MSCA Work Packages

Summary Work Packages

There are five research work packages:

• Towards a European financial data space
• AI for financial markets
• Towards explainable and fair AI-generated decisions
• Driving digital innovation with Blockchain applications
• Sustainability of digital finance

Additionally, there are four supporting work packages:

• Doctoral Training
• Dissemination, Outreach and Exploitation
• Project Management
• Ethics

Milestones overview

• MS 1: All committees and the SB had their constituting meetings (WP 6 - 8, UTW, Due in M1)
• MS 2: Recruitment process for all DCs completed (WP 6, 8, UTW, Due in M12)
• MS 3: The personal CDPs for all DCs are approved by the recruiting institutions (WP 6, UTW, Due in M12)
• MS 4: Doctoral Training evaluated and assessed for adjustments in the second period (WP 6, UTW, Due in M24)
• MS 5: Dissemination and Outreach (WP 7, BFH, Due in M24)
• MS 6: Advanced doctoral training (WP 6, UTW, Due in M24)
• MS 7: Industry secondments evaluated and assessed for adjustments (WP 1 - 5, BBU, Due in M24)
• MS 8: Mid-term results from the IRPs of WP 1 - 5 available to the network and validated by respective industry partners (WP 1 - 5, WWU, Due in M24)
• MS 9: Project mid-term meeting (WP 1 - 8, UTW, Due in M15)
• MS 10: Final network event scheduled, planned and prepared, including pre-press release (WP 8, UTW, Due in M42)
• MS 11: Final results from the IRPs of WP 1 - 5 available to the network and validated by respective industry partners (WP 1 - 5, UNA, Due in M48)
• MS 12: Consortium Agreement (WP 8, UTW, Due in M12)
• MS 13: All recruited fellows enrolled in PhD programme (WP 6, 8, UTW, Due in M12)

WP1 Towards a European financial data space

• Lead Beneficiary: Babes-Bolyai University
• WP lead: Codruta Mare
• WP co-lead: Rubin Haxhiymeri
• Active period: From M4 to M48
• Activity type: Research
• 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

Deliverables

• D.1.1 Status report on the financial data space: Summary report on the research, industry and policy contributions
• D.1.2 Final industry prototype for data quality tools: Industry report on business challenges addressed related to the financial data space
• D.1.3 Technical summary report on data generation: Summary report on all results and impacts related to data quality and methodologies

WP2 AI for financial markets

• Lead Beneficiary: WU Vienna University of Economics and Business
• WP lead: Ronald Hochreiter
• WP co-lead: Stefan Theußl
• Active period: From M4 to M48
• Activity type: Research
• Researchers involved: 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)

Deliverables

• D.2.1 Use cases for RL: Use cases for the applicability of RL models (Due in M48)
• D.2.2 Industry prototype for ML models for trading: Industry prototype for automated trading strategies with ML models (Due in M48)
• D.2.3 Technical summary report on AI in Finance: Summary report on all results and impacts related to AI in Finance (Due in M48)

WP3 Towards explainable and fair AI-generated decisions

• Lead Beneficiary: Bern University of Applied Sciences
• WP lead: Branka Hadji Misheva
• WP co-lead: Barbara Bedowska-Sojka
• Active period: From M4 to M48
• Activity type: Research
• Researchers involved: 1, 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)

Deliverables

• D.3.1 Documentation of explainable AI methods: Documentation of test setups for applying explainable AI methods (Due in M48)
• D.3.2 Technical report on trustworthy AI methods: Technical report showing the achievements on trustworthy and fair AI models (Due in M48)
• D.3.3 Summary report on time-series explainability: Summary report on all results and impacts related to explainability for time-series (Due in M24)

WP4 Driving digital innovation with Blockchain applications

• Lead Beneficiary: Bucharest University of Economic Studies
• WP lead: Wolfgang Härdle
• WP co-lead: Daniel Traian Pele
• Active period: From M4 to M48
• Activity type: Research
• 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 TBD 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)

Deliverables

• D.4.1 White paper on crypto indices: White paper on the construction and the risks involved in crypto indices (Due in M24)
• D.4.2 Policy report on fraud detection: Policy report on fraud detection methods in blockchains and time-series (Due in M48)
• D.4.3 Industry standard for blockchain: Concept for Industry standard for blockchain applications in Finance (Due in M48)

WP5 Sustainability of digital finance

• Lead Beneficiary: University of Naples Federico II
• WP lead: Maria Iannario
• WP co-lead: Ekaterina Svetlanova
• Active period: From M4 to M48
• Activity type: Research
• 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).

