From Forensic Knowledge to Fraud Detection Performance: Mediating Role of Big Data Analytics Skills in the Public Sector
Year: 2025
Type: article
Study design: Observational Study
Abstract: This study examines how forensic accounting knowledge (FAKR) influences fraud detection performance (TPFPD) in the public sector, with big data analytics skills (BDASR) as a mediating factor. Drawing on the Theory of Planned Behaviour and Competency Theory (CT), we surveyed 305 forensic accountants and auditors in Nigeria’s federal institutions. Using Partial Least Squares Structural Equation Modelling (PLS-SEM), outcome reveal that FAKR significantly predicts TPFPD both directly and indirectly through BDASR. Professionals with higher forensic expertise are more likely to develop analytics capabilities, which enhance fraud detection effectiveness in digitized financial management systems like TSA, GIFMIS, and IPPIS. The model demonstrates moderate explanatory power (R² = 0.339) and predictive relevance (Q² = 0.183). Findings underscore the need for governments to integrate analytics training into recruitment and professional development, embed hybrid competencies in curricula, and invest in technology to strengthen fraud prevention. By providing empirical evidence from an emerging economy, this study contributes to accounting information systems and forensic accounting literature while offering practical and policy guidance for enhancing public sector accountability.
Author: Joshua Kadmi Luka
Institution: Universiti Utara Malaysia
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414570504