DEVELOPING A MEASUREMENT INSTRUMENT FOR TECHNICAL AND ANALYTICAL SKILLS IN AUDITING FOR ENHANCED FRAUD DETECTION
Bibliographic record
Abstract
This study bridges a significant gap in forensic accounting and fraud detection by establishing a standardized measure for Technical and Analytical Skills (TAS) in external auditing. Despite the acknowledged importance of TAS in fraud detection, the absence of a universally accepted definition and measurement instrument has limited the field's advancement. This research introduces a validated TAS measurement instrument, underpinned by a novel framework that categorizes TAS into six critical dimensions: Substantive Analytical Procedures, Technical Tools and Software, Critical Thinking, Innovation and Solution Implementation, Professional Development, and Quantitative and Statistical Analysis. A structured survey among 360 auditors from international firms in Southern Africa confirmed the instrument's reliability, with Cronbach's alpha values exceeding 0.70 across all dimensions, and supported the distinctiveness of the six-factor structure through confirmatory factor analysis. The instrument's potential to enhance auditing practices and fraud detection capabilities is considerable. It offers a foundation for future research to explore its cross-cultural applicability, predictive validity, and adaptation to technological advancements. This contribution not only provides a robust tool for auditing professionals but also fosters a culture of innovation and continuous learning within the field. Keywords: Technical and Analytical Skills, Forensic Accounting, Fraud Detection, External Auditing, Skill Measurement, Professional Development, Confirmatory Factor Analysis, Auditing Education.
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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 | 0.021 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".