  Tax evasion is the illegal evasion of taxes by individuals, corporations, and
trusts. The revenue loss from tax avoidance can undermine the effectiveness and
equity of the government policies. A standard measure of tax evasion is the tax
gap, that can be estimated as the difference between the total amounts of tax
theoretically collectable and the total amounts of tax actually collected in a
given period. This paper presents an original contribution to bottom-up
approach, based on results from fiscal audits, through the use of Machine
Learning. The major disadvantage of bottom-up approaches is represented by
selection bias when audited taxpayers are not randomly selected, as in the case
of audits performed by the Italian Revenue Agency. Our proposal, based on a
2-steps Gradient Boosting model, produces a robust tax gap estimate and, embeds
a solution to correct for the selection bias which do not require any
assumptions on the underlying data distribution. The 2-steps Gradient Boosting
approach is used to estimate the Italian Value-added tax (VAT) gap on
individual firms on the basis of fiscal and administrative data income tax
returns gathered from Tax Administration Data Base, for the fiscal year 2011.
The proposed method significantly boost the performance in predicting with
respect to the classical parametric approaches.
