Prof. Dr. Branka Hadji Misheva

Professor in Applied Data Science & Finance

Lead of the Research Group Applied AI Research and Solutions (AIRS)

Project Lead: MSCA Industrial Doctoral Network on Digital Finance

Project Lead: Revolutionizing Compliance and Risk Management through Multi-Agent Generative AI (with Velaw AG Zurich)

Project Lead: Machine Learning Ensemble Modelling Project to Predict Unemployment Duration (with the Canton of Bern’s Office of Unemployment Insurance, AVA)

Project Lead: MOJO (Map for Job Offering) - data-driven platform integrating job market data with geospatial information (with the Canton of Bern’s Office of Unemployment Insurance, AVA)

Project Partner: MSCA Doctoral Network on Training and Innovation in Reliable and Efficient Chip Design for Edge AI (TIRAMISU)

Project Partner: AI Beacon: Enhancing teaching quality with AI-enhanced evaluation tools

Project Lead for BFH: DataInc - Intelligent Data Integration and Cleaning with Transformers

BSc Business Administration

BSc International Business Administration

BSc Business Information Technology

BSc Digital Business & AI

MSc Business Administration

MSc Digital Business Administration

MSc Entrepreneurship & Business Innovation

Artificial Intelligence for Finance

AI Applications in Industry

Advanced Analytics for Decision-Making

Data-Driven Visualization and Decision Making

Digital Banking and Finance

CFA Research Challenge

Applied Artificial Intelligence for finance, risk management, compliance, education, and public-sector decision support, including predictive modelling, AI-driven forecasting, and clustering.

Generative AI and transformer-based solutions for industry, with a focus on large language models, multi-agent systems, document understanding, regulatory technology, compliance automation, and human-AI interaction.

Explainable AI as a core research area, especially explainability for financial time series, post-hoc explanation methods, counterfactual explanations, causal explainability, and stakeholder-specific explanations for high-stakes AI systems.

Network theory, graph-based learning, knowledge graphs, and network-based AI systems for credit risk management, fintech risk analysis, and financial decision-making.

AI in finance and fintech, including ML-based credit scoring, peer-to-peer lending, portfolio optimization, sustainable finance, algorithmic fairness, trustworthy AI, and regulatory alignment.

My main priority is to advance Artificial Intelligence that is useful, trustworthy, and serves the general good. I aim to develop AI systems that create measurable value for society, industry, and public institutions while remaining transparent, accountable, and aligned with human needs.

Hadji Misheva, B. (2025). The Potential of AI in Traditional Finance (L. Moretti, L. Rinaldi, & P. Schlosser, Eds.). European University Institute (EUI). https://doi.org/10.24451/dspace/12237

Hadji Misheva, B., & Osterrieder, J. R. (2023). Wie Geschlechtervielfalt und KI die Fintech-Branche verändern. In SocietyByte – Wissenschaftsmagazin der Berner Fachhochschule. Berner Fachhochschule, Wirtschaft. https://doi.org/10.24451/arbor.20103

Giudici, P., Hadji Misheva, B., & Spelta, A. (2020). Network based credit risk models. In Quality Engineering (Vol. 32, Issue 2). Taylor & Francis. https://doi.org/10.24451/arbor.17297

Giudici, P., Hadji Misheva, B., & Spelta, A. (2019). Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms. In Frontiers in Artificial Intelligence (Vol. 2). Frontiers Research Foundation. https://doi.org/10.24451/arbor.17298

Ahelegbey, D. F., Giudici, P., & Hadji Misheva, B. (2019). Latent factor models for credit scoring in P2P systems. In Physica A: Statistical Mechanics and its Applications (Vol. 522). Elsevier. https://doi.org/10.24451/arbor.17301

Osterrieder, J. R., Hadji Misheva, B., & Machado, M. (2023). Digital Finance: Reaching New Frontiers [version 1; peer review: 2 approved]. In Open Research Europe (Vol. 3, Issue 38). F1000 Research. https://doi.org/10.24451/arbor.20558

