Data-Driven Governance in GST: Leveraging Big Data for Tax Compliance Monitoring | Zenodo Skip to main Communities My dashboard Log in Sign up Journal of Scholastic Engineering Science and Management Published April 10, 2026 | Version v1 Journal article Restricted Data-Driven Governance in GST: Leveraging Big Data for Tax Compliance Monitoring Authors/Creators Nalinadevi T R (Researcher) 1 Show affiliations 1.
Librarian, Department Of Library And Information Science, Government First Grade College, Kodihalli Description Abstract The introduction of the Goods and Services Tax (GST) has significantly transformed India’s indirect tax administration by establishing a technology-based system for tax reporting, monitoring, and compliance. With the rapid growth of digital transactions through the Goods and Services Tax Network (GSTN), vast datasets are now generated and can be analyzed to support data-driven governance. This study examines how big data analytics can strengthen tax compliance monitoring within the GST framework. The research relies on secondary data collected from government reports, policy documents, academic studies, and statistical publications issued by institutions such as the Ministry of Finance and the Central Board of Indirect Taxes and Customs. The study highlights how digital mechanisms including e-invoicing, e-way bills, and automated return filing produce real-time transactional data that can be analyzed to detect irregularities, limit tax evasion, and improve taxpayer compliance. The findings suggest that integrating big data analytics into GST administration enhances transparency, strengthens risk-based compliance systems, and supports evidence-based decision-making. However, issues related to data privacy, cybersecurity, and technical capacity remain significant challenges. The paper concludes that effective use of big data within the GST ecosystem can substantially improve compliance monitoring and policy development, contributing to stronger digital governance and a more efficient tax administration system. Keywords: e-Governance, Big Data Analytics, GST Compliance, Tax Administration, Data-Driven Governance, Digital Taxation 1. Introduction Digital technologies have reshaped governance systems across the world. Governments are increasingly adopting digital platforms to improve administrative efficiency, transparency, and service delivery. In the field of taxation, digital transformation has enabled authorities to collect, process, and analyze large volumes of financial data, which improves monitoring and enforcement mechanisms. India’s Goods and Services Tax (GST), introduced in 2017, represents one of the most significant tax reforms in the country. It replaced multiple indirect taxes with a unified tax system and introduced a technology-driven framework for tax administration. The Goods and Services Tax Network (GSTN) functions as the central digital infrastructure that supports taxpayer registration, return filing, invoice matching, and compliance monitoring. Because GST operations occur on digital platforms, the system generates large volumes of transactional data. This data creates new opportunities for the use of big data analytics in governance. Through analytical tools, tax authorities can examine transaction patterns, detect inconsistencies, and identify potential cases of tax evasion. The integration of digital technologies such as e-invoicing and the e-way bill system has strengthened the monitoring of supply chains and commercial transactions. These tools allow tax authorities to track goods movement and verify invoice information in real time, making tax administration more transparent and efficient. As governments move toward data-driven governance, the use of big data analytics has become increasingly important in improving policy implementation and compliance monitoring. This study explores how big data analytics contributes to GST compliance monitoring and the broader development of digital tax governance in India. 2. Literature Review Scholars have widely examined the relationship between digital governance and tax administration. Research suggests that technology-based tax systems improve efficiency, transparency, and compliance. Nayyar and Singh (2018) argued that the implementation of GST increased transparency in India’s indirect tax system and reduced cascading taxation. However, they also noted that businesses initially struggled with technological adaptation and compliance costs. Over time, digital mechanisms such as e-way bills and e-invoicing improved monitoring of supply chains and reduced opportunities for tax evasion. The concept of big data analytics has also gained significant attention in governance research. Chen, Chiang, and Storey (2012) describe big data analytics as a powerful approach for analyzing complex datasets to support organizational decision-making. Similarly, Kitchin (2014) emphasizes that big data enables governments to adopt predictive analytics and evidence-based policymaking. In taxation systems, big data analytics allows authorities to analyze millions of transactions to identify suspicious patterns. Gupta and Saini (2020) found that data analytics techniques improve compliance monitoring by identifying inconsistencies between reported and actual transactions. By integrating multiple data sources such as invoices, tax returns, and payment records, authorities can detect fraudulent practices including fake invoicing and input tax credit fraud. Davenport (2014) also highlights that big data analytics strengthens organizational capabilities by enabling data-driven decision-making and improving operational efficiency. The use of advanced technologies such as artificial intelligence, machine learning, and blockchain has further expanded the potential of digital tax systems. These technologies enable governments to automate compliance monitoring processes and reduce human error. Artificial intelligence systems can analyze large datasets to identify patterns associated with tax fraud or non-compliance. According to Janssen and Kuk (2016), integrating big data technologies into government information systems enhances