Difference between revisions of "Digital Finance MSCA"
| Line 107: | Line 107: | ||
* [[MSCA_Individual_Research_Projects#Strengthening_European_financial_service_providers_through_applicable_reinforcement_learning | Strengthening European financial service providers through applicable reinforcement learning]] | * [[MSCA_Individual_Research_Projects#Strengthening_European_financial_service_providers_through_applicable_reinforcement_learning | Strengthening European financial service providers through applicable reinforcement learning]] | ||
| − | * Modelling green credit scores for a network of retail and business clients | + | * [[MSCA_Individual_Research_Projects#Modelling_green_credit_scores_for_a_network_of_retail_and_business_clients | Modelling green credit scores for a network of retail and business clients]] |
| − | * Industry standard for blockchain | + | * [[MSCA_Individual_Research_Projects#Industry_standard_for_blockchain | Industry standard for blockchain]] |
| − | * A recommender system to re-orient investments towards more sustainable technologies | + | * [[MSCA_Individual_Research_Projects#A_recommender_system_to_re-orient_investments_towards_more_sustainable_technologies | A recommender system to re-orient investments towards more sustainable technologies]] |
| − | * Fraud detection in financial networks | + | * [[MSCA_Individual_Research_Projects#Fraud_detection_in_financial_networks | Fraud detection in financial networks]] |
| − | * Collaborative learning across data silos | + | * [[MSCA_Individual_Research_Projects#Collaborative_learning_across_data_silos | Collaborative learning across data silos]] |
| − | * Risk index for cryptos | + | * [[MSCA_Individual_Research_Projects#Risk_index_for_cryptos | Risk index for cryptos]] |
| − | * Detecting anomalies and dependence structures in high dimensional, high frequency financial data | + | * [[MSCA_Individual_Research_Projects#Detecting_anomalies_and_dependence_structures_in_high_dimensional.2C_high_frequency_financial_data | Detecting anomalies and dependence structures in high dimensional, high frequency financial data]] |
| − | * Audience-dependent explanations | + | * [[MSCA_Individual_Research_Projects#Audience-dependent_explanations | Audience-dependent explanations]] |
| − | * Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy | + | * [[MSCA_Individual_Research_Projects#Experimenting_with_Green_AI_to_reduce_processing_time_and_contributes_to_creating_a_low-carbon_economy | Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy]] |
| − | * Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period | + | * [[MSCA_Individual_Research_Projects#Applications_of_Agent-based_Models_.28ABM.29_to_analyse_finance_growth_in_a_sustainable_manner_over_a_long-term_period | Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period]] |
| − | * Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms | + | * [[MSCA_Individual_Research_Projects#Developing_industry-ready_automated_trading_systems_to_conduct_EcoFin_analysis_using_deep_learning_algorithms | Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms]] |
| − | * Predicting financial trends using text mining and NLP | + | * [[MSCA_Individual_Research_Projects#Predicting_financial_trends_using_text_mining_and_NLP | Predicting financial trends using text mining and NLP]] |
* Challenges and opportunities for the uptaking of technological development by industry | * Challenges and opportunities for the uptaking of technological development by industry | ||
| − | * Deep Generation of Financial Time Series | + | * [[MSCA_Individual_Research_Projects#Deep_Generation_of_Financial_Time_Series | Deep Generation of Financial Time Series]] |
| − | * Investigating the utility of classical XAI methods in financial time series | + | * [[MSCA_Individual_Research_Projects#Investigating_the_utility_of_classical_XAI_methods_in_financial_time_series | Investigating the utility of classical XAI methods in financial time series]] |
| − | * Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns | + | * [[Fair_Algorithmic_Design_and_Portfolio_Optimization_under_Sustainability_Concerns | Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns]] |
= Training= | = Training= | ||
Revision as of 09:33, 25 September 2023
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.
MSCA Network
- University of Twente
- WU Vienna
- HU Berlin
- Bucharest University of Economic Studies
- Babes-Bolyai University
- Bern Business School
- Kaunas University of Technology
- University of Naples
- Deloitte
- Swedbank
- Intesa Sanpaolo
- Raiffeisen Bank
- Cardo AI
- Royalton Partners
- ARC Greece
- EIT Digital
- Fraunhofer Institute
- European Central Bank
MSCA Committees
- Executive Board
- Supervisory Board
- External Advisory Board
- Doctoral Candidates Committee
- Research and Training Committee
- Communication and Dissemination Board
- IP and Exploitation Team
- Project Coordinator Team
MSCA Work Packages
- WP1 Towards a European financial data space
- WP2 AI for financial markets
- WP3 Towards explainable and fair AI-generated decisions
- WP4 Driving digital innovation with Blockchain applications
- WP5 Sustainability of digital finance
- WP6 Doctoral Training
MSCA Individual Research Projects
Each Individual Research Project has an Early Stage Researcher assigned to it. In other words, for all 17 IRPs, there are 17 ESRs.
