Importance of Data Analytics in Tax Ecosystem - EY India by Bhavya Bedha

Value addition in the tax ecosystem through data analyticsÂ

Background The convergence of several technologies is fueling the rate of growth of data. Its volume and variety continue to grow as information trickles in from the organizations’ internal systems, digital platforms, wireless sensors and hand-held devices like barcode scanners, cell phones, etc. Data storage and processing has become easier too at lower price points. Evidently, all this has value for tax administration or industry if the data, which we speak of, can be leveraged or monetized by way of analytics. In the context of tax, data analytics fuels better discovery of tax revenue for the tax administration while also enabling the industry for tax optimization due to enhanced visibility into their data ecosystems. Above all, it can enable faster and evidence-based decisions on overall tax management. Clearly, this has repercussions for the opinion leaders and decision makers on either side – tax administration, and trade and industry. Implications for the tax administration - Driven by revenue pressures and shrinking headcounts, tax authorities in India and elsewhere are increasingly relying on digital methods to collect taxpayers’ data and administer their tax systems. Amid increasing demands for tax transparency by tax administration and supranational organizations, many tax authorities are building sophisticated datagathering platforms that enable collecting, matching and sharing of taxpayers’ data. They apply data analytics to mine this data to help increase tax collections, target compliance initiatives and improve overall efficiency in their core operations. Practically speaking, this means that an unprecedented amount of taxpayers’ information is flowing between tax administration and businesses. This data is being analyzed and used in new and more expansive ways. The Goods and Services Tax (GST) reporting requirements mandate increased data collection and disclosure. With millions of taxpayers in the states and union territories (UTs), including the Centre, having adopted the GST reporting requirements and many more new taxpayers soon to follow, the volume and pace of data collection and analysis will only

continue to grow. The Economic Survey 2017-18 observed that data from the GST can help unveil some long-elusive and basic facts about the Indian economy. Evidently so, the survey observed that its preliminary analysis of GST data has yielded feast of findings. For industry, there are credible GST benefits too and it has a pervasive effect and can change companiesâ&#x20AC;&#x2122; prospects in several ways. The mechanism of input tax credit across the supply chains leads to elimination of cascading effect, thereby, lowering the supply chain costs. Overall tax is also reduced due to the removal of origin-based taxes like central sales tax, entry tax/octroi, etc. If the set-off is done with the help of data analytics efficiently, effective tax rate can be lower than the GST rate. A data driven tax planning can enable companies to have higher visibility into their supply chain costs thereby drifting toward cost efficiencies. They can also explore market opportunities which can be thrown up by analysis of tax data. EY in partnership with Indian Institute of Management, Kashipur, organized a stakeholder consultative roundtable on 26 September 2018 in New Delhi to help generate new ideas which could be taken up with government. The roundtable brought together distinguished experts on taxation, technology from IIM, GSTN and businesses to share their perspectives on how data analytics can be used for value addition in the tax ecosystem. This document provides a summary of the issues discussed at the round table and the conclusions reached. The speakers and the participants emphasized the importance and urgency of institutionalizing data analytics in tax administration across the centre and Indian states. The seminar also brought out important facts on the small and medium enterprises, especially proprietorship companies that require help in preparing accounts in a structured manner to facilitate easier filing of returns and thereby promoting compliance. Standardization of invoices could also be an important step. The seminar also brought forward the critical shortage of skilled personnel in this field. Training and capacity building of all the stakeholders is therefore urgently required, to explore this opportunity. The opinions shared by the speakers are their own personal observations.

Foreword

This is fourth roundtable in the series of roundtables organized by EY in collaboration with the various knowledge partners. The first roundtable was on the subject of “GST and Institution Building” which was organized in collaboration with the Confederation of Indian industry (CII). The second roundtable on Trade Policy was organized in collaboration with Indian Institute of Foreign Trade (IIFT) and the third roundtable was on Carbon Tax in collaboration with Shakti Foundation. The fourth roundtable on value addition in the tax ecosystem through data analytics was organized in collaboration with IIM, Kashipur. The rationale for these roundtables is to generate ideas for public policy. The intention is to take this up with the line ministries as a thought leadership and recommendation report. Needless to mention the Tax and Economic Policy Group within EY would like to play the role of a think tank for the government. The common refrain now is that data is the new oil of 21st century. Today corporate power is seen to emanate from control of data which could be manipulated to gain strategic advantage in the market. In this roundtable, we focused our attention on tax data especially the enormous tax data captured by the Goods and Services Tax network (GSTN) consequent to the implementation of the GST. This data may provide valuable insights for policy making. Economic Survey 2017-18 demonstrated this quite admirably and some of these were: • Smaller companies have voluntarily taken GST registration even though they may be eligible for exemption because they largely buy from large companies and are therefore required to utilize input tax credit • Per Capita State Gross Domestic Product (GSDP) is more strongly correlated with international exports than interstate trade • India’s internal trade on goods and services (excluding non-GST goods and services) is about 60% of the GDP and is much higher than the earlier estimates of between 30%-50% • The formal sector in India is larger than commonly presumed based on GST tax figures

