Читать книгу Applied Modeling Techniques and Data Analysis 2 - Группа авторов - Страница 19

The learning scheme developed in this chapter is aimed at computing a risk factor for each taxpayer, optimizing the tax authorities’ audit processes, taking into account two competing needs: the profitability of each tax notice and the effective collectability of the additional requested taxes.

The ensemble model seems to tackle both of the above-mentioned issues quite well.

Given that the whole test set’s average claim is € 22,339, while the average collectable taxes are equal to € 10,194, our procedure increases the first figure by 1.17% (€ 26,219) and the second by 72% (€ 17,542).

With respect to the scenario in which only the first model is put in place, by developing the twofold selection process as described above, the presence of coercive procedures dramatically plummets from 70% to 25%. Moreover, the selection of not interesting taxpayers, while causing a drop in the average tax claim (from € 49,094 to € 26,219), is more than compensated by the procedure’s capability of efficiently collecting the additional taxes charged to the selected taxpayers (from € 12,187 to 1 € 17,542).

Table 1.5 summarizes the most significant results reached by the three models that have been built: the first model looks for interesting taxpayers; the second model is in search of solvent taxpayers; and the third model, called the ensemble model, is a combination of the first two. To better understand the figures referred to the models, the same information set is shown, related to the entire test set.

This result can be generalized, and the best selection strategy depends on our estimates of θ’ and θ” in the sets of the selected taxpayers.