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Branka Hadji Misheva

About

Prof. Dr. Branka Hadji Misheva is a Professor in Applied Data Science and Finance at BFH, working on AI applications in finance, XAI methods, network models and fintech risk management. She holds a PhD in Economics and Management of Technology with a specific focus on network models as they apply to the operation and performance of P2P lending platforms, from the University of Pavia, Italy. She leads/co-leads many research and innovation projects among which the notable examples are:  


  • Two larger EU funded projects: Fintech-ho2020: A FINancial supervision and TECHnology compliance training programme and COST Action Finance & Artificial Intelligence for Finance. The budget that was associated for these projects was 2.5 mil CHF and 1 mil CHF, respectively.  

  • Two Innosuisse projects: (lead) Towards Explainable Artificial Intelligence and Machine Learning in Credit Risk Management [5], and (project partner) DataInc - Intelligent Data Integration and Cleaning. Both project jointly had a budget of 850,000 CHF. Both projects aim to provide state-of-art AI-based solutions to persisting problems in financial risk management (i.e. accurate and trustworthy assessment of credit risk and high level of data quality).  

  • An SNF Weave project on Network-based scoring models for P2P systems. The budget associated with this project is 350,000 CHF.  


She has furthermore participated in the acquisition of over 20 SNF, Innosuisse and EU projects and published a variety of papers related within the different research proposals Prof. Hadji Misheva is also research author of over 25 papers in the field of credit risk modeling, graph theory, predictive performance of scoring models, lead behavior in crypto markets and explainable AI models for credit risk management.

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Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Horizon Europe: Marie Skłodowska-Curie Actions. Neither the European Union nor the granting authority can be held responsible for them. This project has received funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101119635

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