MSCA Work Packages

WP1 Towards a European financial data space

• Lead Beneficiary: Babes-Bolyai University.
• Researchers involved: See link
  • Babes-Bolyai University (Lead): Codruta Mare, Cristian Mihai Dragoș, Monica Violeta Achim
  • Cardo AI: Gennaro Di Brino, Federico Giudici, Stefano Panazzi
  • HU Berlin: Wolfgang Härdle, Rui Ren, Stefan Lessmann
  • WU Vienna: Bettina Grün, Kurt Hornik, Ronald Hochreiter
• Active period: From M4 to M48.
• Activity type: Research.
• Early Stage Researchers involved: 6, 8, 13 & 15

Objectives

The WP will work towards a European financial data space.

• O 1.1. To answer the main research questions on solving data quality and availability hurdles outlined in the IRPs
• O 1.2. To demonstrate the novel data quality and augmentation methodologies through industry use cases (SWE, INT, RAI, CAR)
• O 1.3. To disseminate the knowledge, validated by an international research centre (FRA, ECB, ARC)

Description

WP 1 is led by BBU and supported by all partners. The work is divided into the following tasks:

• Task 1.1. Technical coordination. Monitoring the related IRPs, store the output generated in a location accessible to the entire network.
• Task 1.2. Support the research training for all assigned ESRs and contribute to advanced training content.
• Task 1.3. Propose novel methodologies for detecting anomalies and dependence structures in high dimensional, high frequency data.
• Task 1.4. Develop methods to streamline data collection, resolve data quality issues and structure it to support downstream processes.
• Task 1.5. Industry Prototype: Develop solutions that rely on attention networks to incorporate text and temporal dependencies.
• Task 1.6. Disseminate, communicate and exploit the results (Conferences, OS Day, policy paper, prototype, use case,).
• Task 1.7. Jointly with the other research WPs, ensure that 10 new data-driven models can be built for prototypes for the industry

WP2 AI for financial markets

• Lead Beneficiary: WU Vienna.
• Researchers involved:
  • WU Vienna (Lead): Bettina Grün, Kurt Hornik, Ronald Hochreiter
  • ASE Bucharest: Adrian Costea, Daniel Traian Pele, Vasile Strat
  • Cardo AI: Gennaro Di Brino, Federico Giudici, Stefano Panazzi
  • University of Twente: Abhista Abhista, Ekaterina Svetlova, Joerg Osterrieder, Jos van Hillegersberg, Laura Spierdijk, Marcos Machado, Martijn Mes, Wouter van Heeswijk
• Active period: From M4 to M48.
• Activity type: Research.
• Early Stage Researchers involved: 1, 12 & 14.

Objectives

The WP will work on enabling the use of complex AI models in real-world financial settings.

• O 2.1. To answer the main research questions on solving AI deployment hurdles for industry outlined in the IRPs.
• O 2.2. To demonstrate the novel dynamic, rating models, automated trading platforms and market environments for RL (CAR, ROY).
• O 2.3. To disseminate the knowledge validated by an international research centre (FRA, ECB, ARC)

Description

WP 2 is led by WWU and supported by all partners. The work is divided into the following tasks:

• Task 2.1. Technical coordination. Monitoring the related IRPs, store the output generated in a location accessible to the entire network.
• Task 2.2. Support the research training for all assigned ESRs and contribute to advanced training content
• Task 2.3. Industry Prototype: Propose an accurate, robust, composite, machine learning (ML)-based, dynamic rating model for SMEs.
• Task 2.4. Develop a prototype: platforms for trading with improved the explainability of the AI/ML models and ESG/CSR indicators.
• Task 2.5. Address the main practical challenges of applying RL in real-world financial settings and building open access use cases.
• Task 2.6. Disseminate, communicate and exploit the results (Conferences, OS Day, policy paper, two prototypes, use case, media coverage)

WP3 Towards explainable and fair AI-generated decisions

• Lead Beneficiary: Bern Business School.
• Researchers involved:
  • Bern Business School (Lead): Adam Kurpisz, Branka Hadji Misheva, Christian Hopp
  • University of Naples Federico II: Francesco Palumbo, Alfonso Iodice D'Enza, Maria Iannario, Antonio Pescapè
• Active period: From M4 to M48.
• Activity type: Research.
• Early Stage Researchers involved: 9, 16 & 17.

