August 16, 2021 Conference paper Open Access

Tombal Thomas; Simonofski Anthony

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Tombal Thomas</dc:creator>
  <dc:creator>Simonofski Anthony</dc:creator>
  <dc:date>2021-08-16</dc:date>
  <dc:description>Fraud Analytics refers to the use of Big Data Analytics to detect fraud. Numerous techniques, from data mining to social network analysis, are applied to detect various types of fraud. While Fraud Analytics offers the promise of more efficiency in fighting fraud, it also raises data protection challenges for public administrations. Indeed, whether they use traditional or advanced techniques, administrations consistently use more and more data to deliver public services. In this regard, they often need to process citizen’s personal data. Therefore, administrations have to consider data protection legal requirements. While these legal requirements are well documented, the concrete way in which they have been integrated by public administrations in their Fraud Analytics process remains unexplored. Accordingly, we examine two case studies within the Belgian Federal administration (the detection of tax frauds and of social security infringements), in order to shed light on the main data protection challenges faced by public administrations in this regard.</dc:description>
  <dc:identifier>https://zenodo.org/record/5205519</dc:identifier>
  <dc:identifier>10.5281/zenodo.5205519</dc:identifier>
  <dc:identifier>oai:zenodo.org:5205519</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.5205518</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/dfp17</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Fraud Analytics</dc:subject>
  <dc:subject>Public Administration</dc:subject>
  <dc:subject>Data Protection</dc:subject>
  <dc:subject>Challenges</dc:subject>
  <dc:title>Artificial Intelligence and Big Data in Fraud Analytics: Identifying the Main Data Protection Challenges for Public Administrations</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>