Difference between revisions of "Digital Finance MSCA"
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== Network == | == Network == | ||
| − | For this purpose, we have gathered an internationally recognized network consisting of | + | For this purpose, we have gathered an internationally recognized network consisting of seven leading European Universities (WU Vienna, 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 and Raiffeisen Bank International AG) 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 == | == Objectives == | ||
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| University of Naples || Partner || Academic || https://www.unina.it/documents/11958/28410551/GEP_Ateneo_2022-2024_english.pdf | | University of Naples || Partner || Academic || https://www.unina.it/documents/11958/28410551/GEP_Ateneo_2022-2024_english.pdf | ||
|- | |- | ||
| − | | Bucharest University of Economic Studies || Partner || Academic || | + | | Bucharest University of Economic Studies || Partner || Academic || https://egedi.ase.ro/?p=157 |
|- | |- | ||
| Babes-Bolyai University || Partner || Academic || https://www.ubbcluj.ro/en/despre/organizare/files/EN_Ghid_egalitate_de_gen.pdf | | Babes-Bolyai University || Partner || Academic || https://www.ubbcluj.ro/en/despre/organizare/files/EN_Ghid_egalitate_de_gen.pdf | ||
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| Kaunas University of Technology || Partner || Academic || https://equal-ist.eu/eq-uploads/2017/01/EQUAL-IST-GEP-Sustainability-plan-_KTU.pdf | | Kaunas University of Technology || Partner || Academic || https://equal-ist.eu/eq-uploads/2017/01/EQUAL-IST-GEP-Sustainability-plan-_KTU.pdf | ||
|- | |- | ||
| − | | Cardo AI || Partner || Industry|| | + | | Cardo AI || Partner || Industry|| https://cardoai.com/wp-content/uploads/2024/03/Gender-Equality-Plan-GEP-for-Cardo-AI-March-2024.pdf |
|- | |- | ||
| Raiffeisen Bank International AG || Associated || Industry || NA | | Raiffeisen Bank International AG || Associated || Industry || NA | ||
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Each Individual Research Project has an Early Stage Researcher assigned to it. In other words, for each of the 17 IRPs, there is one ESRs. | Each Individual Research Project has an Early Stage Researcher assigned to it. In other words, for each of the 17 IRPs, there is one ESRs. | ||
| − | * [[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 | 1. Strengthening European financial service providers through applicable reinforcement learning]] |
| − | * [[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]] | + | * [[MSCA_Individual_Research_Projects#Modelling_green_credit_scores_for_a_network_of_retail_and_business_clients | 2. Modelling green credit scores for a network of retail and business clients]] |
| − | * [[MSCA_Individual_Research_Projects# | + | * [[MSCA_Individual_Research_Projects#Machine_Learning_for_Digital_Finance | 3. Machine Learning for Digital Finance]] |
| − | * [[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]] | + | * [[MSCA_Individual_Research_Projects#A_recommender_system_to_re-orient_investments_towards_more_sustainable_technologies | 4. A recommender system to re-orient investments towards more sustainable technologies]] |
| − | * [[MSCA_Individual_Research_Projects#Fraud_detection_in_financial_networks | Fraud detection in financial networks]] | + | * [[MSCA_Individual_Research_Projects#Fraud_detection_in_financial_networks | 5. Fraud detection in financial networks]] |
| − | * [[MSCA_Individual_Research_Projects#Collaborative_learning_across_data_silos | Collaborative learning across data silos]] | + | * [[MSCA_Individual_Research_Projects#Collaborative_learning_across_data_silos | 6. Collaborative learning across data silos]] |
| − | * [[MSCA_Individual_Research_Projects#Risk_index_for_cryptos | Risk index for cryptos]] | + | * [[MSCA_Individual_Research_Projects#Risk_index_for_cryptos | 7. Risk index for cryptos]] |
