AI-Driven Predictive Analytics for Enhancing Corporate Tax Compliance

Abstract

This study investigates the role of artificial intelligence (AI)-driven predictive analytics in enhancing corporate tax compliance through a mixed-method experimental framework that integrates quantitative modeling, qualitative validation, and simulation-based analysis. Using a dataset comprising corporate financial statements, tax filings, and audit outcomes, multiple machine learning models—including logistic regression, random forests, gradient boosting, and neural networks—were developed and tested. Results demonstrate that ensemble methods consistently outperformed traditional techniques, achieving accuracy and AUC values above 90% in detecting potential noncompliance. Interpretability was ensured through SHAP analysis, which identified transfer pricing deviations, effective tax rates, and cash flow discrepancies as critical predictors. Monte Carlo simulations further validated the robustness of these models, showing that predictive analytics can reduce compliance costs by up to 30% while simultaneously improving risk detection. Complementary qualitative findings, drawn from case studies and practitioner insights, highlight that explainable AI enhances transparency and supports trust between corporations and regulators. The integration of these methods reveals not only efficiency gains but also the potential for AI to act as a governance mechanism that promotes fairness and accountability in taxation. The study contributes to the literature by bridging predictive accuracy with ethical oversight, demonstrating that AI systems can transform compliance frameworks when aligned with responsible governance practices. Overall, the findings underscore that AI-driven predictive analytics can strengthen regulatory effectiveness, lower corporate costs, and encourage voluntary compliance, while raising important considerations regarding algorithmic fairness, data governance, and cross-jurisdictional adaptability.