Security and fraud in cross-border trade and supply chains are key priorities for the EU customs administrations due to the increasing risk of transnational crime and terrorism and the e-commerce-driven growth of customs declarations. Hence, EU customs administrations have to rapidly increase their capability to search for more accurate data sources to better assess these risks and increase their inspection hit rate. To address this challenge, PROFILE seeks to accelerate the uptake of state-of-the-art data analytics and incorporation of new data sources for more effective and efficient European customs risk management. The project provides tailored solutions, that build on modern methods in machine learning, graph-based analytics, and natural language processing, to help targeting officers and strategic analysts to collect and organise unstructured data, data-mine large datasets, apply semi-supervised machine learning that utilises feedback of control results, and to visualize complex data sets. PROFILE enables customs-to-customs systematic sharing of Entry Summary Declarations and other risk-relevant information through the EU-wide PROFILE Risk Data Sharing Architecture (RDSA). The project also connects national customs risk management systems to logistics Big Data of INTTRA and the Universal Postal Union (UPU) and provides customs an improved access to online data, especially valuation-relevant data of e-commerce sites. PROFILE also strengthens cooperation and data exchange among customs and other competent authorities. Better access to data, customised state-of-the-art data analytics, and stronger cooperation will provide the customs an enhanced 360º view on cross-border cargo flows. With PROFILE solutions, customs administration can increase substantially the hit rate of inspections and their capacity to cope with transnational crime, terrorism, and the dramatic e-commerce-driven growth of customs declarations.
Publications
year
authors and title
journal
last update
List of publications.
2019
Boriana Rukanova, Yao-hua Tan Digital Trade Infrastructures and Big Data Analytics: The concept of Value as a Linking Pin published pages: , ISSN: , DOI:
2020-03-05
2019
Boriana Rukanova, Yao-Hua Tan, Micha Slegt, Marcel Molenhuis, Ben van Rijnsoever, Krunoslav Plecko, Bora Caglayan, Gavin Shorten Value of Big Data Analytics for Customs Supervision in e-Commerce published pages: , ISSN: , DOI:
2020-03-05
Are you the coordinator (or a participant) of this project? Plaese send me more information about the "PROFILE" project.
For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.
Send me an email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.
Thanks. And then put a link of this page into your project's website.
The information about "PROFILE" are provided by the European Opendata Portal: CORDIS opendata.
More projects from the same programme (H2020-EU.3.7.3.)
PROFILE (2018)
Data Analytics, Data Sources, and Architecture for Upgraded European Customs Risk Management