Complex network tools to enable identification of a criminal community

Abstract

Retrieving criminal ties and mining evidence from an organised crime incident, for example money laundering, has been a difficult task for crime investigators due to the involvement of different groups of people and their complex relationships. Extracting the criminal associations from enormous amounts of raw data and representing them explicitly is tedious and time consuming [1, 6, 13]. A study of the complex network literature reveals that graph-based detection methods have not, as yet, been used for money laundering detection. In this research, I explore the use of complex network analysis to identify the communication associations of money laundering criminals, that is, the important people who communicate between known criminals and the reliance of the known criminals on the other individuals in a communication path.