Chair: COST Action CA19130 - Fintech and Artificial Intelligence in Finance
Joerg Osterrieder is chairing the COST Action CA19130. The chair will be in charge of the monitoring, coordination, implementation, and communication of measures decided by the COST action. He also supervises its financial management.
Factsheet
- Schools involved Business School
- Institute(s) Institute for Applied Data Science & Finance
- Research unit(s) Finance, Accounting and Tax
- Funding organisation Europäische Union
- Duration (planned) 01.06.2022 - 30.09.2024
- Head of project Prof. Dr. Jörg Robert Osterrieder
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Project staff
Prof. Dr. Jörg Robert Osterrieder
Prof. Dr. Branka Hadji Misheva
Yiting Liu
Lennart John Baals
Situation
The COST Action CA19130 will investigate AI and Fintech from three different angles: Transparency in FinTech, Transparent versus Black Box Decision-Support Models in the Financial Industry and Transparency into Investment Product Performance for Clients. The Action will bridge the gap between academia, industry, the public and governmental organisations by working in an interdisciplinary way across Europe and focusing on innovation. The financial sector is the largest user of digital technologies and a major driver in the digital transformation of the economy. Financial technology (FinTech) aims to both compete with and support the established financial industry in the delivery of financial services. Globally, more than $100 billion of investments have been made into FinTech companies and Artificial Intelligence (AI) since 2010, and continue growing substantially. In early 2018, the European Commission unveiled (a) their action plan for a more competitive and innovative financial market, and (b) an initiative on AI with the aim to harness the opportunities presented by technology-enabled innovations. Europe should become a global hub for FinTech, with the economy being able to benefit from the European Single Market.
Course of action
The key objectives are: 1. to improve transparency of AI supported processes in the Fintech space 2. to address the disparity between the proliferation in AI models within the financial industry for risk assessment and decision-making, and the limited insight the public has in its consequences by developing policy papers and methods to increase transparency 3. to develop methods to scrutinize the quality of products, especially rule-based “smart beta” ones, across the asset management, banking and insurance industries.