| − | * [[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]] | + | * [[MSCA_Individual_Research_Projects#Detecting_anomalies_and_dependence_structures_in_high_dimensional.2C_high_frequency_financial_data | 8. Detecting anomalies and dependence structures in high dimensional, high frequency financial data]] |
| − | * [[MSCA_Individual_Research_Projects#Audience-dependent_explanations | Audience-dependent explanations]] | + | * [[MSCA_Individual_Research_Projects#Audience-dependent_explanations | 9. Audience-dependent explanations]] |
| − | * [[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]] | + | * [[MSCA_Individual_Research_Projects#Experimenting_with_Green_AI_to_reduce_processing_time_and_contributes_to_creating_a_low-carbon_economy | 10. Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy]] |
| − | * [[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]] | + | * [[MSCA_Individual_Research_Projects#Applications_of_Agent-based_Models_.28ABM.29_to_analyse_finance_growth_in_a_sustainable_manner_over_a_long-term_period | 11. Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period]] |
| − | * [[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]] | + | * [[MSCA_Individual_Research_Projects#Developing_industry-ready_automated_trading_systems_to_conduct_EcoFin_analysis_using_deep_learning_algorithms | 12. Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms]] |
| − | * [[MSCA_Individual_Research_Projects#Predicting_financial_trends_using_text_mining_and_NLP | Predicting financial trends using text mining and NLP]] | + | * [[MSCA_Individual_Research_Projects#Predicting_financial_trends_using_text_mining_and_NLP | 13. Predicting financial trends using text mining and NLP]] |
| − | * [[MSCA_Individual_Research_Projects#Challenges_and_opportunities_for_the_uptaking_of_technological_development_by_industry | Challenges and opportunities for the uptaking of technological development by industry]] | + | * [[MSCA_Individual_Research_Projects#Challenges_and_opportunities_for_the_uptaking_of_technological_development_by_industry | 14. Challenges and opportunities for the uptaking of technological development by industry]] |
| − | * [[MSCA_Individual_Research_Projects#Deep_Generation_of_Financial_Time_Series | Deep Generation of Financial Time Series]] | + | * [[MSCA_Individual_Research_Projects#Deep_Generation_of_Financial_Time_Series | 15. Deep Generation of 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]] | + | * [[MSCA_Individual_Research_Projects#Investigating_the_utility_of_classical_XAI_methods_in_financial_time_series | 16. Investigating the utility of classical XAI methods in financial time series]] |
| − | * [[MSCA_Individual_Research_Projects#Fair_Algorithmic_Design_and_Portfolio_Optimization_under_Sustainability_Concerns | Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns]] | + | * [[MSCA_Individual_Research_Projects#Fair_Algorithmic_Design_and_Portfolio_Optimization_under_Sustainability_Concerns | 17. Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns]] |
= Training= | = Training= | ||
| − | The training of the ESRs is built on four | + | The training of the ESRs is built on four pillars: |
| − | + | #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. | 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 == | + | ==Pillar 1: Training through research and mandatory scientific training == |
* Foundation of data science (BBU, 4 ECTS) | * Foundation of data science (BBU, 4 ECTS) | ||
* Introduction to AI for financial applications (WWU, 4 ECTS) | * Introduction to AI for financial applications (WWU, 4 ECTS) | ||
* The need for eXplainable AI: methods and applications in finance (BFH, 4 ECTS) | * The need for eXplainable AI: methods and applications in finance (BFH, 4 ECTS) | ||
| − | * Introduction to Blockchain applications in finance ( | + | * Introduction to Blockchain applications in finance (ASE, 4 ECTS) |
* Sustainable finance (UNA, 4 ECTS) | * Sustainable finance (UNA, 4 ECTS) | ||
| + | * Ethics Applicable to Digital Aspects (UTW, 4 ECTS) | ||
| − | == Advanced scientific training == | + | ==Pillar 2: Advanced scientific training == |
* Synthetic Data Generation for Finance (ARC, 4 ECTS) | * Synthetic Data Generation for Finance (ARC, 4 ECTS) | ||
