  Mexico has kept electronic records of all taxable transactions since 2014.
Anonymized data collected by the Mexican federal government comprises more than
80 million contributors (individuals and companies) and almost 7 billion
monthly-aggregations of invoices among contributors between January 2015 and
December 2018. This data includes a list of almost ten thousand contributors
already identified as tax evaders, due to their activities fabricating invoices
for non-existing products or services so that recipients can evade taxes.
Harnessing this extensive dataset, we build monthly and yearly temporal
networks where nodes are contributors and directed links are invoices produced
in a given time slice. Exploring the properties of the network neighborhoods
around tax evaders, we show that their interaction patterns differ from those
of the majority of contributors. In particular, invoicing loops between tax
evaders and their clients are over-represented. With this insight, we use two
machine-learning methods to classify other contributors as suspects of tax
evasion: deep neural networks and random forests. We train each method with a
portion of the tax evader list and test it with the rest, obtaining more than
0.9 accuracy with both methods. By using the complete dataset of contributors,
each method classifies more than 100 thousand suspects of tax evasion, with
more than 40 thousand suspects classified by both methods. We further reduce
the number of suspects by focusing on those with a short network distance from
known tax evaders. We thus obtain a list of highly suspicious contributors
sorted by the amount of evaded tax, valuable information for the authorities to
further investigate illegal tax activity in Mexico. With our methods, we
estimate previously undetected tax evasion in the order of \$10 billion USD per
year by about 10 thousand contributors.