analytical capacity and supports more effective policy implementation. Despite these advantages, researchers also highlight several challenges. One of the most significant concerns involves the protection of taxpayer data. Digital tax systems collect sensitive financial information that must be safeguarded against unauthorized access or misuse. Zuboff (2019) warns that large-scale data systems may pose privacy risks if appropriate regulatory frameworks are not established. Another challenge involves data integration. Tax administration often requires information from multiple government databases that may not be compatible. Janssen and Kuk (2016) note that the absence of standardized data formats and integration frameworks can limit the effective use of big data in governance. In addition, small and medium enterprises (SMEs) may experience difficulties adapting to digital tax systems due to limited technological resources and digital literacy. Scholars therefore emphasize the importance of capacity-building initiatives and user-friendly digital platforms to support taxpayers during the transition to digital systems. Overall, existing literature indicates that big data analytics has significant potential to strengthen tax administration and compliance monitoring. However, successful implementation requires strong institutional frameworks, data protection policies, and technological infrastructure. 3. Methodology This research adopts a qualitative approach based on secondary data analysis. The objective is to examine how big data analytics contributes to GST compliance monitoring and digital tax governance. Data Sources Secondary data for the study was collected from multiple credible sources, including: Government reports issued by the Ministry of Finance Policy documents and circulars from the Central Board of Indirect Taxes and Customs Reports published by the Organisation for Economic Co-operation and Development (OECD) Academic journals, books, and conference papers Industry reports and statistical publications Method of Analysis The collected data was analyzed using descriptive and thematic analysis techniques. The analysis focused on identifying key themes related to: Digital tax governance Big data analytics in taxation GST compliance monitoring mechanisms Challenges and opportunities in data-driven governance The findings from different sources were synthesized to understand how big data analytics supports compliance monitoring under the GST framework. 4. Discussion The findings indicate that the implementation of GST has significantly accelerated the digitalization of tax administration in India. The GST regime introduced a unified and technology-driven system that integrates multiple digital platforms to support tax reporting, monitoring, and compliance (Garg, 2017; Nayyar & Singh, 2018). The Goods and Services Tax Network (GSTN) provides the digital infrastructure required for GST operations. Through this platform, millions of taxpayers submit returns, upload invoices, and make tax payments electronically. These processes generate extensive datasets that can be analyzed to improve governance and compliance monitoring (Government of India, 2023; OECD, 2019). One major outcome of the GST system is the creation of a comprehensive digital ecosystem for tracking business transactions. Digital tools such as e-invoicing, the e-way bill system, and automated return filing enable real-time recording of transactions across supply chains (Awasthi & Engelschalk, 2018). These systems allow tax authorities to monitor the movement of goods and services across regions and sectors, increasing transparency in tax administration (Rajaraman, 2018). Another major advantage is the ability to conduct real-time transaction monitoring. Data generated through GST returns, e-invoices, and e-way bills provides detailed information about supplier-buyer relationships, tax liabilities, and supply chain movements (Gupta & Saini, 2020). By analyzing these datasets, tax authorities can identify mismatches between input and output tax credits and detect potential irregularities (Chen, Chiang, & Storey, 2012; Kitchin, 2014). Big data analytics also enables risk-based compliance management. Traditional tax systems relied heavily on manual verification and random audits. With big data tools, tax authorities can analyze large datasets to detect patterns associated with non-compliance (Davenport, 2014). For example, unusual invoice patterns or repeated tax credit claims may indicate fraudulent activity (Gupta & Saini, 2020). Such insights allow tax authorities, particularly the Central Board of Indirect Taxes and Customs, to conduct targeted investigations rather than broad inspections (Ministry of Finance, 2022). Data analytics also supports policy formulation and revenue forecasting. By examining historical tax data and transaction trends, policymakers can identify patterns in tax collections across industries and regions (OECD, 2020). These insights help government agencies design more effective tax policies and regulatory strategies (World Bank, 2020). Predictive analytics can also assist in estimating future tax revenues, which improves fiscal planning (Manyika et al., 2011). Furthermore, digital tax systems enhance transparency and accountability in tax administration. Automated processes reduce manual intervention and discretionary decision-making by officials (Bhatnagar, 2014). Standardized algorithms ensure consistent verification of transactions and tax calculations (Srivastava & Teo, 2010), which helps build trust between taxpayers and government institutions. Despite these benefits, several challenges remain. One major issue is the management of massive volumes of data generated within the GST ecosystem. Processing millions of transactions daily requires advanced data infrastructure and high-performance computing systems (Mayer-Schönberger & Cukier, 2013). Data privacy and cybersecurity are also critical concerns. Tax databases contain sensitive financial information that must be protected against breaches or misuse (Zuboff, 2019). As digital systems expand, they also become more vulnerable to cyber threats, making