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Machine Learning for Digital Finance

  • Host institution: University of Twente, The Netherlands (UTW)

  • Starting month: M9

  • Duration: 36 months

  • Pillar 1: Introduction to Blockchain applications in finance (Bucharest University of Economic Studies, 4 ECTs), Work Package 4

  • Work Packages: WP4, WP6, WP7, WP8

Objectives

The project aims to design and enhance the functionality and user interaction of applications that model Early Warning Signals.
It will develop models and frameworks that leverage big data analytics, focusing on refining usability to improve clarity for various
stakeholders. This project also seeks to implement and evaluate explainability methods to determine their impact on the decision-makingprocesses of internal customers within financial institutions. The goal is to enhance their ability to provide accurate feedback and reporting and to facilitate implementing necessary corrections

Expected Results

The project will develop and integrate advanced explainability features into various Early Warning Signal (EWS)
applications, aiming to make analytical results and sentiment assessments more intuitive and actionable for users. Specifically, the project expects to (i) Enhance user understanding and interaction with big data outputs, promoting broader and more effective use across different business areas within financial institutions and regulatory bodies. This will involve improvements to make sentiment analysis results more transparent, enabling supervisors to more effectively assess and verify these insights; (ii) Enables te assessment of the impact of these enhancements on the decision-making processes of internal stakeholders, with the goal of establishing a feedback loop that encourages continuous improvement of the platforms.

Planned Secondments

  • European Central Bank (ECB). Lukasz Kubicki, M21, 12 months, exposure to globally leading central bank, research training on EU principles, supervision

  • Fraunhofer (FRA). Prof. Dr. Ralf Korn, M33, 6 months, applied industry-research, contribute to multiple projects on blockchain and decentralized finance

  • Swedbank (SWE). Prof. Dr. Tadas Gudaitis, M39, 6 months, ESG and credit score modelling

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Planned Timetable

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