Vijayawada, May 4, 2017: PwC India today announced that it has deployed a cutting-edge fraud analytics solution for the Andhra Pradesh Commercial Taxes Department. The solution has helped the tax department to not only identify fraudulent dealers, but also expand the tax base and increase compliance. This is an ongoing multi-year engagement for PwC India, the first phase of which was completed in March 2017.
Combining its deep knowledge in the indirect taxation space along with sophisticated data analytics on the dealers data, PwC India deployed a scalable analytics platform to create a 360-degree view of transactions and a predictive model for identifying high-risk dealers -- tools now being shared with other state governments in India.
According to J. Syamala Rao, Commissioner, Commercial Taxes Department of Andhra Pradesh: “PwC’s fraud analytics solution has helped us in automating the audit process and identifying possible fraudulent dealers and transactions. This will deter dealers from committing fraud and enable us to increase the tax net, which our government is committed to doing.”
Some of the other key features of this solution include:
Sudipta Ghosh, Partner and Leader, Data & Analytics, PwC India said: “Leakages in tax collections and inability to detect fraud has been a challenge for successive state governments. PwC is proud to partner with the AP Commercial Taxes Department in using advanced analytics to plug the perennial loopholes faced in tax collection. We hope that this solution can be a game changer for the AP and other state tax departments, as we truly believe that efficient tax collections can go a long way in the development of a progressive society.”
The solution has the potential to scan around 100 million records of all dealers’ data such as invoices, returns, waybills and transit passes to identify and categorize possible fraudulent dealers based on several metrics and analytical models. This would help the department to narrow down the investigation set and increase the effectiveness of the audits.
Further, there is an ability to refine the models on a continual basis in order to increase the accuracy over a period of time. Specialist knowledge of the various causes of leakage gets incorporated in the statistical models, which help in institutionalizing this knowledge base. This knowledge base can subsequently be shared between the state governments for widening the tax net. The flexibility of the solution can be further enhanced by incorporating external data feeds which can potentially increase the accuracy of the predictions.
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