Prakash Kumar CEO, Goods and Services Tax Network

V S Krishnan National Leader, Tax and Economic Policy Group, EY India

Nitin Singh Associate Professor, IIM, Kashipur

There is a whole new opportunity for Indian states to use data analytics while estimating tax non-compliance especially in sectors like services where states were not involved in the tax collection. This is particularly important due to limited period of compensation for five years and need for sustainable growth in revenue. To use data analytics, we need expertise with twin skills of domain knowledge in tax as well as an understanding of methodology and techniques for doing tax gap analysis.

The effective use of tax data by government would require a close coordination between government agencies especially Central Board of Indirect taxes and Customs (CBIC)-Central Board of Direct Taxation (CBDT)state tax departments. I remember when I was in Board (CBEC), just before I retired in November 2015, the CBIC and CBDT signed a Memorandum of Understanding (MoU) to share information on specific datasets. The corporates may also use the tax data to analyze the consumer behavior patterns for improving market share.

V S Krishnan

National Leader, Tax and Economic Policy Group, EY India

Dr. S B Singh Senior Advisor, Tax and Economic Policy Group, EY India

Rajesh Bahl Senior Vice President, Finance, Teleperformance

Speakers

Key

recommendations

Some of the significant ideas that emerged from the discussions at the round table are as following:

1 Setting up of analytics unit at state tax departments Data analytics unit is a low hanging fruit for the state governments. By identifying non-compliance and evasion, the tax authorities will benefit by institutionalizing analytics by developing a single view of the taxpayer by creating an integrated information and communications technology (ICT) infrastructure that combines, transforms and consolidates data from a wide range of sources. State tax authorities by establishing the analytical unit/ economic intelligence unit (EIU) can maximize the use of data and deploy analytics to identify the incidence, scale and significance of noncompliance and to target resources effectively to overcome those risks. New opportunities await states to use data analytics to estimate tax gap, particularly non-compliance especially in sectors like services where states were not involved in tax collection. This is particularly important for a limited period of five years for compensation and is needed for sustainable growth in revenue. To derive benefit from this data, we need people who have the twin skills of domain knowledge in tax with an understanding of methodology and techniques for doing tax gap analysis. At the Centre, CBIC and CBDT have already initiated data analysis, states’ tax authorities could benefit by their expertise and experiences. Hand-holding support from central tax authorities will enable states to make informed decisions. States can also engage with institutes of eminence such as IIMs and IITs in setting up and making the analytics unit operational. Recommendation for consideration of respective state tax authorities

Data sharing between Centre and states With the advent of GST, both Centre and states are increasingly working together while collecting massive data related to taxpayers whether individuals or businesses. Aptly, central government in consultation with states should come out with a data sharing protocol between various government agencies not limited to tax, for result-oriented and meaningful analysis of patterns, relationships and suspicious transactions. Timely sharing of approved structured data will help in maximizing governance through appropriate allocation of resources. Recommendations for Ministry of Finance and Ministry of Electronics and Information Technology

3 Tax officers’ training curriculum should include analytics Currently the tax departments are facing shortage of candidates with skills of analytics, finance and tax. Creating a talent management strategy blending the right mix of skills and experience — IT, statistical, analytical and tax domain knowledge — may help in driving informed decision-making. To address the shortage of skills, state tax authorities must include analytics as a subject in the training curriculum at induction. States’ public service commissions should include basic data analysis as part of recruitment examination for tax departments. Recommendation for consideration of respective state tax authorities in consultation with union/ state public service commissions

GST compliance reporting

Transportation planning

The conference brought out the critical need in helping small proprietorship firms in maintenance of accounts. Some accounting guidelines in consultation with Institute of Chartered Accountants of India (ICAI) which could include standardization of invoices can be introduced. At present assistance is being provided for filing of returns but one needs to also help in maintenance of accounts and invoice formats.