Deliverables

• D.5.1 Design report for recommender systems: Design report for a prototype of recommender systems (Due in M24)
• D.5.2 Policy report on novel green AI concepts: Policy report to showcase and support regulatory initiatives on green AI (Due in M24)
• D.5.3 Concept report on sustainable finance growth: Concept for sustainable finance growth using agent-based modelling (Due in M48)

WP6 Doctoral Training

• Lead Benificiary: University of Twente
• WP lead: Wouter van Heeswijk
• WP co-lead: Xiaohong Huang
• Active period: From M1 to M48
• Activity type: Training
• Researchers involved: All IRPs

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

Deliverables

• D.6.1 Research Training Evaluation: Evaluation survey completed by each ESR at the end of training, follow-up after two years (Due in M1)
• D.6.2 Sustainable Training Programme: Plan for creating a sustainable training programme beyond the action created (Due in M24)

WP7 Dissemination, Outreach and Exploitation

• Lead Beneficiary: Bern University of Applied Sciences
• WP lead: Branka Hadji Misheva
• WP co-lead: Frédérik Sinan Bernard 
• Active period: From M1 to M48
• Activity type: Dissemination
• Researchers involved: All IRPs

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

Deliverables

• D.7.1 Dissemination and Exploitation Plan M13: Plan for the dissemination and exploitation of results, including communication activities, submitted at mid-term (M13) and an update towards the end of the project (M48). This plan should be periodically updated, and an updated version is required by M48.

This plan for the exploitation should address both internal impacts (such as internal research, collaborative research using the network, product development with the industrial partners) on the network as well as external impacts (such as impact on industry and academia outside the consortium, policy makers and regulators and civil society.) The dissemination part should clearly specify and detail specific criteria and the structure for impact monitoring.

• D.7.2 Website: Project website (Due in M2)
• D.7.3 Dissemination and exploitation repository (including research training): Repository for all dissemination materials periodically updated (Due in M3)
• D.7.4 Dissemination and Exploitation Plan M48: Plan for the dissemination and exploitation of results, including communication activities, submitted at mid-term (M13) and an update towards the end of the project (M48). This plan should be periodically updated, and an updated version is required by M48. This plan for the exploitation should address both internal impacts (such as internal research, collaborative research using the network, product development with the industrial partners) on the network as well as external impacts (such as impact on industry and academia outside the consortium, policy makers and regulators and civil society.) The dissemination part should clearly specify and detail specific criteria and the structure for impact monitoring.

WP8 Project Management

• Lead Benificiary: University of Twente
• WP lead: Marcos Machado
• WP co-lead: Branka Hadji Misheva
• Active period: From M1 to M48
• Activity type: Management
• Researchers involved: All IRPs

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

Deliverables

• D.8.1 Final meeting: Final meeting organised between the participants and the granting authority (Due in M48)
• D.8.2 Progress report: Progress Report submitted to the REA covering the first year implementation of the project (Due in M13)
• D.8.3 Career Development Plan: Document describing how the individual Career Development Plans have been established (listing also the researchers for whom such plans have been put in place). To be submitted before the mid-term meeting. (Due in M13)
• D.8.4 Data Management Plan: Data Management Plan submitted to the REA (updated towards the end of the project if needed) (Due in M13)
• D.8.5 Supervisory Board of the Network: Document establishing the supervisory board and defining the way of working (Due in M2)

WP9 Ethics Requirements

• Lead Benificiary: University of Twente
• WP lead: Catarina Silva
• WP co-lead: Ioana-Florina Coita
• Active period: From M1 to M48
• Activity type: Ethics
• Researchers involved: All IRPs

Description

This project should include an Ethics Mentor for the following reason: The proposal intends to analyze financial transaction data of persons by doctoral students. Because of the typical detail of such data and the time frame encompassed it is not possible to effectively anonymize this data. Hence it needs to be assumed that the data is pseudonymous data which is considered as personal data according to the GDPR. In this context explainable AI (XAI) is going to be investigated which allows insights in the decisions of the classifier and needs to be ethically reflected. The proposal states that the aim is "explainable and fair AI-generated decision". The consortium shows some awareness by mentioning a potential member of the advisory board with expertise in this area but in the context of the training measurements these subjects are only briefly sketched. Also in the expertise of the scientific advisors these subjects are not substantially present.

The Ethics Mentor should train participants on the ethical dimensions including privacy, fairness and trust in the context of financial data and the intended financial data space. This training should be given to participants at the latest in month 12. In the training concerning XAI (M12) in addition an ethical reflection needs to be introduced. Also in the training course concerning sustainable finance (M18) a critical reflection of the application of XAI and blockchain should be introduced. For the doctoral students the Ethics Mentor should provide reflection of the ethical dimensions of the individual research projects.

These measurements needs to be documented in a report at month 24 and another report at the end of the project.

Deliverables

• D9.1. Appointment of Ethics Advisor (Due in M6)
• D9.2. First report of the ethics advisor (Due in M24)
• D9.3. Second report of the ethics advisor (Due in M48)