Artificial Intelligence and Big Data in Fraud Analytics:Identifying the Main Data Protection Challenges for Public Administrations

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

Modelling e-participation implementation:A network-based approach for online and offline participation

E-participation consists of several phases such as planning, implementation and evaluation. However, when representing this process, the implementation phase tends to be considered as a single block (the so-called "black-box"). This becomes a problem when the implementation combines offline and online methods, as it requires a detailed characterization and representation of all elements involved. In this paper we tackle this issue by proposing a network-based model to describe these methods. This choice is motivated by the fact that network models allow to better describe the distributed nature of these activities. To build this model we make use of the theory in Social Networks Analysis (SNA) to represent the main interactions between all actors involved. To asses the reliability and added value of the presented model, this approach is applied to four different use cases that showcase various combinations of online and offline participation methods. The results of these use cases show the great potential of the network-based model as a tool for designing, comparing and evaluating different types of implementations. Namely, the visualization of the model allows to asses the level of participation, the role of the different actors and how different instruments are combined

Modelling e-participation implementation:A network-based approach for online and offline participation

E-participation consists of several phases such as planning, implementation and evaluation. However, when representing this process, the implementation phase tends to be considered as a single block (the so-called "black-box"). This becomes a problem when the implementation combines offline and online methods, as it requires a detailed characterization and representation of all elements involved. In this paper we tackle this issue by proposing a network-based model to describe these methods. This choice is motivated by the fact that network models allow to better describe the distributed nature of these activities. To build this model we make use of the theory in Social Networks Analysis (SNA) to represent the main interactions between all actors involved. To asses the reliability and added value of the presented model, this approach is applied to four different use cases that showcase various combinations of online and offline participation methods. The results of these use cases show the great potential of the network-based model as a tool for designing, comparing and evaluating different types of implementations. Namely, the visualization of the model allows to asses the level of participation, the role of the different actors and how different instruments are combined