Tax Analytics: An Overview

Tax data-gathering has traditionally focused on retrospective, dealing with transactions that have already happened, be it in terms of business planning, compliance, income-tax etc. While hindsight is an important aspect, the usage of data analytics can give us more insight as well as foresight, into what really lies ahead for tax departments/organizations. Benefitting both private and public sector alike, tax analytics combines both technical tax knowledge and advanced IT, with large sets of master and transactional data, for the identification of patterns as well as anomalies. This, in turn, can help enterprises understanding aspects of business, which can have an impact on tax outcomes. While in the case of the public sector, tax analytics would be extremely helpful in social/financial profiling, and identification of tax evasion and other financial malpractices such as, ITC over claims, circular trading, under-declaration of sale, manipulation of transfer prices etc. Corporate organizations can transform volumes of transactional data into valuable insights, thus gaining an opportunity to find overpaid/under-claimed taxes as well as hidden business opportunities using data analytics. This, in turn, also allows businesses to identify risks, leverage opportunities and gain quantitative insights as well as visibility, on their tax obligations. Coming to the public sector and the milieu in India, advanced analytics can play a crucial role in the integration/exploitation of multiple data sources and assist tax departments in bridging the so-called tax gap and enhancing efficiency, by the optimal use of resources. This approach will also help them build an integrated view of tax filers/individual tax submissions and respond in a more targeted way. As financial malpractices continue to evolve, tax analytics can help in the identification of data patterns, which may otherwise not be so visible to human analysts, as sometimes unearthing a culprit means going through a confounding labyrinth of data i.e. phone records, PAN details, credit card information, tax returns and in some cases even social media etc. This is where the use of analytics comes in, as combing through the gargantuan volume of structures/unstructured data and make sense of it, is virtually impossible, however, data, so collected generally tends to leave a pattern, which analytics can detect, raise an alert about and tax authorities can further investigate. Tax departments in India, like the CBDT (Central Board of Direct Taxes), CBEC (Central Board of Excise and Customs) and various State level departments like CTD (Commercial Tax Departments)are now employing the use of metrics to catch tax evaders, discovering benami property transactions etc., wherein relationship between different entities/people via varied data sets such as phone calls addresses, social media, travel, IT returns etc. can go up to 16 levels deep. Tax analytics, hence, can facilitate early detection, a reduction in false positives, help authorities in running scenario models that would help in better planning/policy formation. All this, in turn, will result in a faster real-time analysis of information, creating a platform to handle more cases with ease and efficacy. As our informal economy continues to shrink, post-demonetization and GST, the deployment of tax analytics, is where the future of tax administration lies. Even for the private sector, analytical procedures on reportable historic data will not only highlight the areas of tax authority, but also provide remedial opportunities. Saving the reputation of the tax function from harm, tax analytics can also counter inexplicable anomalies and save organizations from potential monetary impacts, in the pre-filing stage itself. Hence a holistic tax overview via tax analytics can help enhance decision making and avoid any unintended consequences for the private sector, while increasing the tax to GDP ratio of the country, acting as a total game changer.

Author: Rajiv Diwan, Practice Head – Advanced Analytics

Rajiv Diwan heads the Advanced Analytics Practice at ITL and is responsible for both – customer acquisition and defining solution offerings of the Practice. He has setup the CoE on Machine Learning from scratch at ITL and has been instrumental in penetrating into new verticals for ITL; including BFSI. Rajiv is engineering graduate from BIT, Bangalore with specialization in Computer Science; having over 18 years of experience in Analytics, Data warehousing, BI and Large Program Management.

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