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

AI Readiness Digital Forensic Competency Data Governance Fraud Detection Effectiveness Certified Fraud Examiners

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October 26, 2026

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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.