Union Ministry of Road Transport and Highways can use the e-way bill data to gain insights into what moves from place-A to B and how. The Ministry could be made a nodal agency for making informed decisions on alignment of states’ roads or planning of new routes based on the data shared by GSTN. The understanding of such data will enable better planning of hubs and spokes of transportation system along with associated infrastructure like cold storage, warehousing, etc. This will certainly help in better logistics planning.

Recommendation for Ministry of Corporate Affairs in consultation with ICAI

Recommendation for Ministry of road transport and highways

5 GST Saathi or financial correspondents Government of India’s path breaking initiative of “Bank Saathi” or banking correspondent for financial inclusion should be replicated by introducing GST Saathi or financial correspondents for the taxpayers having smaller turnover. These financial correspondents will enable taxpayers in book keeping and basic accountancy. This will increase the documentary discipline as well as compliance. There is already a similar arrangement implemented by CBEC, by way of services tax return preparer and by CBDT as well. Recommendation for CBIC in consultation with the Department of Revenue, Union Ministry of Finance

7 Credit rating of small and medium enterprises The data on turnover/tax data contained in return and regularity of payment of taxes could be used to generate credit rating of smaller and medium sized enterprises. Ministry of Micro, Small and Medium Enterprises (M/o MSME) along with Department of Financial Services (banking) and credit rating agencies should take the initiative of hosting a pilot project for credit ratings of micro and small enterprises. Recommendation for Ministry of Micro, Small and Medium Enterprise

Prakash Kumar

CEO, GSTN

Using GSTN tax data for policy making â&#x20AC;&#x201C; some ideas For the first time, GST system has enabled a collection of granular data at harmonized system of nomenclature (HSN) level and at invoice level, as invoice level data was not collected by the central tax authorities (Central Excise and Service Tax) and majority of state value added tax (VAT) administrations. The availability of such a granular data opens new avenues for data analytics for policy making by various wings of the government. The scope gets further enhanced due to the availability

of data contained in e-way bills which, apart from containing data on invoice and HSN, also includes data on movement of goods, vehicle used, places of transshipments, etc. The data also comprises information from 19,100 pin codes, making it very granular. On analyzing this data, a deep insight into consumption patterns, what moves from one location to another, modes of transport, availability of tricks at various locations, etc. can be derived.

Tel E-way bill

GSTN

Pin to Pin Email

Regitration data Invoice

HSN Return Mode of Tpt

The data contained in the various forms and statuses for first 14 months of operations is given below: Form

Data contained

Numbers

Registration application

• Legal name, trade name and PAN of business entity

1.14 crores

• Names, addresses and PAN of promoters • Names, addresses and PAN of authorized signatories • Address of principal place of business and all additional places of business • HSN codes of top five goods and services dealt • Bank account details Return

• B2B invoice data (tax rates, taxable amount, tax, etc.) • B2C supplies (tax rates, taxable amount, tax, place of supply (PoS), etc.)

15.7 crores in 14 months

• Quantity of export, supplies to Special Economic Zone (SEZ), etc. • Import of goods and services, etc. Challan

• Taxes paid under each head and period

5.34 crores

E-Way Bill

• Invoice with details of item, HSN and taxable amount

18 to 19 lakhs

• Consignor and consignees

per day

• Origin and destination at PIN code level

5.5 crores per

• Vehicle details

month

• Goods sent for job work

A few lakhs per

• Goods received after job work, etc.

month

Other forms

The policy makers can now have a granular data on commodities in terms of origin, distribution and place of consumption. This is available for 17,000 items for which HSN code is available in GST system. Since data is available at PIN code level, it gives a much better insight on the consumption of goods at this level.

Value addition in the tax ecosystem through data analytics

Some of the areas where policy making can get benefitted from this data are given below:

Consumption pattern

The commercial goods carrier market is highly fragmented. Now with e-way bill system, data is available at most granular levels on trucks that is likely to reach at a particular place in the next few days and places they would like to go further. The data, if made available on a platform, can be used by truck owners and businesses to create a truckers’ market place ushering huge transparency and helping trade/industry and truck-owners.

