Leveraging Forensic Data Analytics to Curb Electronic Tax Evasion: Evidence from the Federal Inland Revenue Service, Nigeria

Aim: This study examines whether adoption and effective use of forensic data collection and analytic tools by tax auditors deters and reduces electronic tax evasion within the Federal Inland Revenue Service (FIRS) in Abuja, Nigeria. It assesses auditors' perceptions, forensic knowledge, and the association between forensic tool adoption and reported levels of electronic tax evasion. Methodology: A cross sectional survey of 332 FIRS officers from five Abuja offices collected demographics, a five item Forensic Tools Scale (FOTLS), measures of audit rigour and impact, and a scenario based forensic knowledge assessment. Reliability was confirmed with Cronbach's alpha greater than 0.84. Data were analysed with descriptive statistics, chi square tests, and ordinary least squares regression. Diagnostics included tests for normality, multicollinearity, and residual behavior; robust standard errors and log transformations were applied where appropriate. The primary model regressed reported evasion scores on FOTLS, audit rigour, impact measures and demographic controls. Results: Respondents were predominantly aged 30 to 39 and mostly university educated. Mean responses indicated strong agreement that computer assisted audit techniques, Benford's Law analysis and digital forensics improve evidence quality and case outcomes. Regression analysis showed a significant positive association between forensic tool adoption and higher compliance scores; the FOTLS coefficient was 0.122 (t = 15.06, p < 0.001) and the model explained a large share of variance (R² ≈ 0.945). Diagnostics indicated acceptable normality, low multicollinearity (VIF < 2) and well behaved residuals. Conclusion: Among surveyed FIRS personnel, greater adoption and capability in forensic data collection and analytics correlates with lower reported electronic tax evasion. The findings support investment in forensic technologies, targeted auditor training and integration of advanced analytics into tax administration to strengthen detection and deterrence of tax fraud. Policy implementation should be monitored and evaluated for sustained impact. This abstract is based on the uploaded manuscript