The Influence of AI Readiness, Digital Forensic Competency, and Data Governance Practices on Fraud Detection Effectiveness among Certified Fraud Examiners in Zimbabwe: A Case of ACFE Zimbabwe Chapter 128
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The digitalization of financial activities and the increasing complexity of technology-enabled fraud have required organizations to strengthen their fraud detection capabilities through technological adoption, professional expertise, and effective data governance. This study aimed to examine the influence of artificial intelligence (AI) readiness, digital forensic competency, and data governance practices on fraud detection effectiveness among Certified Fraud Examiners (CFEs) affiliated with the Association of Certified Fraud Examiners (ACFE) Zimbabwe Chapter 128. A quantitative approach with a cross-sectional survey design was employed. Data were collected from 117 respondents through an online questionnaire and analyzed using descriptive statistics and multiple linear regression in IBM SPSS Statistics version 26. The results indicated that AI readiness had a positive but statistically insignificant association with fraud detection effectiveness (? = 0.093, p = 0.186). Digital forensic competency demonstrated a positive and statistically significant association (? = 0.252, p < 0.001), while data governance practices exhibited the strongest positive association (? = 0.488, p < 0.001). Collectively, the three predictors explained 58.9% of the variance in fraud detection effectiveness (R² = 0.589, F = 54.073, p < 0.001). These findings suggested that technological readiness alone was insufficient to enhance fraud detection effectiveness without adequate investigative competencies and robust data governance practices. Accordingly, organizations should adopt an integrated strategy that combines technological advancement, continuous digital forensic competency development, and strengthened data governance frameworks to improve fraud prevention and detection capabilities.
Ahmed, M., Mahmood, A. N., & Islam, M. R. (2016). A survey of anomaly detection techniques in financial domain. Future Generation Computer Systems, 55, 278–288. https://doi.org/10.1016/j.future.2015.01.001
Andaru, F. M., & Utami, B. P. (2024). The convergence of 5.0 and forensic accounting: Leveraging blockchains for effective corruption prevention in Indonesia. Asia Pacific Fraud Journal, 9(1). https://doi.org/10.21532/apfjournal.v9i1.338
Association of Certified Fraud Examiners. (2024). Occupational fraud 2024: A report to the nations.
Babbie, E. R. (2021). The practice of social research (15th ed.). Cengage Learning.
Bryman, A. (2022). Social research methods (6th ed.). Oxford University Press.
Chang, V., Di Stefano, A., Sun, Z., & Fortino, G. (2022). Digital payment fraud detection methods in digital ages and Industry 4.0. Computers and Electrical Engineering, 100, 107734.
Committee of Sponsoring Organizations of the Treadway Commission. (2022). COSO framework.
Cressey, D. R. (1953). Other people’s money: A study in the social psychology of embezzlement. Free Press.
Creswell, J. W., & Creswell, J. D. (2022). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE Publications.
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.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., . . . Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Field, A. (2024). Discovering statistics using IBM SPSS statistics (6th ed.). SAGE Publications.
Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499–510. https://doi.org/10.1207/s15327906mbr2603_7
Hair, J. F., Page, M., & Brunsveld, N. (2021). Essentials of business research methods (5th ed.). Routledge.
Ikhsan, W. M., Ednoer, E. H., Kridantika, W. S., & Firmansyah, A. (2022). Fraud detection automation through data analytics and artificial intelligence. Riset, 4(2). https://doi.org/10.37641/riset.v4i2.166
Kranacher, M. J., Riley, R. A., & Wells, J. T. (2022). Forensic accounting and fraud examination (3rd ed.). Wiley.
Mabior, M. M., & Wanyama, K. W. (2024). Role of forensic investigations in reduction of financial fraud among commercial banks in South Sudan. International Journal of Finance and Accounting, 3(1), 1–14. https://doi.org/10.37284/ijfa.3.1.1845
Mangala, D., & Kumari, P. (2017). Auditors’ perceptions of the effectiveness of fraud prevention and detection methods. Indian Journal of Corporate Governance. https://doi.org/10.1177/0974686217738683
Mashitoh, H., Daurrohmah, E. W., Inan, D. I., & Firman, A. (2023). The analysis of fraud in perspective of fraud pentagon theory: An empirical study in Indonesia. JATI: Jurnal Akuntansi Terapan Indonesia, 6(2), 107–120. https://doi.org/10.18196/jati.v6i2.18765
Murinda, G. T., & Chiwariro, R. (2023). Impact of forensic accounting on fraud detection in Zimbabwe’s public sector expenditure cycle. International Journal for Research in Applied Science and Engineering Technology. https://doi.org/10.22214/ijraset.2023.57067
Mvunabandi, J. D. (2023). Modelling proactive fraud risk management practices in South Africa’s non-governmental organisations. Journal of Accounting and Finance in Emerging Economies. https://doi.org/10.26710/jafee.v9i3.2618
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006
Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58. https://doi.org/10.1016/j.accinf.2018.03.001
Siahaan, S., Simanjuntak, A., Simanjuntak, W., Silalahi, M., & Tarigan, D. (2023). Information systems and capabilities auditors investigative to effectiveness implementation audit procedure in proving fraud: Case study of the Regional Inspectorate of Karo Regency. Jurnal Ilmiah Accusi, 5(2), 128–145. https://doi.org/10.36985/jia.v5i2.789
Sukardi, S. (2022). Reconstruction of financial crime investigation methods in law enforcement in the era of the Industrial Revolution 4.0. Unnes Law Journal, 8(1). https://doi.org/10.15294/ulj.v8i1.53059
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Wolfe, D. T., & Hermanson, D. R. (2004). The fraud diamond: Considering the four elements of fraud. The CPA Journal, 74(12), 38–42.
Zhu, X., Ao, X., Qin, Z., Chang, Y., Liu, Y., He, Q., & Li, J. (2021). Intelligent financial fraud detection practices in post-pandemic era. The Innovation, 2(4).
Copyright (c) 2026 Joshua Foma, Tafadzwa Chimbare, Brian Marambo, Trust Chidadza, Rumbidzai Chibvongodze, Adiel Chikwekwete

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