- Strengthening European financial service providers through applicable reinforcement learning
- Modelling green credit scores for a network of retail and business clients
- Industry standard for blockchain
- A recommender system to re-orient investments towards more sustainable technologies
- Fraud detection in financial networks
- Collaborative learning across data silos
- Risk index for cryptos
- Detecting anomalies and dependence structures in high dimensional, high frequency financial data
- Audience-dependent explanations
- Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy
- Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period
- Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms
- Predicting financial trends using text mining and NLP
- Challenges and opportunities for the uptaking of technological development by industry
- Deep Generation of Financial Time Series
- Investigating the utility of classical XAI methods in financial time series
- Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns
Training
The training of the ESRs is built on four pilars:
- #Training through research and mandatory scientific training
- #Advanced scientific training
- #Transferable skills training
- #Training through secondments
Furthermore, training of the ESRs will consist of attending international conferences and other training programs, lab training, and lectures from external scientific lectureres.
Training through research and mandatory scientific training
- Foundation of data science (BBU, 4 ECTS)
- Introduction to AI for financial applications (WWU, 4 ECTS)
- The need for eXplainable AI: methods and applications in finance (BFH, 4 ECTS)
- Introduction to Blockchain applications in finance (HUB, 4 ECTS)
- Sustainable finance (UNA, 4 ECTS)
Advanced scientific training
- Synthetic Data Generation for Finance (ARC, 4 ECTS)
- Anomaly Detection in Big Data (BBU, 4 ECTS)
- Natural Language Processing with Transformers (ARC, 4 ECTS)
- Dependence Structures in High Frequency Financial Data (ASE, 3 ECTS)
- Reinforcement Learning in Digital Finance (UTW, 4 ECTS)
- Machine Learning in Industry (CAR, 4 ECTS)
- Deep Learning for Finance (BBU. 3 ECTS)
- Data-Centric AI (WWU, 3 ECTS)
- Cybersecurity in Digital Finance (UTW, 3 ECTS)
- AI Design in Digital Finance (HUB, 4 ECTS)
- Barriers in Digital Finance Adoption (WWU, 3 ECTS)
- Explainable AI in Finance (BFH, 4 ECTS)
- Digital Finance Regulation (ECB, 3 ECTS)
- History and Prospects of Digital Finance (UNA, 3 ECTS)
- Blockchains in Digital Finance (HUB, 4 ECTS)
- Digital EIT Summer School (EIT, 4 ECTS)
- Green Digital Finance (KUT, 3 ECTS)
- Multi-Criteria Decision Making in Sustainable Finance (FRA, 3 ECTS)
Transferable skills training
- Gender and Diversity Dimension in Research (ECB, 2 ECTS)
- Research and Project management: Project Management (ROY, DEL, 4 ECTS), HE framework and research project management (HUB, 4 ECTS), Research Ethics and Sustainable Research Management (BFH, 4 ECTS), Environmental Aspects (UNA, 4 ECTS)
- Research Skills: Scientific Writing (BFH, 4 ECTS), Scientific Communication (RAI, 4 ECTS), Open Science Principles (UNA, 4 ECTS), Citizen Science (WWU, 4 ECTS)
- Entrepreneurship: Intellectual Property Rights and Patenting (ECB, DEL, 4 ECTS), Entrepreneurship Training (EIT, 4 ECTS), Entrepreneurial Finance (BFH, 4 ECTS), Start-ups and Industry Transfer (EIT, DEL, 4 ECTS)
- Labor Market Skills: Job Applications (UTW, 2 ECTS), communication skills (UTW, DEL, 2 ECTS)
Training through secondments
Each ESR spends four months at a research center, and 18 months in industry.
Output
Deliverables
Milestones
Social Media
Contacts
Joerg Osterrieder Branka Hadji Misheva
Meetings
- 09.08.2023 Kick-Off meeting
- 21.08.2023 Thematic session on Doctoral Training
Important links
Network
Overview of the Action
- MSCA Doctoral Network on Digital Finance
- MSCA Digital Internal Overview
- Expected contributions per partner