A Critical Study of the Impact of AI in the Compliance Procedure of Indirect Taxation of Corporates in the Maval Region of the Pune District in Maharashtra State

Abstract

The use of artificial intelligence (AI) in corporate tax compliance has become necessary due to the growing complexity of indirect taxation and the ongoing changes in tax regulations. AI-driven tax automation reduces human error and boosts efficiency by assisting with tax filing, compliance monitoring, and regulation adherence. The impact of AI on corporates' indirect tax compliance processes in the Pune area is critically examined in this study. This study determines the degree to which AI improves the effectiveness and transparency of tax compliance through a methodical examination of AI implementation, obstacles, and advantages. In order to provide a comprehensive understanding of AI's involvement in taxation, this study also examines case studies, legal ramifications, technological developments, and company experiences.

By decreasing human labour, lowering tax fraud, and guaranteeing increased accuracy, AI-driven solution such as machine learning, robotic process automation (RPA), and predictive analytic are revolutionizing indirect tax compliance. Corporates, however, confront a number of difficulties, such as data security issues, high implementation costs, and legal restrictions. Using data from structured surveys and interviews with tax experts, corporate accountants, and AI developers, this study offers empirical proof of the uptake and efficacy of AI-driven tax compliance solutions. Although widespread deployment necessitates supportive regulations and a trained staff, findings indicate that AI adoption considerably enhances compliance efficiency. The study ends with tactical suggestions for resolving issues and improving the incorporation of AI in corporate tax compliance.