Title: Public services data analytics using artificial intelligence solutions derived from telecommunications systems

Authors: Christophe Gaie; Markus Mueck

Addresses: Direction Interministérielle du Numérique, 20 Avenue de Ségur, 75007 Paris, France ' Intel Deutschland GmbH, 85579 Neubiberg, Germany

Abstract: In the present article, we propose to bridge two worlds as we suggest to repurpose artificial intelligence solutions originally developed for telecommunications systems to the field of tax fraud detection by government administrations. The European Telecommunications Standards Institute (ETSI) has indeed recently published a related architecture Group Specification which introduces a number of building blocks and a general high level approach enabling automated data analysis and related decision-making in the context of large-scale communication systems. We illustrate how the available structure can be adapted to the needs and analysis of tax related data and which learning and decision-making processes may be applied for the extraction of information related to fraud and others. As a consequence, the analysis and processing of such data by public services are expected to be optimised considerably building on state of the art data analytics as originally developed in a different field.

Keywords: data analytics; fraud detection; artificial intelligence; tax recovery; optimisation; telecommunications.

DOI: 10.1504/IJBISE.2021.122747

International Journal of Business Intelligence and Systems Engineering, 2021 Vol.1 No.4, pp.283 - 299

Received: 30 Mar 2020
Accepted: 26 Jun 2020
Published online: 10 May 2022 *

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