Objectives

The WP will work towards a unifying framework of explainability for AI models applied to financial use cases.

• O 3.1. To answer the main research questions on solving explainability deployment hurdles for financial applications.
• O 3.2. To demonstrate the proposed framework for audience-dependent explanations, through use cases (SWE, INT, ROY).
• O 3.3. To disseminate the knowledge, validated by an international research centre (FRA, ECB, ARC)

Description

WP 3 is led by BFH and supported by all partners. The work is divided into the following tasks:

• Task 3.1. Technical coordination. Monitoring the related IRPs, store the output generated in a location accessible to the entire network.
• Task 3.2. Support the research training for all assigned ESRs and contribute to advanced training content
• Task 3.3. To provide global and local post hoc explainability techniques that address the explainability needs of different stakeholders.
• Task 3.4. To propose explainability functions, tailored for financial time series, preserving the non-stationary dependence structure.
• Task 3.5. Develop new portfolio optimization models that address challenges of incorporating fairness considerations into investments.
• Task 3.6. Disseminate, communicate and exploit the results (Conferences, OS Day, policy paper, two prototypes, use case, media coverage)

WP4 Driving digital innovation with Blockchain applications

• Lead Beneficiary: HU Berlin.
• Researchers involved:
  • HU Berlin (Lead): Wolfgang Härdle, Rui Ren, Stefan Lessmann
  • University of Twente: Abhista Abhista, Ekaterina Svetlova, Joerg Osterrieder, Jos van Hillegersberg, Laura Spierdijk, Marcos Machado, Martijn Mes, Wouter van Heeswijk
  • WU Vienna: Bettina Grün, Kurt Hornik, Ronald Hochreiter
• Active period: From M4 to M48.
• Activity type: Research.
• Early Stage Researchers involved: 3, 5 & 7

Objectives

The WP will focus on driving forward digital innovations through increased adoption of the blockchain technology:

• O 4.1. To answer the main research questions on solving blockchain deployment hurdles for financial applications.
• O 4.2. To demonstrate the proposed risk index, fraud detection system and industry standard for blockchain (DEL, RAI, ROY).
• O 4.3. To disseminate the knowledge, validated by an international research centre (FRA, ECB, ARC)

Description

WP 4 is led by HUB and supported by all partners. The work is divided into the following tasks:

• Task 4.1. Technical coordination. Monitoring the related IRPs, store the output generated in a location accessible to the entire network.
• Task 4.2. Support the research training for all assigned ESRs and contribute to advanced training content
• Task 4.3. To develop a risk index for cryptos to measure dependencies and spillover effects in tail risk events in the crypto universe.
• Task 4.4. Industry Prototype: To propose a fraud detection system for financial networks with dynamic AI learning models.
• Task 4.5. To propose an industry standard for blockchain applications in finance.
• Task 4.6. Disseminate, communicate and exploit the results (Conferences, OS Day, policy paper, two prototypes, use case, media coverage)

WP5 Sustainability of digital finance

• Lead Benificiary: University of Naples.
• Researchers involved:
  • University of Naples Federico II (Lead): Francesco Palumbo, Alfonso Iodice D'Enza, Maria Iannario, Antonio Pescapè
  • University of Twente: Abhista Abhista, Ekaterina Svetlova, Joerg Osterrieder, Jos van Hillegersberg, Laura Spierdijk, Marcos Machado, Martijn Mes, Wouter van Heeswijk
  • Kaunas University of Technology: Audrius Kabasinskas, Eimutis Valakevičius, Kristina Šutienė
• Active period: From M4 to M48.
• Activity type: Research.
• Early Stage Researchers involved: 2, 4, 10 & 11

Objectives

The WP will focus on combining traditional investment approaches with ESG insights.

