MSCA Work Packages

WP1 Towards a European financial data space

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

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

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

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)