Market place for registered taxpayers

The invoice level data contained in GSTR-1 and that in GSTR-2A gives a complete picture of the enterprise in terms of the sales and purchases of the unit. This has great potential of creating a market place for registered units in terms of potential buyers and suppliers where search could be done based on location, type of goods/services, rating of taxpayer, etc. The platform, if created, could provide functionalities, which a market place provides.

The credit rating agencies largely focus on large and medium businesses leaving micro and small enterprises to fend for themselves. In addition, micro and small enterprises find it costly and rigorous to get the ratings. The data on turnover/tax data contained in return and regularity of payment of taxes could be used to generate credit rating of such enterprises.

Lending to micro and small enterprises

Truckers’ market place

Credit rating of micro and small enterprises

Small businesses rely heavily on the large businesses for payments. In addition, they also need working capital. Invoice discounting based on the concept of “trusted invoices” is a concept used today under Trade Receivables Discounting System (TReDS), which requires an elaborate process as invoices are not vouched. Return filed under GST can be used for “flow-based lending”, allowing SMEs to access working capital based on monthly returns. The four key characteristics for lending to be termed as “flow-based lending” are that lending is based on non-repudiating data; loan offers are customized for each customer; there is real time lending decision and disbursement; and loans are for short tenure. The return GSTR-3B with which payment is made is not linked with GSTR-1 which contains the invoice level data. Thus, there is a lack of trust in invoices due to provisional input credit, making it difficult for the lenders to offer loan. The provisional credit effectively makes the invoices “not trustable”. The proposed new GST return will bring the required linkage between invoice and return/taxes paid. This may help setup flow-based lending to enable firms to meet their working capital requirements, including continuous invoice upload and acceptance.

Dr. Nitin Singh

Associate Professor, IIM Kashipur

Data driven taxation: Challenges, opportunities and realities Tax evasion represents one of the major issues for many countries since it has a strong political and economic impact on the governance. India is among those countries that are particularly affected by severe tax evasions. The Indian government has detected GST evasion worth INR3,026 crores in 2017-2018 in relation to misuse of input tax credit and nonpayment of taxes. It was officially tabled before Rajya Sabha by Minister of State for Finance that, between July 2017 and June 2018, a total of 1,205 cases were detected amounting

to INR3,026 crores. Evidently, this is a big number and is a direct loss to the exchequer. Consequently, various centre and state government bodies are devoting great amount of human and financial resources to tackle the problem. Nature of cases include 1) misuse of input tax credit, 2) incorrect declaration in the GST returns, 3) tax declared in GST returns and are not paid, 4) cases where GST returns are not filed and tax not paid. This presents significant challenges as well as opportunities for the departments.

Challenges and opportunities Challenges relate to dealing with huge and diverse data sets lying in various databases and systems. It involves data extraction, storage, management and maintaining the data quality. The tax departments continuously deal with large and heterogeneous data managed through different software applications. Several times, it causes information redundancy and makes it difficult for the officers to work through the maze of data. Another challenge is that the software applications used at the departments are based on a taxpayer-centric paradigm, which do not allow any in-built analytics. As a result, it becomes difficult to do any exploration of relations between different companies within the software. Under such circumstances, tax evasion involving companies or even groups of companies are difficult to discover. This presents opportunities to apply data analytics methods. Rulebased detection techniques are useful for uncovering tax evasions. Such methods can identify anomalous transactions (or companies) and direct the resources to focus on those exceptions that need to be further investigated. Such methods can also be useful in exhaustive testing of data and lead to a faster determination of tax evasions. The detection methods can be through specific rule-based queries or may be based on predictive methods applying econometric techniques.

Traditional tax auditing methods typically require significant amount of time and human resources. They consume effort and rely on sample thus severely limiting the identification of tax evasions and eventually lead to lesser detection of evasions. In the recent past, efforts have been taken to use data analytics to discover tax evasion and financial frauds. Rulebased queries and methods become redundant with the change in behavior of tax evasion. In such cases, predictive methods adopting econometric methods are found to be useful. Data visualization techniques have also been applied to study tax evasion behavior in real time. Such methods offer a high flexibility in identifying fraudulent patterns and facilitate capturing a wide variety of suspicious tax evasion schemes (including fake invoices, VAT missing trader fraud and controlled transactions).