strong security frameworks essential (Janssen & Kuk, 2016). Another challenge involves data integration across government systems. Effective data-driven governance requires smooth information exchange between different regulatory agencies (Sun & Medaglia, 2019). However, differences in technological platforms and data formats can limit interoperability (OECD, 2019). The study also highlights the digital divide among taxpayers. Large corporations generally have the resources to adapt to digital tax systems, while SMEs may face difficulties due to limited infrastructure and technical expertise (Sharma & Singh, 2021). Addressing these issues requires government programs focused on digital literacy, training, and user-friendly digital interfaces (Yadav & Shankar, 2019). Finally, successful implementation of big data governance depends on skilled human resources capable of managing advanced analytics systems. Tax administrations must invest in training programs in data science, analytics, and information management (Davenport, 2014). Despite these challenges, integrating big data analytics into GST administration represents a major step toward modernizing tax governance in India. The digital infrastructure developed under GST provides a strong foundation for implementing advanced analytics tools that can improve compliance monitoring and revenue administration (OECD, 2020). Conclusion The implementation of the Goods and Services Tax has marked a major transition toward digital governance in India’s tax administration (Garg, 2017). The integration of digital technologies and big data analytics has created new possibilities for improving transparency, efficiency, and compliance monitoring (OECD, 2019). This study shows that data-driven governance can significantly strengthen GST compliance through real-time transaction monitoring, anomaly detection, and risk-based audits (Gupta & Saini, 2020). Big data analytics allows tax authorities to detect fraudulent activities more effectively and make informed policy decisions (Kitchin, 2014). However, the success of data-driven governance depends on addressing challenges related to data privacy, cybersecurity, and technological infrastructure (Janssen & Kuk, 2016). Strengthening digital systems, improving data protection frameworks, and increasing digital literacy among taxpayers will be essential for maximizing the benefits of big data in tax administration (World Bank, 2020). Overall, the use of big data analytics within the GST ecosystem has the potential to improve tax governance, increase compliance, and strengthen revenue administration in India References Awasthi, R., & Engelschalk, M. (2018). Taxation and the digital economy. World Bank. Bird, R. M., & Zolt, E. M. (2014). Technology and taxation in developing countries: From hand to mouse. ICTD Working Paper, 27. Bhatnagar, S. (2014). E-government: From vision to implementation. Sage Publications. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. Davenport, T. H. (2014). Big data at work: Dispelling the myths, uncovering the opportunities. Harvard Business Review Press. Garg, G. (2017). Basic concepts and features of goods and services tax in India. International Journal of Scientific Research and Management, 5(3), 542–549. Government of India. (2023). GST annual report. Ministry of Finance. Gupta, S., & Saini, R. (2020). Big data analytics in taxation: Implications for compliance management. Journal of Financial Regulation and Compliance, 28(3), 321–335. Janssen, M., & Kuk, G. (2016). Big data in government: A review and research agenda. 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Journal of Tax Administration, 7(1), 45–60. World Bank. (2020). Digital government and taxation systems. World Bank Publications. Yadav, S., & Shankar, R. (2019). E-governance initiatives in Indian taxation system. International Journal of Public Administration, 42(9), 739–750. Zuboff, S. (2019). The age of surveillance capitalism. Public Affairs. Zysman, J., & Kenney, M. (2018). The next phase in the digital revolution. Communications of the ACM, 61(2), 54–63. Central Board of Indirect Taxes and Customs. (2022). Annual report. Government of India. Ministry of Finance. (2022). Economic Survey of India. Government of India. Goods and Services Tax Council. (2023). GST statistics and policy updates. Government of India Files Restricted The record is publicly accessible, but files are restricted. 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Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords e-Governance, Big Data Analytics, GST Compliance, Tax Administration, Data-Driven Governance, Digital Taxation Details DOI DOI Badge DOI 10.5281/zenodo.19500216 Markdown [](https://doi.org/10.5281/zenodo.19500216) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19500216.svg :target: https://doi.org/10.5281/zenodo.19500216 HTML <a href="https://doi.org/10.5281/zenodo.19500216"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19500216.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19500216.svg Target URL https://doi.org/10.5281/zenodo.19500216 Resource type Journal article Publisher Journal of Scholastic Engineering Science and Management (JSESM) Published in Journal of Scholastic Engineering Science and Management (JSESM), A Peer Reviewed Universities Refereed Multidisciplinary & UGC Approved Research JournalJournal of Scholastic Engineering Science and Management (JSESM) A Peer Reviewed Universities Refereed Multidisciplinary & UGC Approved Research Journal, 5(Special Issue 3), 219-225, ISSN: 2583-3294, 2026. Conference ICSSR-SRC Sponsored National Conference (GST Reforms and India's Growth Trajectory: Sectoral Opportunities and Challenges) , Government First Grade College Kanakapura, Bengaluru South District, Karnataka, India - 562 117, 24th March 2026 (Session II, Part II) Languages English Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Copyright Journal of Scholastic Engineering Science and Management (JSESM) Citation Export Technical metadata Created April 10, 2026 Modified April 10, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. 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