Applied Modeling Techniques and Data Analysis 2

1Data Mining Application Issues in the Taxpayer Selection Process

This chapter provides a data analysis framework designed to build an effective learning scheme aimed at improving the Italian Revenue Agency’s ability to identify non-compliant taxpayers, with special regard to self-employed individuals allowed to keep simplified registers. Our procedure involves building two C4.5 decision trees, both trained and validated on a sample of 8,000 audited taxpayers, but predicting two different class values, based on two different predictive attribute sets. That is, the first model is built in order to identify the most likely non-compliant taxpayers, while the second identifies the ones that are are less likely to pay the additional due tax bill. This twofold selection process target is needed in order to maximize the overall audit effectiveness. Once both models are in place, the taxpayer selection process will be held in such a way that businesses will only be audited if they are judged as worthy by both models. This methodology will soon be validated on real cases: that is, a sample of taxpayers will be selected according to the classification criteria developed in this chapter and will subsequently be involved in some audit processes.

1.1. Introduction

Fraud detection systems are designed to automate and help reduce the manual parts of a screening/checking process (Phua et al. 2005). Data mining plays an important role in fraud detection as it is often applied to extract fraudulent ...