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Using Data Analytics to optimize Revenue Management

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With the increase in digital penetration, government finance agencies have started to use advanced analytics as data has become the most important element to improve their efficiency. Technologies such as data analytics have provided sales departments with an effective solution for optimizing revenue generation, management, and plug-in leaks in revenue. To clarify these questions, Siddarth Jain, Haryana Government Additional Commissioner, Excise and Taxes, spoke at the Intelligence and Revenue Management Summit.

He opened his address agreeing to Yashpal Singh’s statement that “Haryana is the only Model-1 state among the participants of the summit”. He said, “We’ve already started working on it and in the coming few days we will be turning into a Model-2 state. Transition process is already ongoing.”

Speaking on the importance of data and how other panellists have thrown light on the same, he mentioned that “In the last one month I’ve worked on numerous cases of TDS, GST, etc. and we’ve made crores of recovery. It becomes easy if we have data.” Sharing his experience of working with various government departments of the state and of the Centre, he added, “What people are not able to understand is that data is low-hanging anti-vision fruit. It all falls in line and becomes easy when you have complete data. So one does not need to have informers, perform roadside checks, etc.”

Jain cited an example and said that TCS is where it is known that so many suppliers are based in Haryana but are not paying any taxes to the state. “Amazon is my data provider. So when I have enough data I do not have to go to conventional sources or consult departments like GSTN, PIFA, etc. Counterparty data, banking data, data from PayTm and other platforms, when integrated then even through simple reports we will be able to improve our revenue,” he added.

Addressing emerging technologies like artificial intelligence and machine learning, Jain expressed a little scepticism saying “If you personally ask me, until and unless I see a proof of concept I do not have much faith in artificial intelligence or machine learning. I haven’t heard anyone saying that we booked some multi-crore case and did some multi-crore recovery using artificial intelligence.” It is possible that in the coming years there are advanced tools with such technologies that would be effective but as of now what is required is to integrate the existing scattered data, build a common platform, convert it into actionable intelligence and start acting on it, he added.

He raised questions on the utilisation of artificial intelligence and machine in revenue and related work. “If someone is working on carousel fraud and witnessed circular trading using artificial intelligence tool, then that’s not something that requires such a technology. For me, even an excel programme can work for the issue. So, I want to know from other panellists that in what ways actually can artificial intelligence, big data, ML, etc. be implemented in revenue intelligence.”

On data security, he said, “Even being a Model-1 state we have clearly demarcated guidelines. We’re working with Wipro for data. We have NDAs with them and we have dedicated servers on which data is stored. Now when we’re heading to be a Model-2 state, our data will be sourced from State Data Centres (SDCs). Thus, considering this, our data is quite safe.” In fact, with the new edition of GSTN that holds options like know your supplier, know your taxpayer, etc. the department is sharing a lot of data in the public domain, he added.

Elaborating on sharing the data on public platforms he said, “In the coming years I see that big companies come and say ‘can you run a know your supplier for me?’. This is because the startups that have been caught and booked for carrying out transactions of hundreds of crores and were sitting on fake credit, the companies that have collaborated with them had no clue who they were and all the liability of recovery went onto the companies.” So to avoid such happening, GSTN is providing risk ratings. This is the next step in the industry where through data, vendor authentication and vendor verification will be done. All this data will come through GSTN. Such data lays out quite clear information about the taxpayer, he added.

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