An Intelligent Accounting-legal Simulation Model for Proactive Resolution of Tax Disputes: Empirical and Comparative Evidence from Egypt
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study develops a smart accounting–legal reform model to prevent tax disputes in Egypt by integrating high-quality accounting information, digital audit trails, and simulation-based decision support. A mixed-methods design combines a structured survey of taxpayers, CPAs, and tax officers (n≈280), semi-structured interviews, and a multi-agent simulation calibrated to sectoral risk patterns. The empirical results show that weak documentation and fragmented IT systems are the primary drivers of recurring disputes; by contrast, e-filing/e-audit and early mediation shorten resolution time and reduce escalation. The simulation forecasts that embedding AI-enabled risk scoring and CPA-facilitated pre-assessment reconciliation can lower dispute frequency by 25–30% over five years, while cutting administrative costs relative to litigation. Comparative benchmarks (UK ADR, Canada digital compliance audits, Australia independent pre-litigation review) corroborate the preventive governance approach and inform implementation priorities for Egypt. The paper contributes theoretically by linking accounting information quality, agency incentives, and preventive governance within a simulation-driven framework; and practically by offering an actionable roadmap-digital mediation platform, SME documentation standards, targeted training, and sector-focused pilots-to institutionalize proactive dispute resolution. Overall, the findings demonstrate that sustainable reform depends less on temporary settlement laws and more on accounting transparency, intelligent analytics, and trust-building procedures embedded in everyday administration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it