FINancial Supervision and TECHnological Compliance training programme – FIN-TECH

 

Project no.: 825215

Project description:

Financial Technology (Fin Tech) means “Technology enabled financial innovations”. There is a strong need to improve the competitiveness of the European FinTech sector, creating a common regulatory field across all countries which, while encouraging innovations in Big Data analytics, Artificial Intelligence, and Blockchain technologies, can correctly measure their risks. Europe is a broad mosaic of regulatory landscapes. Policy makers and regulators must move quickly to establish a new regulatory framework for emerging fintechs, without stifling their economic potential.
The FIN-TECH project, under the EU’s Horizon2020 funding scheme, aims to create a European training programme, aimed at providing shared risk management solutions that automatize compliance of Fintech companies (RegTech) and, at the same time, increases the efficiency of supervisory activities (SupTech). In other words, we aim at connecting FINancial supervision with TECHnological compliance, from which the acronym of the project: FIN-TECH.
The project programme was built jointly by 25 university (from Lithuania only FMNS KTU) and FinTech partners that are established experts in fintech risk management, that will share knowledge with with regulators, supervisors and fintech associations and hubs from all 28 European Union countries, plus Switzerland.
The goals of the project will be achieved through research activity in risk management models for Big data analytics, AI and Blockchain applications to finance, discussed in three different research workshops; two levels of knowledge exchange sessions: a training level, run at the location of the involved supervisor in each of the 29 countries, to achieve uniformity across Europe; a coding level, centralised at the location of six different fintech hubs.

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Project funding:

The EU Framework Programme for Research and Innovation “Horizon 2020”


Project results:

In general, from all activities three groups of results are planned: repositories, coding environment and web pages.

For Artificial Intelligence, Big Data Analytics and Blockchain Research deliverables are:
– Repository of research papers by universities;
– Repository of white papers by fintechs;
– Repository of policy impact evaluation papers drafted by regulatory partners.
The training hubs will have output on:
– Repository of syllabus and slides for big data analytics;
– Repository of syllabus and slides for artificial intelligence;
– Repository of syllabus and slides for blockchain;
The coding lab during project will deliver:
– Launch of research and development environment;
– Repository for coding session materials (syllabus, scripts, datasets) for big data analytics;
– Repository for coding session materials (syllabus, scripts, datasets) for artificial intelligence;
– Repository for coding session materials (syllabus, scripts, datasets) for blockchain.
For dissemination of results above web site and social media channels will be established.

Period of project implementation: 2019-01-01 - 2020-12-31

Project coordinator: University of Pavia

Project partners: Humboldt University of Berlin, Zurich University of Applied Sciences, Panteion University of Social and Political Sciences, Kaunas University of Technology, University College London, Private company "modeFinance", Firamis GmbH, INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Paris Pantheon-Sorbonne University, Politecnico di Milano, University College Dublin, University of Luxembourg, Jožef Stefan Institute, University of Warsaw, University of Rijeka, Masaryk University, University of Tampere, The Bucharest University of Economic Studies, The Complutense University of Madrid, University of Economics in Bratislava, B-Hive Europe, University of Economics - Varna, Vienna University of Economics and Business

Head:
Audrius Kabašinskas

Duration:
2019 - 2020

Department:
Department of Mathematical Modelling, Faculty of Mathematics and Natural Sciences

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