F. Bonchi, F. Giannotti, G. Mainetto and D. Pedreschi, “A Classification-Based Methodology for Planning Auditing Strategies in Fraud Detection,” KDD’99 Conference Proceeding of the 5th ACM SIGKDD, San Diego, CA, 15-18 August 1999, pp. 175-184.

has been cited by the following article:

• TITLE: Taxpayers Fraudulent Behavior Modeling The Use of Datamining in Fiscal Fraud Detecting Moroccan Case

  AUTHORS: Farid Ameur, Mohamed Tkiouat

  KEYWORDS: Tax Fraud; Tax Evasion, Data Mining; Knowledge Discovery in Databases (KDD); Fiscal Policy; Fiscal Reform; Fiscal Control; Fiscal Justice; Taxpayers; Tax Administration

  JOURNAL NAME: Applied Mathematics, Vol.3 No.10, October 12, 2012

  ABSTRACT: The fraudulent behavior of taxpayers impacts negatively the resources available to finance public services. It creates distortions of competition and inequality, harming honest taxpayers. Such behavior requires the government intervention to bring order and establish a fiscal justice. This study emphasizes the determination of the interactions linking taxpayers with tax authorities. We try to see how fiscal audit can influence taxpayers’ fraudulent behavior. First of all, we present a theoretical study of a model pre established by other authors. We have released some conditions of this model and we have introduced a new parameter reflecting the efficiency of tax control; we found that the efficiency of a fiscal control have an important effect on these interactions. Basing on the fact that the detection of fraudulent taxpayers is the most difficult step in fiscal control, We established a new approach using DATA MINING process in order to improve fiscal control efficiency. We found results that reflect fairly the conduct of taxpayers that we have tested based on actual statistics. The results are reliable.