Realities The realities are that interventions have been done in other counties and are also underway in Indian context. The importance of handling tax evasions by means of methods that analyze networks of business transactions between subjects has been recently highlighted by the Council of the European Union. Some recent works include the analysis of networks about companies in Belgium. Likewise, data analytics was applied to discover specific families of suspicious transactions in China which mainly involved medium and large companies. In Italy, the economic environment is predominantly characterized by small- and medium-sized enterprises and the continuous evolution of tax evasion has been tackled by data analytics methods. We foresee adopting more such methods in Indian context as well at the center and the states. It is also a reality and fact that it is not easy to have a program to coordinate and architect the processes that make up their tax analytics strategy. For example, building relationships or joins across different transactional datasets (tax registration, tax filing, etc.) based on common information like GSTIN itself is time-consuming and requires significant efforts from data scientists. These joins have to be done tediously before data can be harnessed for any meaningful analyses. To cite another case, transactional data on tax filings may come in parts, i.e., for each week, month or quarter. It will require appending of multiple line items into a single consolidated database. Software vendors have identified this as a mine of business opportunity and created solutions around it. This process of data cleansing takes huge effort and sometimes costs 50%60% of time in a typical data analytics assignment. Indeed, any process and application depend on clear and consistent data. Also, it requires a team composition of skills coming from data sciences and domain, i.e., tax and technology. It is foreseen that increasing attention would be given by tax departments as well as companies to form cross-functional teams. Such teams may comprise of people from functions like finance, tax, analytics and technology. We see this convergence increasingly and, in fact, such teams will form a task force to put precise and meaningful analyses through tax ecosystems. It will emphatically facilitate data management and consistency issues and that is vital to reap the benefits. Evidently, there are benefits to be reaped but it will require a consistent, clear and structured approach to data driven taxation.

Dr. SB Singh

Senior Advisor, EY

Tax gap analysis by state governments to augment GST revenues With GST being implemented over a year ago, the revenue collection has not gone above INR1,00,000 crores in any of the months from a period of August 17 till August 18 except in April 2018. The revenues witnessed an initial strong growth but then declined till March 2018. After the spike in revenues in April 2018, the revenues declined and remained constant around INR95,000 crores. Out of 34 states/UTs in the country, 31 reported a “revenue shortfall” during the August 2017-June 2018 period. Among the larger states, Bihar’s deficit was 32% and Madhya Pradesh’s 22%. In the recent months of July-August 2018 some of the states’ tax authorities have improved their respective revenue collections, however majority of them are yet recover revenue to their truest potential and are largely reliant on compensation by centre.

EY estimated, for a state that in 2022-23, it will have an effective GST revenue of INR12,777 crores, a decline of INR1,295 crores compared to estimates of 2021-22. The five-year fullcompensation mechanism under the GST may ensure that the centre will make good any shortfall in the states’ GST revenue from what a 14% annual growth (over FY16 base) would have yielded them. However, once the compensation period of five years is over, there could be a dip in revenues for the state.

Value addition in the tax ecosystem through data analytics

compliance gaps. The above table highlights the enormity of taxpayers’ data collected by GSTN in first 14 months of GST. It certainly offers an exciting opportunity for the government and private sectors to make efficient use of this data in value addition of tax ecosystem and beyond.

In this context, the tax authorities should consider measuring the tax gap not only for the forecasting of potential tax revenue, but also for compliance management. Tax gap consists of two components, i.e., policy gap and compliance gap. Policy gap relates to a system where there is an alternative and preferred policy design. Compliance gap, on the other hand, relates to the behavior of tax assesses and working of the tax administration. The analysis of tax gap has helped many tax administrations improve their efficiency in resource allocation within a revenue authority to combat non-compliance and hence, has been increasingly used as a measure of effectiveness of a revenue authority. The identification of the total gap and the disaggregated tax gap by major industries and sectors provides a starting point for managing compliance. It identifies the priority sectors for which compliance activities should be undertaken.

Data analytics is a useful tool in estimating and addressing the tax gap observed by centre/state governments. Using various analytical techniques to identify anomalies, suspicious relationships and patterns, tax agencies can address a wide spectrum of non-compliance in a proactive, targeted and cost-effective way. Analytics mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Using analytical tools, EY estimated revenue potential for a few state tax administrations by using analytical tools. Accordingly, the revenue loss to the tune of INR2,158 crores were highlighted to the tax authorities due to non-compliance and tax evasion in a few states. The respective tax authorities are investigating the cases and raising demands against the findings.