• O 5.1. To answer the main research questions on incorporating ESG considerations into investment process and portfolio construction.
• O 5.2. To demonstrate the recommender system, green AI credit scoring and ABMs through use cases (SWE, RAI, DEL, INT).
• O 5.3. To disseminate the knowledge through open source implementations, validated by FRA, ECB, ARC

Description

WP 5 is led by UNA and supported by all partners. The work is divided into the following tasks:

• Task 5.1. Technical coordination. Monitoring the related IRPs, store the output generated in a location accessible to the entire network.
• Task 5.2. Support the research training for all assigned ESRs and contribute to advanced training content
• Task 5.3. Three Industry Prototypes: To develop and deploy a recommender system for investing in sustainable technologies.
• Task 5.4. 2 Use cases: To propose novel green AI concepts and analyse the economical and practical impact of their deployment.
• Task 5.5. To propose applications of agent-based models to analyse finance growth in a sustainable manner.
• Task 5.6. Disseminate, communicate and exploit the results (Conferences, OS Day, policy paper, three prototypes, use cases).

WP6 Doctoral Training

• Lead Benificiary: University of Twente.
• Researchers involved:
  • University of Twente (Lead): Abhista Abhista, Ekaterina Svetlova, Joerg Osterrieder, Jos van Hillegersberg, Laura Spierdijk, Marcos Machado, Martijn Mes, Wouter van Heeswijk
  • ASE Bucharest: Adrian Costea, Daniel Traian Pele, Vasile Strat
  • Babes-Bolyai University: Codruta Mare, Cristian Mihai Dragoș, Monica Violeta Achim
  • Bern Business School: Adam Kurpisz, Branka Hadji Misheva, Christian Hopp
  • Cardo AI: Gennaro Di Brino, Federico Giudici, Stefano Panazzi
  • Kaunas University of Technology: Audrius Kabasinskas, Eimutis Valakevičius, Kristina Šutienė
  • HU Berlin: Wolfgang Härdle, Rui Ren, Stefan Lessmann
  • University of Naples Federico II: Francesco Palumbo, Alfonso Iodice D'Enza, Maria Iannario, Antonio Pescapè
  • WU Vienna: Bettina Grün, Kurt Hornik, Ronald Hochreiter
• Active period: From M1 to M48.
• Activity type: Training.
• Early Stage Researchers involved: All 1-17

Objectives

The WP will focus on providing doctoral training to all ESRs.

• O 6.1. To ensure the quality and excellence of the doctoral training
• O 6.2. To create a sustainable doctoral training programme for Europe
• O 6.3. To educate a new generation of Digital Finance ESRs, with exhaustive technical and transferable skills

Description

WP 6 is led by UTW and supported by all partners. The work is divided into the following tasks:

• Task 6.1. Technical coordination. UTW is responsible for collecting, storing and enabling access to all the preliminary, intermediate and final outputs of the related IRPs, including papers, reports, scripts, datasets, slides, etc.
• Task 6.2. Coordination of doctoral training that will be organised by the network partners.
• Task 6.3. Collect research content from WP 1 - 5 for doctoral training courses
• Task 6.4. Approve the content of newly generated training material
• Task 6.5. Build a new European Doctoral Programme in Digital Finance

WP7 Dissemination, Outreach and Exploitation

• Lead Benificiary: Bern Business School.
• Researchers involved:
  • Bern Business School (Lead): Adam Kurpisz, Branka Hadji Misheva, Christian Hopp
  • ASE Bucharest: Adrian Costea, Daniel Traian Pele, Vasile Strat
  • Babes-Bolyai University: Codruta Mare, Cristian Mihai Dragoș, Monica Violeta Achim
  • Cardo AI: Gennaro Di Brino, Federico Giudici, Stefano Panazzi
  • Kaunas University of Technology: Audrius Kabasinskas, Eimutis Valakevičius, Kristina Šutienė
  • HU Berlin: Wolfgang Härdle, Rui Ren, Stefan Lessmann
  • University of Naples Federico II: Francesco Palumbo, Alfonso Iodice D'Enza, Maria Iannario, Antonio Pescapè
  • University of Twente: Abhista Abhista, Ekaterina Svetlova, Joerg Osterrieder, Jos van Hillegersberg, Laura Spierdijk, Marcos Machado, Martijn Mes, Wouter van Heeswijk
  • WU Vienna: Bettina Grün, Kurt Hornik, Ronald Hochreiter
• Active period: From M1 to M48.
• Activity type: Dissemination.
• Early Stage Researchers involved: All 1-17.

