The Effect of Forensic Audit Services on Tax Fraud in South-South, Nigeria
Bibliographic record
Abstract
The need to address tax fraud has increasingly been attracted to the state authorities’ administrators, decision-makers, scholars, and investigators in Nigeria. Although various efforts have been made to alleviate it for effective revenue generation, mobilization through taxation is still low. Consequently, this study examined Forensic Audit Services and Tax Fraud in South-South Nigeria. To this end, a cross-sectional research design with a survey research strategy was used. Copies of questionnaire, reflecting the research questions, were distributed to a sample size of 228 staff in the Nigerian Federal Inland Revenue Service in the South-South States with a target population (of 530) and a sampling error of 5% at a 95% confidence interval. To assess the study's hypotheses, the Robust Least Squares Estimation technique was used. Findings revealed that Forensic Audit Investigation Services disaggregated into Background Investigation, Investigative Interview, and Analytical Procedures exert a negative and significant effect on Tax Fraud. It was also revealed that the explanatory power of Litigation Support Services in the form of Pre-trial Support and Expert Witnessing negatively and significantly affected Tax Fraud. The implications of these findings suggest that forensic audit services mitigate the occurrence of tax fraud in Nigeria, thereby improving compliance and tax revenue generation. On the premise of the foregoing, we recommend that tax investigating agencies, such as the federal Inland Revenue Service and its counterparts in the states (all in Nigeria) should employ background investigation techniques including surveillance, undercover operations and database searches as a routine procedure to proactively search for indicators of fraud. Besides, tax officials should be well trained on the usefulness and application of analytical procedures, ranging from simple ratio analysis, data mining techniques, Bedford’s Law, and Beneish model during an investigation, to help in their audit efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".