The tax gap in the United Kingdom (UK) is estimated as 5.7% in 2016-17 or £33 billion (Measuring Tax Gap 2018 Edition, HMRC). This is 5.7% of the total theoretical tax liabilities and the same level as 2015-16. There has been a long-term reduction in the overall tax gap from 7.3% in 2005-06 to 5.7% in 2016-17. HMRC UK also calculated the tax gap in different categories. The value of the tax gap 2016-17 for different customer groups, types of tax and behaviors is provided below.

Senior leadership of respective tax authorities may advance the use of analytics by setting up of dedicated analytic unit within tax administration. It will enable the states to reduce revenue losses from non-compliance, criminal attacks, tax evasion and avoidance; making it easier for the taxpayers to comply and develop strategies to “nudge” taxpayers towards compliant behaviors, allocate their resources more efficiently and maximize returns from their debt management activities.

With the GST, tax authorities are better placed to realize value from their analytics programs. GST has provided us with ever increasing granular level details of business to business (B2B) and business to consumer (B2C) transactions, tax authorities should measure the tax gap and use the data in minimizing the

HMRC Tax gap by customer group (in billion £)

Individual

Other Taxes

£3.40

Large Business Small Business

£7.00

Value Added Tax £13.70

Source: HMRC- Measuring Tax Gap 2018 Edition

IT, NIC and CGT

£11.70

£13.50

1.7

Hidden Economy

3.2

Error

3.2

Non-payment

Corporation Tax £3.50

£5.40

HMRC Tax gap behavior wise (in billion £)

Avoidance

£1.60

Excise Duties £3.10

Mid-Sized Business £3.90 Criminals

HMRC Tax Gap by type of tax (in billion £)

3.4

Evasion

5.3

Legal Interpretation

5.3

Criminal Attack

5.4

Failure to take reasonable care

5.9

Evidently, analytics is playing an increasingly critical role in at least four domains of governmental revenue management: Risk case selection

Improving filing rate and debt management

Better taxpayer services

Policy evaluation

Risk case selection • Predictive Analytics • Pattern recognition and selection of risky cases • Identification of fraudulent evasion and concealment by taxpayers in their self-assesment

Improving filing rate and debt management • Prescriptive analytics • How taxpayer might behave in response to particular intervention

Application of analytics

Better taxpayer services • Proactive messaging, calling and other interventions in anticipation of potential noncompliance • Analytics can uncover areas of common confusion or lack of knowledge for e.g. filing deadlines or working of tax rule • Grievance redressal

Policy evaluation • Evaluate the impact of changes in policy • Forecast if growth in tax revenues is ‘equitable’ with growth in overall economy • Targets for revenue collection

With an objective to harness the true potential, tax authorities should invest in developing the skill sets of data analytics. Public sector will have to compete with private sector in attracting the talent.

Value addition in the tax ecosystem through data analytics

Rajesh Bahl

Senior Vice President Finance, Teleperformance

Optimizing taxes: Mapping value in data ecosystems As we all know that digital wave is embracing the corporates across the globe and as a result, companies are now sitting on piles of data which, if structured and analyzed, can result in tremendous value additions. Intense competition has forced companies to innovate using data analytics and has resulted in reduced product creation and delivery cycles. While the digital wave have swept into business decisions and analysis and helped in customer acquisition, customer lifecycle financial analysis, etc., there are still neglected areas where the power

of data analytics has not been harnessed. One such area is Tax (both direct and indirect) function where technology can play a key role in detecting leakages. On the indirect tax side, with the Introduction of GST in India, many taxes have been abolished and shall put pressure on the operational system of companies to ensure that no transaction skips the tax net. This will also have a huge impact on realignment of the distribution models of the companies to mitigate the increased impact of the new rates.

Potential areas where data analytics can be deployed to detect tax leakages a) Detecting incorrect tax deducted at source (TDS) deductions. Wrong or no TDS deductions can result into large penalties. b) Sales invoices analysis – Detecting which items are being tagged with wrong tax codes. c) Analyzing sales deliveries across geographies to detect impact of taxes. d) Vendor invoice transactions analysis – Detecting which of the invoice input taxes have been completely missed for claim from tax authorities. e) Analyzing the buying patterns across the inputs or services to restructure buying across geographies to lower the impact of taxes.

