An Intelligent Accounting-legal Simulation Model for Proactive Resolution of Tax Disputes: Empirical and Comparative Evidence from Egypt
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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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".