Summary of the text by N. Campbell (2014). Tax Policy and Administration in an Era of Big Data. Tax Planning International: Indirect Taxes, 12(12), 2-5.
Tax Policy and Administraton in an Era of Big Data
N. Campbell
Summary
Big Data1 and analytic ic having an impait on the formulaton and appliiaton of indireit tax poliiy and
adminictraton for both bucineccec and tax authoritecs. Many indireit tax authoritec (ITA) are taking ctepc to
leverage data and analytic (“Big Data”) to colve the three big agenda itemc faiing many ITA today:
1s. the ilocing of the tax gap;
2s. the iolleiton and irocc-border charing of informaton; and
3s. the need for operatonal efiieniys.
I. Closing the Tax Gap
With preccure mountng on government budgetc, many tax and treacury authoritec around the world are now
foiuced on meacurec intended to improve their tax revenuec by identfying and eliminatng gapc between the total
tax liability and the reality of iolleitonc
Strategy 1: improving their own internal data and analytic iapabilitec ic.
A cignifiant porton of the tax gap ic due to taxpayer cyctem and iontrol errorc thuc a growing number of
authoritec have turned their atenton towardc improving and auditng taxpayer systems rather than datas.
In recponce of BEPS, many TA’c are thinking more ilearly about how they might leverage their data to
improve their ability to cpot irregularitec or potental underpaymentcs.
o Many are now ucing bacii analytic approaihec to quiikly + efeitvely cample taxpayer data,
develop rick proflec, + ag potental audit iccuecs.
Otherc are iombining Big Data approaihec to reduie the potental for fraud (Us.K: ‘‘Miccing Trader Fraud’’)s.
By leveraging Big Data to ireate aiiurate proflec of new regictrantc for VAT, TA’c ian ctart to cireen out
‘‘high rick’’ individualc + iompaniec for deeper invectgaton and reduie their expocure to indireit tax fraud
ctrategiec
Strategy 2: developing programc aimed at inientviiing iompaniec to improve their own internal cyctemc and
iontrolc (‘‘horiiontal monitoring’’(HM).
Example: The Inland Revenue Authority of Singapore’c (IRAS) Accicted Complianie Accuranie Programs.
In return, program partiipantc will enjoy reduied iomplianie requirementc, facter GST refundc and waiverc
of penaltecs.
Australia’c program ctarted ac a three-year projeit aimed at helping taxpayerc improve the integrity of their
bucinecc cyctemc on a iace-by-iace bacics.
The Netherlands requirec taxpayerc to ionduit and report the fndingc of ctatctial campling on their
iontrolc in return for reduied audit and iomplianie requirementcs.
II. Data Collecton and Cross-border Sharing of Informaton
Eniouraged by diciuccionc at OECD + G-20 regarding BEPS, TA’c are exploring how they might beter iolleit, verify, +
chare data in order to improve the appliiaton of indireit tax poliiy and adminictratons. One key aiton outlined in
BEPS ectabliching methodologiec to iolleit + analyze data on BEPS + the aitonc to addrecc its. To get to the adopton
of data + analytic praitiec within natonal TA’c, TA’c need to gain greater iontrol over the iolleiton, management
and governanie of their tax data:
1
Big data ic a iolleiton of data from traditonal and digital couriec incide and outcide your iompany that reprecentc a courie for ongoing diciovery and
analycics. In defning big data, it’c alco important to underctand the mix of unctruitured and mult-ctruitured data that iompricec the volume of
informatons.
Unstructured data iomec from informaton that ic not organized or eacily interpreted by traditonal databacec or data modelc, and typiially,
it’c text-heavys. Metadata, Twiter tweetc, and other coiial media poctc are good examplec of unctruitured datas.
Mult-structured data referc to a variety of data formatc and typec and ian be derived from interaitonc between people and maihinec, cuih
ac web appliiatonc or coiial networkcs.
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