Ahelegbey, D. F., Giudici, P., & Hadji Misheva, B. (2019). Factorial Network Models to Improve P2P Credit Risk Management. In Frontiers in Artificial Intelligence (Vol. 2). Frontiers Research Foundation. https://doi.org/10.24451/arbor.17302

Chen, Y., Giudici, P., Hadji Misheva, B., & Trimborn, S. (2020). Lead Behaviour in Bitcoin Markets. In Risks (Vol. 8, Issue 1). MDPI. https://doi.org/10.24451/arbor.17295

Lyócsa, Š., Vašaničová, P., Hadji Misheva, B., & Vateha, M. D. (2022). Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets. In Financial Innovation (Vol. 8, Issue 1). Springer. https://doi.org/10.24451/arbor.17290

Baals, L. J., Liu, Y., Osterrieder, J. R., & Hadji Misheva, B. (2024). Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning. In Expert Systems with Applications (Vol. 252). Elsevier. https://doi.org/10.24451/arbor.21961

Baals, L. J., Osterrieder, J. R., Hadji Misheva, B., & Liu, Y. (2024). Towards a new PhD Curriculum for Digital Finance. In Open Research Europe (Vol. 4, Issue 7). F1000 Research. https://doi.org/10.24451/arbor.22019

45957

Baals, L. J., Liu, Y., Osterrieder, J. R., & Hadji Misheva, B. (2024). Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics. In Finance Research Letters (Vol. 63). Elsevier. https://doi.org/10.24451/arbor.21851

46759

Posth, J.-A., Kotlarz, P., Hadji Misheva, B., Osterrieder, J., & Schwendner, P. (2021). The Applicability of Self-Play Algorithms to Trading and Forecasting Financial Markets. In Frontiers in Artificial Intelligence (Vol. 4). Frontiers Research Foundation. https://doi.org/10.24451/arbor.17293

Hadji Misheva, B., Hirsa, A., Osterrieder, J., Kulkarni, O., & Fung Lin, S. (2021). Explainable AI in Credit Risk Management. In SSRN. Elsevier. https://doi.org/10.24451/arbor.17292

Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T., & Osterrieder, J. (2021). Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey. In Frontiers in Artificial Intelligence (Vol. 4). Frontiers Research Foundation. https://doi.org/10.24451/arbor.17289

Liu, Y., Osterrieder, J. R., Hadji Misheva, B., Koenigstein, N., & Baals, L. J. (2023). Navigating the Environmental, Social, and Governance (ESG) landscape: constructing a robust and reliable scoring engine - insights into Data Source Selection, Indicator Determination, Weighting and Aggregation Techniques, and Validation Processes for Comprehensive ESG Scoring Systems. In Open Research Europe (Vol. 3). F1000 Research. https://doi.org/10.24451/arbor.20516

Cetintav, B., Silva, C., Hadji Misheva, B., Mare, C., Tanda, A., & Dias, J. (2024). Technical Report On Explainability Of Ai (XAI): For Non-expert Users. SSRN. https://doi.org/10.24451/dspace/12222

de Meer Pardo, F., Lehmann, C., Gehrig, D., Nagy, A., Braschler, M., Stockinger, K., Hadji Misheva, B., & Nicoli, S. (2025). GraLMatch : matching groups of entities with graphs and language models. In Open Proceedings: 28th International Conference on Extending Database Technology (EDBT), Barcelona, Spain, 25-28 March 2025. https://doi.org/10.24451/arbor.22168

MC member and Scientific Grant Holder of the COST FinAI Action (2022-2024)

Data Innovation Alliance - Academic lead of the expert group "AI in Finance and Insurance" (2022-2025)

Researcher at the Safe AI Lab of University of Pavia Italy

Member of COST Action: Text, functional and other high-dimensional data in econometrics: New models, methods, applications