* Anomaly Detection in Big Data (BBU, 4 ECTS) | * Anomaly Detection in Big Data (BBU, 4 ECTS) | ||
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* Data-Centric AI (WWU, 3 ECTS) | * Data-Centric AI (WWU, 3 ECTS) | ||
* Cybersecurity in Digital Finance (UTW, 3 ECTS) | * Cybersecurity in Digital Finance (UTW, 3 ECTS) | ||
| − | * AI Design in Digital Finance ( | + | * AI Design in Digital Finance (ASE, 4 ECTS) |
* Barriers in Digital Finance Adoption (WWU, 3 ECTS) | * Barriers in Digital Finance Adoption (WWU, 3 ECTS) | ||
* Explainable AI in Finance (BFH, 4 ECTS) | * Explainable AI in Finance (BFH, 4 ECTS) | ||
* Digital Finance Regulation (ECB, 3 ECTS) | * Digital Finance Regulation (ECB, 3 ECTS) | ||
* History and Prospects of Digital Finance (UNA, 3 ECTS) | * History and Prospects of Digital Finance (UNA, 3 ECTS) | ||
| − | * Blockchains in Digital Finance ( | + | * Blockchains in Digital Finance (ASE, 4 ECTS) |
* Digital EIT Summer School (EIT, 4 ECTS) | * Digital EIT Summer School (EIT, 4 ECTS) | ||
* Green Digital Finance (KUT, 3 ECTS) | * Green Digital Finance (KUT, 3 ECTS) | ||
| − | * Multi-Criteria Decision Making in Sustainable Finance (FRA, 3 ECTS) | + | * Multi-Criteria Decision Making in Sustainable Finance (FRA, 3 ECTS) |
| − | == Transferable skills training == | + | ==Pillar 3: Transferable skills training == |
| − | 1. Gender and | + | 1. Gender, Diversity and Ethics (M3, 2 ECTS): Gender and Diversity Dimension in Research (ECB); Ethical dimensions |
| − | 2. Research and Project management (M12, 4 ECTS): Project Management (Royalton | + | 2. Research and Project management (M12, 4 ECTS): Project Management (Royalton), HE framework and research project management (ASE), Research Ethics and Sustainable Research Management (BFH), Environmental Aspects (UNA); |
3. Research Skills (M18, 4 ECTS): Scientific Writing (BFH), Scientific Communication (RAIFFEISEN), Open Science Principles (UNA), Citizen Science (WWU); | 3. Research Skills (M18, 4 ECTS): Scientific Writing (BFH), Scientific Communication (RAIFFEISEN), Open Science Principles (UNA), Citizen Science (WWU); | ||
| − | 4. Entrepreneurship (M24, 4 ECTS): Intellectual Property Rights and Patenting (ECB | + | 4. Entrepreneurship (M24, 4 ECTS): Intellectual Property Rights and Patenting (ECB), Entrepreneurship Training (EIT), Entrepreneurial Finance (WWU), Start-ups and Industry Transfer (EIT, DELOITTE); |
5. Labor Market Skills (M36, 2 ECTS): Job Applications (UTW), communication skills (UTW, DELOITTE) | 5. Labor Market Skills (M36, 2 ECTS): Job Applications (UTW), communication skills (UTW, DELOITTE) | ||
| − | == Training through secondments == | + | ==Pillar 4: Training through secondments == |
Each ESR spends four months at a research center, and 18 months in industry (see also [[MSCA_Individual_Research_Projects | MSCA Individual Research Projects]] for the comprehensive path of each of the ESRs) | Each ESR spends four months at a research center, and 18 months in industry (see also [[MSCA_Individual_Research_Projects | MSCA Individual Research Projects]] for the comprehensive path of each of the ESRs) | ||
= Meetings = | = Meetings = | ||
| − | + | A list of meeting is visible at the [https://www.digital-finance-msca.com/events | MSCA website) | |
| − | |||
= Information for new joiners = | = Information for new joiners = | ||
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# Next, please also sign up to our COST network as a Work Group member via [https://www.cost.eu/actions/CA19130/#tabs+Name:Working%20Groups%20and%20Membership this link] | # Next, please also sign up to our COST network as a Work Group member via [https://www.cost.eu/actions/CA19130/#tabs+Name:Working%20Groups%20and%20Membership this link] | ||
# Furthermore, please join our LinkedIn group via [https://www.linkedin.com/groups/12887335/ this link] | # Furthermore, please join our LinkedIn group via [https://www.linkedin.com/groups/12887335/ this link] | ||
| − | # Please also apply to our mailing lists via [mailto: | + | # Please also apply to our mailing lists via [mailto:b.vanbraak@student.utwente.nl?cc=joerg.osterrieder@utwente.nl;m.r.machado@utwente.nl;branka.hadjimisheva@bfh.ch&subject=Application%20to%20MSCA%20DIGITAL%20network email]. State your first name, last name, institution, role (researcher, early stage researcher, or administrative), and (if not the address you are emailing with) your preferred email. Please also include the email address you use for the EU portal so that we can add you there. |
