A study on benefits of data analytics tools in indirect tax administration and management – with reference to GST in India

Автор: Mr. Gowtham R., Dr. Chitra S.

Журнал: Science, Education and Innovations in the Context of Modern Problems @imcra

Статья в выпуске: 4 vol.8, 2025 года.

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Purpose: The purpose of this study is to examine the benefits of various data analytics tools in indirect tax administration and management especially in the Indian context. The other purpose of this paper is grouping the overall benefits of data analytics in managing indirect taxes. Design/Methodology/Approach: Reports of various companies specializing in tax audit has been used to strongly establish the background for this study. The study adopted survey design where around 100 tax professionals were selected on convenient basis to examine the benefits of data analytics in tax admin-istration. The collected primary data were analysed with the help of IBM SPSS25 software and descrip-tive statistics & Exploratory Factor Analysis was used to generate the results. Findings: The study finds that data analytics proves to best option for managing indirect taxes with grow-ing digitalization and industry 4.0. The study has grouped overall benefits of data analytics in three components namely tax administration and management, fraud detection and monitoring. Research limitations/Implications: It is better recommended to use data analytics tools to ensure quality services for tax payers as well to have tax control and preventing fraudulent practices due to loop holes in the system. Originality/value: Studies related to tax and technology was quite common in foreign literature and not much in Indian context. This study is unique because it attempts to examine the benefits of data analytics in taxation through survey of tax professional thereby providing empirical evidences for further stud-ies in this domain.

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Data analytics, Goods and Services Tax, Enterprise Resource Planning, Business Intelligence, Tax Administration and Management

Короткий адрес: https://sciup.org/16010640

IDR: 16010640   |   DOI: 10.56334/sei/8.4.79

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