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.


The Action 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 organizations by working in an interdisciplinary way across Europe and focusing on innovation.

The key objectives of the Action are:

  • to improve transparency of AI supported processes in the Fintech space
  • 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
  • to develop methods to scrutinize the quality of products, especially rule-based “smart beta” ones, across the asset management, banking and insurance industries.

Research Goals

  • Develop approaches to evaluate innovative financial services and their providers, especially in the FinTech domain, building on Machine Learning methods, focusing on prediction (early warning) of operational fragility, fraudulent and illegal behavior ranging from appropriation of loaned funds to money laundering activities.
  • Development of conceptual and methodological tools for establishing when black-box models are admissible and, to the extent possible, making them more transparent
  • Receive input from regulators and practitioners’ communities and validate results with regard to increasing transparency of artificial intelligence applications.
  • Development of methodologies for reducing the false discovery rate in financial research and applied financial investment management.
  • Disseminate to the public and share with regulators the results on investment product performance
  • Creation of the first European platform comparing the out-of-sample performance of banks’ investment products, insurance-linked investment products and asset management products available to the general public.