Organised Crime Groups quickly adopt and integrate new technologies into their ‘modi operandi’ or build brand-new business models around them. More than 5,000 OCGs operating on an international level are currently under investigation in the EU, whereas document fraud, money laundering and the online trade in illicit goods and services are recognised as the engines of organised crime. Notably, goods and services offered on the Darknet are available to anyone, be it an individual user, an OCG or terrorist organisation. Almost all types of organised crime, criminals are deploying and adapting technology with ever greater skill and to ever greater effect, which represents the greatest challenge being posed to law enforcement authorities in the EU and globally at present time. Exponentially growing connectivity of all types of devices, including phones and appliances, is expected to amplify this concern, as criminals are already deploying techniques to exploit their vulnerabilities in order to gain access to personal and financial information or confidential business data. All of the above can have tremendous impact in the society. The effects on victims of terrorism and organised crime can be devastating and multiple and the consequences can be experienced at many interrelated levels - individually, collectively and societally. They often go far beyond the deaths, injuries, destruction and direct monetary losses and extend to the psychological effects on the population, the indirect financial costs, as well as the social and political impact. So, PREVISION’s objective is to provide LEAs with the capabilities of a) analysing and jointly exploiting multiple massive data streams, coming from online social networks, the open web, the Darknet, CCTV and video surveillance systems, traffic and financial data sources, and many more, b) semantically integrating them into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals, cybercriminal organisations and OCGs, c) predicting abnormal or deviant behaviour and radicalisation risks, based on sound predictive policing methods, underpinned by valid psychological, sociological and linguistic models, in conjunction with historical data patterns, d) performing dependable soft target risk assessment and cybercrime trend prediction at different timescales, e) becoming continuously more knowledgeable of the operations and activities of criminal organisations, by coupling the semantic technologies with deep and ensemble learning techniques, f) maintaining high situation awareness at all times by means of user-centred visual analytics and human-machine interaction techniques.
