Data science for assessing possible tax income manipulation: The case of Italy

TL;DR

This study applies Benford's law to Italian regional tax data from 2007-2011 to detect potential income manipulation, revealing unexpected disparities that challenge common assumptions about financial shadow regions.

Contribution

It introduces a data science approach using Benford's law to identify possible tax income manipulation in Italian regions, providing new insights into regional fiscal disparities.

Findings

Disparities found in regional tax data suggest possible manipulation.

Unexpected regions show anomalies contrary to traditional beliefs.

Benford's law effectively highlights regions with irregular fiscal patterns.

Abstract

This paper explores a real-world fundamental theme under a data science perspective. It specifically discusses whether fraud or manipulation can be observed in and from municipality income tax size distributions, through their aggregation from citizen fiscal reports. The study case pertains to official data obtained from the Italian Ministry of Economics and Finance over the period 2007-2011. All Italian (20) regions are considered. The considered data science approach concretizes in the adoption of the Benford first digit law as quantitative tool. Marked disparities are found, - for several regions, leading to unexpected "conclusions". The most eye browsing regions are not the expected ones according to classical imagination about Italy financial shadow matters.