Digital Finance MSCA

MSCA Industrial Doctoral Network on Digitial Finance - Reaching New Frontiers

Introduction and Timeliness

A competitive European financial sector is vital for the modernisation of the European economy across sectors and to turn Europe into a global digital player. The term Digital Finance refers to the rapid development of new technology, goods, and business models that have taken place in recent years.

We have identified the five most pertinent areas within this domain: Towards a European financial data space. Artificial intelligence for financial markets. Towards explainable and fair AI-generated decisions. Driving digital innovations with Blockchain applications. Sustainability of Digital Finance.

What they have in common: They are all key strategic priorities of the European Commission over the next five years. They contribute to the UN Sustainable Development Goals. Europe must invest significantly in them over the next five years if it is to remain globally competitive. They are characterised by a significant shortage of skilled labour. Initial progress has been made in academia, but there are still numerous unanswered research questions. They have the potential to revolutionise the Finance industry with new technologies, business models, and products, while strengthening the resilience of Europe. They are the foundation for a new generation of PhD candidates and training in Digital Finance.

Considering these developments across industries and within the financial sector, it is absolutely essential to work on those research topics now and to train new PhD graduates, because: Digital Finance has already changed the way the Finance industry works. To deal with the realities of academia and industry, PhD graduates in Finance will be required to acquire the skill set of Digital Finance. There is a substantial research gap in academia that needs to be resolved now by academics and a new generation of Digital Finance PhDs to keep Europe's Finance industry competitive.

Network

For this purpose, we have gathered an internationally recognized network consisting of eight leading European Universities (WU Vienna, HU Berlin, University of Twente, Bucharest University of Economic Studies, Babes-Bolyai University, Bern Business School, Kaunas University of Technology and University of Naples), all ranked among the top 200 universities globally in their fields, four major international corporations (Deloitte, Swedbank, Intesa Sanpaolo and Raiffeisen Bank) with a significant R&D presence across Europe, two SMEs (Cardo AI and Royalton Partners) being some of the most innovative ones in their field, three large and internationally renowned research centres (ARC Greece, EIT Digital and Fraunhofer Institute) and the European Central Bank, as one of the seven principal decision-making bodies of the European Union and the Euratom as well as one of the world's most important central banks. The results of the research carried out within DIGITAL are of substantial interest to three leading European-wide research networks that our members either lead or serve on the management committee for: COST Action CA19130 Fintech and AI in Finance (240 researchers across 39 European countries), European Consortium of Mathematics for Industry (200 researchers across Europe) and the European Consortium of Innovative Universities (13 European Universities). It is only through a network that incorporates the expertise of all distinct shapers of the financial industry (technology experts, academics, Fintechs, domain experts, incumbents, regulators, civil society) that we can see a comprehensive shift towards innovation and digitalization of a sector that is notoriously averse to change.

Objectives

Today, Digital Finance does not exist as a standalone research discipline, despite many research gaps, the EU’s key strategic priorities and the urgent needs from industry. DIGITAL will overcome this and significantly advance the methodologies and business models for Digital Finance through the use of five interconnected and coherent research objectives and a total of seventeen Early Stage Researchers (ESRs) hired by the beneficiaries, both from academia and industry. The main objectives are: Towards a European financial data space. Ensure sufficient data quality to contribute to the EU's efforts of building a single digital market for data (WP 1). Artificial intelligence for financial markets. Address deployment issues of complex artificial intelligence models for real-world financial problems (WP 2). Towards explainable and fair AI-generated decisions. Validate the utility of state-of-the-art explainable artificial intelligence (XAI) algorithms to financial applications and extend existing frameworks (WP 3). Driving digital innovations with Blockchain applications. Design risk management tools concerning the applications of the Blockchain technology in Finance (WP 4). Sustainability of Digital Finance. Simulate financial markets and evaluate products with a sustainability component (WP 5).

Research Training for Digital Finance

The network will specifically train young researchers in R&D topics that cover the multiple disciplines required in the rapidly evolving field of Digital Finance substantially going beyond the traditional Finance PhD education in a wide range of inter-sectoral applications: data quality, Artificial Intelligence (AI) and Machine Learning (ML), Explainability of AI (XAI), Blockchain applications and sustainable finance; all of which are required for a wide range of industrial (financial products, risk management, customer-centric products, enhanced processes, and improved services) and scientific (new AI techniques, new business models, and enhanced modelling) applications, necessitating new scientific insight, new training courses, and future specialists in the field.

Need for an Industrial Doctoral Network

The European Finance industry needs to compete on a global scale. To overcome key hurdles which financial service companies will face in the near future, they will have to find answers to (WEF 2020): Data quality issues related with the increasing dimensionality of financial data. Deployment issues of complex models in real-world applications. Deficits in trust and user adoption of AI-supported financial products. Potential data or algorithmic bias inherent in AI models. Labour shortage: AI leaders overwhelmingly argue that access to talent represents a key obstacle to the digitization efforts in finance, as more sophisticated solutions demand different employee capabilities. All of those hurdles towards scientific, societal and economic/ technological impact will be solved in DIGITAL.

Meetings

09.08.2023 Kick-Off meeting

• materials

21.08.2023 Thematic session on Doctoral Training

• materials

Important links

Network =

• Network Structure
• Verification of the network

Overview of the Action =

• Action Overview
• Expected contributions per partner

Early Stage Researchers

• Overview of ESRs
• Timeframe overview of ESRs