Economic loss caused by fraud has become a subject of concern for countries globally. Digital world also provides data and these can be leveraged to detect and prevent fraud while also applying forensic analytics to recover the loss. Although gathering and collating data from various sources poses a challenge, the benefits outweigh the costs. Data analytics, if implemented correctly, may detect fraud and prevent a potential economic loss. The article discusses challenges, solutions and technologies for implementing a data-driven approach.

Goods and service tax (GST) is a value-added tax which is levied on goods and services sold and consumed domestically within a country. Although GST is paid by customers it is remitted to the government by the businesses selling the goods and services. The implementation of GST in India is a relatively new development that has impacted on fiscal transfers. The Fifteenth Finance Commission of India is currently deliberating on its terms of reference to determine fiscal transfers from the centre to state governments for the period 2020/1 to 2024/5. The GST Network (GSTN) has been established to provide information technology infrastructure to taxpayers, central and state governments, dealers and all stakeholders. Evidently, there are substantial opportunities to leverage data emanating from GSTN. In such a context, the role of data analytics becomes prominent in monitoring tax administration, mitigating tax evasion, leveraging digitisation and designing fiscal federal policy. The implications presented in this article are relevant to any country having a federal structure that has implemented GST in some form or another.