Predictors of Organized Crime and Subversion: a Machine Learning Approach
Abstract: The share of offences that can be related to organized crime and subversive activities is increasing. Organized crime engages in systematic violation of the law with serious effects on society and is able to cover these violations in a very effective way. The same is true for subversion, which can be seen as “the paralysis of the (regional) society”. Notwithstanding the serious negative effects of these activities, it is difficult to combat them as they are performed illegally and ‘under the shadow of the law’. In this paper, we disentangle patterns of organized crime and subversion on Dutch industrial areas. Based on patterns and best predictors, we develop a method with which policy makers can judge the level of criminal and subversive activities taking place in a certain area. This method provides an instrument, think of a ‘sliding scale’ or an ‘organized crime hot spot’ providing insights in the state of a certain industrial area. Besides data we scrape from the Internet (and eventually the Darknet), we use all types of available data and information related to different aspects of organized crime and subversion. For example information from a suspicious person or firm; all sorts of files, for example on ‘clients’; annual reports and balance sheets; tax information; and registries from the Chamber of Commerce. In theory, also social media data and the browse/search behavior of a suspicious person or firm can be analyzed with respect to interesting trends and patterns. To mine, cluster, and analyze these structured as well as unstructured data sources, we mainly use techniques related to natural language processing and machine learning techniques. Based on all the patterns found, we can derive the best predictors for organized crime and subversion on Dutch industrial areas. This provides a method and an instrument with which policy makers can judge the level of criminal and subversive activities that take place in a certain area.