Challenges While we do agree that technology can play a role in value creation in tax, there are various challenges to meet the above objectives: • Large data set volumes cannot be extracted from the enterprise resource planning (ERP) systems • Absence of skills within information technology department to link these data sets to arrive at decision points • Unavailability of hardware and software budgets to manage large volumes of these data • Data transportation from one location to another • Multiple systems capturing the scattered data and absence of data universe • Lower staff count in Tax teams to take these projects ahead • Incomplete visibility of data flows across systems with users

Value addition in the tax ecosystem through data analytics

Optimizing taxes â&#x20AC;&#x201C; An opportunity for businesses It may also be highlighted that the corporate world is facing increasing regulatory uncertainties with the changeover from the value added tax (VAT) to GST regime. The policy landscape is continuously evolving and thereby firms are also in a state of uncertainty. It automatically extends regulatory uncertainties regarding data analytics in the tax environment. Data analytics can be of a great help in optimizing tax liability for the firms and businesses. It may facilitate better tax planning by optimizing all the related supply chain costs and GST payments for the firms. However, data analytics in the tax environment is relatively new and many of the regulatory details have not yet been tested in the businesses. Tax analytics programs aim mostly to leverage both structured and unstructured data. This data may come from different departments, teams, geographies and even companies. However, connecting both the format provides a more efficient mechanism to access and process data and harvest new insights. Since tax is integral to most areas of business, tax departments of businesses have immense opportunities to harness structured and unstructured intracompany datasets related to taxes. Data that exists in ERP systems and other relational database management systems is structured. On the other hand, data that exists as videos, work papers, scanned documents and applications like email and calendars is generally unstructured.

Setting up of tax shared services With an aim to achieve the objective of tax optimization, businesses may create tax shared services within firms with staff with different skill sets. The centre should have the required infrastructure, technology tools and functional staff so that pre-requisites of data analytics can be ensured. A structured framework for the analysis and outcomes need to be defined to achieve measurable outcomes. Most importantly, the IT infrastructure would need to be substantial to accommodate growing needs of data storage and processing. It is becoming increasingly affordable for companies to set up such a shared service center due to an increased convergence of in-memory processing, distributed architecture and parallel computing technologies. Over the next five years, we also foresee that tax shared services within firms will emerge as a sustainable business model. In the next five years, companies would increasingly use shared service centers supported by internal private cloud or off-premise. Another key success factor of such centers would depend on the availability of talent. Technology adoption cannot grow any faster than talents/skill which is trained to use it. Data is indeed driving growth in optimizing and tax planning for firms but that also depends on the availability of talent in data sciences.

Concluding remarks The roundtable organized in partnership with IIM, Kashipur, brought together an array of speakers from government, academia, trade and industry. The deliberations, marked by an active interaction highlighted the importance of data in policy making. The focus was on using the data on value addition in the tax ecosystem. The deliberations also emphasized the need for sharing structured tax data between the centre and state governments and also with think-tanks. The tax data may reveal interesting trends in the economy, which will be useful for the policy makers. Centre and states government may profitably use this for tax gap analysis to reduce non-compliance. A novel approach could be charted out to use data to facilitate shift from strong enforcement measures to prodding taxpayers towards greater compliance. For the captains of trade and industry, tax data could help them by providing illuminating insights on market trends. The roundtable concluded acknowledging the whole vistas of opportunities that have been created for policy formulations and after understanding taxpayersâ&#x20AC;&#x2122; behaviors.

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Hyderabad Oval Office, 18, iLabs Centre Hitech City, Madhapur Hyderabad - 500 081 Tel: + 91 40 6736 2000 Fax: + 91 40 6736 2200 Jamshedpur 1st Floor, Shantiniketan Building Holding No. 1, SB Shop Area Bistupur, Jamshedpur – 831 001 Tel: +91 657 663 1000 BSNL: +91 657 223 0441

Pune C-401, 4th floor Panchshil Tech Park Yerwada (Near Don Bosco School) Pune - 411 006 Tel: + 91 20 4912 6000 Fax: + 91 20 6601 5900

Kochi 9th Floor, ABAD Nucleus NH-49, Maradu PO Kochi - 682 304 Tel: + 91 484 304 4000 Fax: + 91 484 270 5393

Value addition in the tax ecosystem through data analytics

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