Fraud Detection by Finding Patterns in the Dynamics of Shareholders Networks.

The current fight against fiscal fraud is confronted with a number of significant challenges, as fraudsters adopt ever growing complex structures and operate in an organized fashion. In this project we will investigate how the shareholder network structures change over time in legitimate versus fraudulent cases. To do so, we will apply data mining techniques on a unique dataset that we have obtained from a European tax administration, with data of company ownership networks from 2006 till today.

Keywords:FRAUD DETECTION, FISCAL FRAUD, NETWORKS, DATA MINING

Disciplines:Law