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Fraud detection in financial networks

  • Host institution: WU Vienna University of Economics and Business, Austria (WWU)

  • Starting month: M3

  • Duration: 36 months

  • Pillar 1: Introduction to Blockchain applications in finance (Humboldt University of Berlin, 4 ECTs), Work Package 4

  • Work Packages: WP4, WP6, WP7, WP8


Detecting fraud is currently one of the most important topics in Finance. However, it is also one of the most complex, given that fraudsters typically represent and generate a highly dynamic system, requiring that the boundaries and objectives of any system designed to detect and reduce fraud be constantly adapted to new extrinsic structures. This enables the definition of not only a static fraud detection system, but also a dynamic AI learning system, particularly in relation to network analysis.

Expected Results

On a meta-level, a set of Machine Learning and Artificial Intelligence models will be defined to enable a research-based approach that can be applied directly in financial institutions. The models are defined in such a way that the outcomes of the learning process within the institutions can be used to define and design new algorithms from a scientific standpoint. The work on network algorithms during the process of designing Machine Learning environments, will result in the publication of seminal papers.

Planned Secondments

  • Raiffeisen Bank International (RAI), Dr. Stefan Theußl, M6, 18 months, research exposure in a global business environment

  • European Central Bank (ECB), Dr. Lukasz Kubicki, M27, 4 months, exposure to globally leading central bank, research training on EU principles, supervision

Planned Timetable

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