Architecting Intelligent Tax Automation: Research Innovations in Machine Learning for Global Compliance - Vedashree Kedar Karandikar (International Journal of Computational and Experimental Science and Engineering, 2026)
The global indirect tax compliance of large-scale digital commerce platforms has become a complex, high-stakes systems problem due to jurisdictional fragmentation, regular change of regulations, and the high pace of expansion of heterogeneous product catalogs. Rule-based tax engines, although auditable and deterministic, fail to scale in such a situation because their authoring processes are fragile, maintenance is expensive, and their semantic knowledge of product data is limited. This article provides a detailed design of an intelligent tax automation system that is based on machine learning-based item-to-tax prediction services, supported by confidence-aware orchestration, human-in-the-loop protection, and explainability features that are appropriate in regulated financial settings. The suggested framework uses transformer-based language models that are trained on large-scale and multilingual commerce data to predict tax classifications directly based on item titles, descriptions, and structured taxonomy cues. Instead of using fixed mappings, the system is trained on semantic associations between product representations and jurisdiction-specific tax treatments, allowing it to correctly process long-tail, ambiguous, and newly added items. Calibrated confidence scores are provided with predictions, and this indicates whether the transactions can be safely automated, sent to policy validation, or sent to expert scrutiny. This is the selective automation model that balances operational efficiency and regulatory risk, compliance integrity, and scale. The architecture is deployed in a controlled machine learning system and combines continuous monitoring, auditability, and retraining pipelines based on feedback. The experience of large-scale deployments has shown that they can substantially reduce the effort required for manual rule formulation and scrutiny, increase the accuracy of classification into thousands of categories, and have a quantifiable financial effect, without compromising transparency to auditors and other regulatory stakeholders. The article defines intelligent, ML-driven tax automation as a feasible and responsible alternative to the legacy rule-based systems in the global compliance areas.