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Short abstract:

This paper critically examines a fraud detection software used by an Austrian health insurance to detect bogus companies. What drives the software, what knowledge is created, and how that relates to wider transformations of the welfare state will be discussed.

Long abstract:

All over Europe, algorithmic systems are introduced to detect welfare fraud more efficiently. Within a wider context of new managerialism, these software tools conduct risk scoring and identify irregular patterns in large data-sets. This implies the incorporation of typical implications of data analytics –surveillance, bias, discrimination – into public sectors (Dubois et al. 2018). Moreover, social practices of welfare institutions change due to the implementation of algorithmic systems and the numeric “evidence” they create that case workers have to balance with their own “instincts” (Allhutter et al. 2021).

This paper critically examines a fraud detection software used by an Austrian health insurance to detect bogus companies (FWF I 6075). In Austria, a law has been passed in 2015 to fight welfare fraud and make its detection mandatory for public institutions. Accordingly, the software repurposes administrative data to conduct data forensics and help the financial police. Based on qualitative interviews with software developers, case workers, and representatives of the health insurance, our analysis will focus on the following questions: What drives the development of the software? What knowledge, or “evidence”, is created with the software and how is it framed in the context of fraud detection? How do larger socio-political transformations of the welfare state impact both practices and narratives? Theoretically, the paper draws on STS and knowledge production, critical algorithm studies, and public policy literature. To conclude, we discuss larger questions related to the automation of the welfare state and its impact on social practices, social roles, and social inequalities.