-driven insights, these teams can enhance the accuracy of transaction monitoring, improve the identification of suspicious activities, and strengthen overall regulatory compliance.

Keywords

Forensic Data AnalyticsAnti-Money LaunderingFraud Prevention

References

Adekunle, J. J., Onuorah, L., Binuyo, M., & Lartey, T. (2025). Data-driven strategies for early detection of financial fraud in mobile money systems: a case study of Nigeria's Mobile Money Ecosystem. Journal of Advanced Science and Optimization Research, 10(9). Amoako, E. K. W., Boateng, V., Ajay, O., Adukpo, T. K., & Mensah, N. (2025). Exploring the role of machine learning and deep learning in anti-money laundering strategies within US financial industry: A systematic review of implementation, effectiveness, and challenges. Finance & Accounting Research Journal, 7(1), 22-36. Boateng, N. V., Amoako, N. E. K., Ajay, N. O., & Adukpo, N. T. K. (2025). Harnessing Artificial Intelligence for combating money laundering and fraud in the US financial industry: A comprehensive analysis. Finance & Accounting Research Journal, 7(1), 37-49. Chukwuma, O. V., Uzoanigbo, C., Offordile, H. O., Udeh, C., Madukwe, J. U., & Taiwo, B. (2024). The effectiveness of forensic accounting techniques in curbing money laundering in the oil and gas sector. ICPA Journal of Accounting and Finance, 1(3), 1– 6. Daraojimba, R. E., Farayola, O. A., Olatoye, F. O., Mhlongo, N., & Oke, T. T. (2023). Forensic accounting in the digital age: a US perspective: scrutinizing methods and challenges in digital financial fraud prevention. Finance & Accounting Research Journal, 5(11), 342-360. 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. Elumilade, O. O., Ogundeji, I. A., Achumie, G. O., Omokhoa, H. E., & Omowole, B. M. (2021). Enhancing fraud detection and forensic auditing through data-driven techniques for financial integrity and security. Journal of Advanced Education and Sciences, 1(2), 55-63. Flores, D. A., Angelopoulou, O., & Self, R. J. (2012, September). Combining digital forensic practices and database analysis as an anti-money laundering strategy for financial institutions. In 2012 Third International Conference on Emerging Intelligent Data and Web Technologies (pp. 218-224). IEEE. Gilmour, P. M. (2023). Reexamining the anti-money-laundering framework: a legal critique and new approach to combating money laundering. Journal of Financial Crime, 30(1), 35-47. Guptam, M. D. (2024). Corporate governance and the prevention of fraud-an analytical study. International Journal of Multidisciplinary Research and Explorer, 4(2), 38-53. Hossain, M. Z. (2023). Emerging trends in forensic accounting: Data analytics, cyber forensic accounting, cryptocurrencies, and blockchain technology for fraud investigation and prevention. Cyber Forensic Accounting, Cryptocurrencies, and Blockchain Technology for Fraud Investigation and Prevention (May 16, 2023). Huber, W. D. (2017). Forensic accounting, fraud theory, and the end of the fraud triangle. Journal of Theoretical Accounting Research, 12(2). Ikumapayi, O. J., & Ayankoya, B. B. (2025). AI powered forensic accounting: Leveraging machine learning for real-time fraud detection and prevention. International Journal of Research Publication and Reviews, 6(2), 236–250. Ikwuo, A. K., Nworie, G. O., & Moedu, V. O. (2024). Implementation of AI-driven automation: A game-changer in accounting research. International Journal of Financial, Accounting, and Management, 6(3), 385-398. P-ISSN 2695-186X Jofre, M., & Gerlach, R. (2018). Fighting accounting fraud through forensic data analytics. arXiv preprint arXiv:1805.02840. Kukman, T., & Gričar, S. (2025). Blockchain for quality: Advancing security, efficiency, and transparency in financial systems. FinTech, 4(1), 7. Maidarasu, A. U., Adebisi, J. F., & Mlanga, S. (2025). Effect of forensic data analysis on fraud detection of listed firms in Nigeria. Management Journal of Advanced Research, 5(1), 58–70. Mangala, D., & Kumari, P. (2015). Corporate fraud prevention and detection: Revisiting the literature. Journal of Commerce & Accounting Research, 4(1), 35-45. Meshack, A., Orji, M. G., & Ogechukwu, N. G. (2022). Poverty Reduction in Awka Metropolis: The Role of Entrepreneurship Productivity. Economit Journal: Scientific Journal of Accountancy, Management and Finance, 2(4), 244-262. Mugarura, N. (2020). Anti-money laundering law and policy as a double edged sword. Journal of Money Laundering Control, 23(4), 899-912. Musyoki, K. M. (2023). Internal control systems and their role in financial fraud prevention in Kenya. African Journal of Commercial Studies, 3(3), 173-180. Odia, J. O., & Akpata, O. T. (2021). Role of data science and data analytics in forensic accounting and fraud detection. In Handbook of research on engineering, business, and healthcare applications of data science and analytics (pp. 203-227). IGI Global Scientific Publishing. Ofoeda, I., Agbloyor, E., & Abor, J. Y. (2024). Financial sector development, anti-money laundering regulations and economic growth. International Journal of Emerging Markets, 19(1), 191-210. Okafor, C., & Bello, A. (2024). Exploring the Role of Predictive Analytics in Enhancing Anti- Money Laundering Surveillance and Transaction Monitoring Systems. International Journal of Data Science, Big Data Analytics, and Predictive Modeling, 14(8), 1-16. Onochie, C. C., & Nworie, G. O. (2025). Financial stress among female entrepreneurs in Nigeria: The buffering effect of financial literacy. Journal of Governance and Accountability Studies, 5(1), 47-62. Rahayu, S., Yudi, Sofyan, Rahayu, & Jumaili, S. (2025). Enhancing good governance and fraud prevention for school performance. Cogent Business & Management, 12(1), 2534097. Sánchez-Aguayo, M., Urquiza-Aguiar, L., & Estrada-Jiménez, J. (2021). Fraud detection using the fraud triangle theory and data mining techniques: A literature review. Computers, 10(10), 121. Siddiqui, R. A. (2023). An Assessment of Current Anti-Money Laundering Strategies Among Financial Institutions in the United States (Doctoral dissertation, Northcentral University). Sotande, E. O. (2025). Anti-Money Laundering Compliance Challenges Within Nigerian Financial Institutions. In Global Anti-Money Laundering Regulation (pp. 136-160). Routledge. Tickner, P., & Button, M. (2021). Deconstructing the origins of Cressey’s Fraud Triangle. Journal of Financial Crime, 28(3), 722-731. Usman, N., Usman, S., Khan, F., Jan, M. A., Sajid, A., Alazab, M., & Watters, P. (2021). Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics. Future Generation Computer Systems, 118, 124-141.

More Articles from IIARD INTERNATIONAL JOURNAL OF ECONOMICS AND BUSINESS MANAGEMENT