Objectives

The WP will focus on dissemination, outreach and exploitation of results.

• O 7.1. To promote and raise awareness on the network's training and research program and its potential impact through digital channels
• O 7.2. To provide information and access to the network, its research, policy and industry-specific results, conferences, and activities.
• O 7.3 To create impact in the research, industry and regulatory community by validating the results of the IRPs through: (i) publications

in high quality scientific journals; (ii) industry use cases, and (iii) policy papers discussed with international regulatory bodies.

Description

WP 7 is led by BFH and supported by all partners. The work is divided into the following tasks:

• Task 7.1. Establish the dissemination team (one representative per partner).
• Task 7.2. Establish an online communication infrastructure within DIGITAL for the main network communication.
• Task 7.3. Define a dissemination and exploitation strategy, which will detail the communication protocols, intervals and channels.
• Task 7.4. Launch of the external communication channels of DIGITAL (e.g. website, social media channels and GitHub repository).
• Task 7.5. Maintain a database that records the progress and impact of the network in all its aspects
• Task 7.6. Ensure exploitation to a large number of industry partners, including banks, Fintechs, technology and software providers, consulting companies, companies from other industries
• Task 7.7. Support the research WPs to deploy use cases and prototypes and enhance exploitation and 2 spin-offs

WP8 Project Management

• Lead Benificiary: University of Twente.
• Researchers involved:
  • University of Twente (Lead): Abhista Abhista, Ekaterina Svetlova, Joerg Osterrieder, Jos van Hillegersberg, Laura Spierdijk, Marcos Machado, Martijn Mes, Wouter van Heeswijk
  • ASE Bucharest: Adrian Costea, Daniel Traian Pele, Vasile Strat
  • Babes-Bolyai University: Codruta Mare, Cristian Mihai Dragoș, Monica Violeta Achim
  • Bern Business School: Adam Kurpisz, Branka Hadji Misheva, Christian Hopp
  • Cardo AI: Gennaro Di Brino, Federico Giudici, Stefano Panazzi
  • Kaunas University of Technology: Audrius Kabasinskas, Eimutis Valakevičius, Kristina Šutienė
  • HU Berlin: Wolfgang Härdle, Rui Ren, Stefan Lessmann
  • University of Naples Federico II: Francesco Palumbo, Alfonso Iodice D'Enza, Maria Iannario, Antonio Pescapè
  • WU Vienna: Bettina Grün, Kurt Hornik, Ronald Hochreiter
• Active period: From M1 to M48.
• Activity type: Management.
• Early Stage Researchers involved: All 1-17.

Objectives

The WP will coordinate the overall work of the project and its implementation, both at the technical and financial level.

• O 8.1. Coordinate and maintain cooperation among all involved partners and participants
• O 8.2: Coordinate financial management of the project
• O 8.3: Coordinate internal communication, between partners and with the other participants to the project
• O 8.4: Coordinate submission of all reports and deliverables to the EC and validate external dissemination

Description

WP 8 is led by UTW and supported by all partners. The work is divided into the following tasks:

• Task 8.1. Establishment and management of the relationships of the DIGITAL Network (all, UTW).
• Task 8.2. Establishment and management of the relationships of the DIGITAL Advisory Board (UTW).
• Task 8.3. Technical coordination (UTW). Monitor the content and progress of each work package and coordinate the cooperation
• Task 8.4. Financial coordination (UTW). Monitor how the financial resources are employed, to ensure effectiveness of the project
• Task 8.5 Communication (UTW). Maintain internal communications within the consortium, with external parties and the EC

WP9 Ethics Requirements

• Lead Benificiary: University of Twente.
• Active period: From M1 to M48.
• Activity type: Ethics.
• Early Stage Researchers involved: All 1-17.