[School-of-data] Using public data to flag tax avoidance schemes?

[Jonathan Gray]

Tony Hirst and I drafted this piece yesterday, which we thought might be of
interest to some of you (also copied below):

http://blog.okfn.org/2013/07/11/using-public-data-to-flag-tax-avoidance-schemes/

J.
USING PUBLIC DATA TO FLAG TAX AVOIDANCE SCHEMES?

Today OpenCorporates <http://opencorporates.com/> added a new visualisation
tool<http://blog.opencorporates.com/2013/07/11/open-corporate-network-data-not-just-good-but-better/>
that
enables you to explore the global corporate networks of the six biggest
banks in the US.

The visualisation shows relationships between companies that are members of
large corporate groups.

You can hover over a particular company within a corporate group to
highlight its links with other companies that either control or are
controlled by the highlighted company. It also shows which companies are
located in countries commonly held to be tax havens.

[image: OpenCorporates control map
example]<http://opencorporates.com/viz/financial/index.html#goldman/ky>

As well as corporate ownership data, OpenCorporates also publishes a
growing amount of information detailing company directorships. Mining this
data can offer a complementary picture of corporate
groupings<http://blog.okfn.org/2013/07/11/using-public-data-to-flag-tax-avoidance-schemes/%E2%80%9Dhttp://blog.ouseful.info/2013/01/28/corporate-sprawl-sketch-trawls-using-opencorporates/%E2%80%9D>
.

The Offshore Leaks Database <http://offshoreleaks.icij.org/> from The
International Consortium of Investigative Journalists <http://www.icij.org/>,
released earlier this year, also contains information about “122,000
offshore companies or trusts, nearly 12,000 intermediaries …, and about
130,000 records on the people and agents who run, own, benefit from or hide
behind offshore companies”.

As you may have seen, we’ve recently been
thinking<http://blog.okfn.org/2013/06/25/what-data-needs-to-be-opened-up-to-tackle-tax-havens/>
about
how all of this publicly available information about corporate ownership
networks might be used to help identify potential tax avoidance schemes.

While the visualisation that OpenCorporates released today focuses on six
corporate networks, we’d be interested in seeing whether we might be able
to mine bigger public data sources to detect some of the most common tax
avoidance schemes.

As more and more corporate data becomes openly available, might we be able
to identify patterns within corporate groupings that could be indicative of
tax avoidance schemes? What might these patterns look like? To what extent
might you be able to use algorithms to flag certain corporate groupings for
further attention? And to what extent are others (auditors, national tax
authorities, or international fraud or corruption agencies) already using
algorithmic techniques to assist with the detection of such arrangements?

There are several reasons that using open data and publicly available
algorithms to detect potential tax avoidance schemes could be interesting.

Firstly, as tax avoidance is a matter of public concern arguably civil
society organisations, journalists and citizens should be able to explore,
understand and investigate potential avoidance, not just auditors and tax
authorities.

Secondly, we might get a sense of how prevalent and widespread particular
tax avoidance schemes are. Not just amongst high profile companies that
have been in the public spotlight, but amongst the many other tens of
millions of companies and corporate groupings that are publicly listed. The
combination of automated flagging and collaborative investigations around
publicly available data could be a very powerful one.

If you’re interested in looking into how data on corporate groupings might
be used to flag possible tax avoidance schemes, then you can join us
on the School
of Data discussion list<http://lists.okfn.org/mailman/listinfo/school-of-data>
.

-- 

Jonathan Gray

Director of Policy and Ideas  | *@jwyg <https://twitter.com/jwyg>*

The Open Knowledge Foundation <http://okfn.org/>
*

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