International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Tax compliance constitutes a substantial challenge for national revenues and public services worldwide, particularly in a digital economy that enables rapid international transactions. Artificial intelligence (AI) can enhance automated risk scoring and tax auditing capabilities by bridging the gap between the rapid development of machine-learning methods and the pressing operational needs of tax administrations. The applicability of AI-based risk scoring and auditing methods in the tax domain has thus far remained largely unexplored in the literature, as has the evaluation and validation of the resulting systems. Motivation, design, methods, and specific foundations (data-driven evidence, risk-scoring models, and automated auditing techniques) are presented in these sections, along with considerations of data governance, privacy, and ethics.
Evidence drawn from knowledge engineering and computational taxonomy outlines the data requirements, provenance, and quality for reliable AI applications for tax compliance, providing a foundation for subsequent sections on risk-scoring models, data-driven evidence, and automated tax auditing. Risk-scoring models identify the relevance of explainability, novelty detection, and machine-generated human-readable components, supported by privacy-preserving techniques and algorithmic transparency. Two key approaches are identified: supervised learning generates predictions for tax-relevant domains, whereas unsupervised and semi-supervised methods support hierarchical anomaly detection. These directions together address the completeness of AI auditing systems, complementing research on planning, knowledge representation, and evaluation of audit systems.

Keywords: Automated Tax Auditing. Artificial Intelligence; Classification and Regression; Data-Driven Audit Planning; Data Mining Technologies; Document Analysis; Natural Language Processing; Risk Scoring Models. Auditing Apparatus. Governance Framework.

Downloads: | DOI: 10.17148/ IJARCCE.2023.121229