Profiling tax and financial behaviour with big data under the GDPR. Issue 3 (May 2019)

• Record Type: Journal Article
• Authors: Politou, Eugenia; Alepis, Efthimios; Patsakis, Constantinos
• Is Part Of: Computer law & security review. Volume 35:Issue 3(2019)
• Page Start: 306
• Page End: 329
• Publication Date: 2019-05
• Subjects: Computers -- Law and legislation -- Periodicals; Computer security -- Law and legislation -- Periodicals; Electronic commerce -- Law and legislation -- Periodicals; Data protection -- Law and legislation -- Periodicals; Computer security -- Law and legislation; Computers -- Law and legislation; Data protection -- Law and legislation; Electronic commerce -- Law and legislation; Periodicals; 343.0999
• DOI: 10.1016/j.clsr.2019.01.003
• Languages: English
• ISSNs: 2212-473X

Abstract

Abstract: Big data and machine learning algorithms have paved the way towards the bulk accumulation of tax and financial data which are exploited to either provide novel financial services to consumers or to augment authorities with automated conformance checks. In this regard, the international and EU policies toward collecting and exchanging a large amount of personal tax and financial data to facilitate innovation and to promote transparency in the financial and tax domain have been increased substantially over the last years. However, this vast collection and utilization of "big" tax and financial data raise also considerations around privacy and data protection, especially when these data are fed to clever algorithms to build detailed personal profiles or to take automated decisions which may exceptionally affect people's lives. Ultimately, these practices of profiling tax and financial behaviour provide fertile ground for discriminating processing of individuals and groups. In light of the above, this paper aims to shed light on the following four interdependent and highly disputed areas: firstly, to review the most well-known profiling and automated decision risks emerged from big data technology and machine learning algorithmic processing as well as to analyse their impact on the tax and financial privacy rights through their immense profiling practices; secondly, to document the current EU initiatives toward financial and tax transparency, namely the AEOI, PSD2, MiFID2, and data retention policies, along with their implications for personal data protection when used for profiling and automated decision purposes; thirdly, to highlight the way forward for mitigating the risks of profiling and automated decision in the big data era and to investigate the protection of individuals against these practices in the light of the new technical and legal frameworks; in this respect, we finally delve into the regulatory EU efforts towards fairer and accountable profiling and automated decision processes, and in particular we examine the extent to which the GDPR provisions establishes a protection regime for individuals against advanced profiling techniques, enabling thus accountability and transparency.