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)