Skills
Our current data-based research focuses on start-up financing, leadership and innovation management
Applied AI Research & Solutions
We leverage cutting-edge AI research to create innovative, end-to-end digital solutions. Our team collaborates with businesses, government agencies, and NGOs to unlock the full potential of their data—transforming information into actionable insights. We emphasize explainability, fairness, transparency, and usability, ensuring that solutions are not only clear but also easily understood and employed by all stakeholders. Additionally, we prioritize accountability, privacy, and robustness, ensuring that our AI systems are ethical, secure, and reliable. This holistic approach empowers organizations to make confident, data-driven decisions.
Applied data science
We are engaging methodologically with developments in the areas of machine learning and artificial intelligence. Our research projects look at innovative and disruptive business models in the context of advancing digitalisation. Several of our projects also focus on the provision of open data and replication studies.
Finance, accounting and tax
In the field of start-up financing, we focus on understanding product acceptance for digital products and services, and on financing new products via public funding platforms (crowdfunding).
COST-Action «Fintech and Artificial Intelligence in Finance»
Jörg Osterrieder from our institute is chairing the COST Action «Fintech and Artificial Intelligence in Finance» until 2024, the budget is managed by Branka Hadji Misheva. A COST Action is an interdisciplinary research network that brings researchers and innovators together to investigate a topic of their choice for 4 years. COST Actions are typically made up of researchers from academia, SMEs, public institutions and other relevant organisations or interested parties.
The main objectives of this COST Action are
- to improve the transparency of AI-supported processes in the FinTech sector
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to address the gap between the prevalence of AI modelling in the financial industry for risk assessment and decision making and the limited public understanding of the consequences by developing policy papers and methods to increase transparency.
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to develop methods to verify the quality of products, especially rules-based «smart beta» products, used in asset management, banking and insurance.
