Abstract
The objective of the article is to analyze the transformation of information systems for controlling migration flows in the EU in cases where migration becomes a security issue, which intensified after the refugee crisis of 2015-2016. Special attention is paid to the use of big data analytics for forecasting migration. The research methods of the investigation were: the problem-chronological method to trace the evolution of the EU regulatory framework in the field of border control digitalization and an interdisciplinary approach to analyze the possibilities of using big data to forecast migration, with sociological and statistical methods to characterize migration flows. The article is based on the legal, statistical and fact sheets documents of the EU and the UN, and the publications of foreign and Russian researchers on this theme. The author considers the main characteristics and functional features of the Schengen information system and its components, identifies the limitations, highlightes the stages of transformation of the system and indicates the achieved and planned results. During the modification of the system, along with expanding the set of alerts, enhanced access is provided for EU agencies, including the possibility of making searches using fingerprints. A key function of the new system is to ensure the cooperation of nation states for prompt, confidential and efficient follow-up of cases, through the data exchange via a secure network. The author infers that the digital transformation of border control is aimed at creating a European-wide information infrastructure to investigate crimes and terrorist acts, generate alerts about the danger of their commission by “suspicious” persons and highlight groups of “unreliable” people whose stay in the EU is undesirable. At the same time, the target risk group is not limited to terrorists and criminals, but also includes illegal immigrants. In addition to border control information systems, big data analytics is used to monitor and forecast migration. Mobile phone call detail records, social media and Google trends become a leveraged data source to study mobility patterns and create profiles of potential migrants in real-time; the provided big data can also reflect emerging trends and support early warning mechanisms of easier monitoring of migration at national, subnational and local levels. Based on the analysis of the documents, statistical and sociological data, the author concludes that European migration policy has been advanced into a kind of risk management, in which, due to the process of digitalization, it is possible to profile groups of migrants and create series of “risk filters” serving to identify, isolate and deflect those whose presence in the EU should be limited.
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