# Additionally, the introductory presentations can be found [[Digital_Finance_MSCA#Introductory_PowerPoints | here]] and the previous meeting presentations and minutes can be found [[Digital_Finance_MSCA#Meetings | here]]. Other relevant information is found on the rest of this Wiki. | # Additionally, the introductory presentations can be found [[Digital_Finance_MSCA#Introductory_PowerPoints | here]] and the previous meeting presentations and minutes can be found [[Digital_Finance_MSCA#Meetings | here]]. Other relevant information is found on the rest of this Wiki. | ||
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= Additional Links = | = Additional Links = | ||
| + | ''Note: most links are protected and only accessible for members of the MSCA network'' | ||
== Introductory Presentations == | == Introductory Presentations == | ||
| − | * [https:// | + | * [https://universiteittwente.sharepoint.com/:p:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP7%20Dissemination%20Exploitation%20and%20Communication/DIGITAL%20Promotional%20Materials/DIGITAL%20Introduction%20Slides/2024.07.18%20MSCA%20Digital%20Finance%20Overview.pptx?d=w5ba669d7ee5544e594acb25ddb6fb296&csf=1&web=1&e=8K4pvw MSCA DIGITAL Network Introduction] |
| − | * [https://docs.google.com/presentation/d/1CvlOCH7Fm0RcX9oFEPd6kgZVoQOGCMUS/edit?usp=drive_link&ouid=111168062664241819317&rtpof=true&sd=true COST | + | * [https://docs.google.com/presentation/d/1CvlOCH7Fm0RcX9oFEPd6kgZVoQOGCMUS/edit?usp=drive_link&ouid=111168062664241819317&rtpof=true&sd=true COST Network Introduction] |
| + | * [https://universiteittwente.sharepoint.com/:p:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP7%20Dissemination%20Exploitation%20and%20Communication/Archive/Presentations/05.%20MSCA%20IRPs/2023.11.27%20Secondments%20overview.pptx?d=w10cbdb5c46c84ce496cc97413512605b&csf=1&web=1&e=ysZ9TI Secondments Overview] | ||
== Network == | == Network == | ||
| − | * [https:// | + | * [https://universiteittwente.sharepoint.com/sites/MSCADigitalFinance2/ MSCA DIGITAL SharePoint] |
| − | * [https://docs.google.com/spreadsheets/d/1VciplTaQtZwHJFyBLwbxrG42yIaAH-9vRkr-KO_Jw-M/edit#gid= | + | * [https://teams.microsoft.com/l/team/19%3A2biqS0__D5-lidhVHeOfOT-xPcE5cr1k0mqnpECYyeg1%40thread.tacv2/conversations?groupId=4f30b94c-86a4-42be-b660-e37056eb9599&tenantId=723246a1-c3f5-43c5-acdc-43adb404ac4d MSCA DIGITAL Teams] |
| − | * [https:// | + | * [https://docs.google.com/spreadsheets/d/1VciplTaQtZwHJFyBLwbxrG42yIaAH-9vRkr-KO_Jw-M/edit#gid=0&fvid=728164742 Network Contacts] |
| + | * [https://www.meetup.com/fintech_ai_in_finance/ MeetUp] | ||
| − | == | + | == Recruitment == |
| + | * [https://euraxess.ec.europa.eu/jobs/178873#job-information Job Offers for IRPs] | ||
| + | * [https://universiteittwente.sharepoint.com/:f:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP7%20Dissemination%20Exploitation%20and%20Communication/DIGITAL%20Promotional%20Materials/DIGITAL%20poster%20on%20job%20openings?csf=1&web=1&e=eLT1ho DIGITAL Promotional Poster] | ||
| + | * [https://universiteittwente.sharepoint.com/:f:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP6%20Doctoral%20Training/Career%20Development%20Plans%20(CDPs)?csf=1&web=1&e=scess6 Folder for Career Development Plans (CDPs)] | ||
| + | * [https://universiteittwente.sharepoint.com/:x:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP8%20Project%20Management/Recruitment/IRP%20overview.xlsx?d=wd7e951e037384c77a180e1d0e482905d&csf=1&web=1&e=YActME Recruitment Status] | ||
| − | * [https:// | + | == Doctoral Training == |
| − | * [https:// | + | * [https://universiteittwente.sharepoint.com/:x:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP6%20Doctoral%20Training/Course%20overview%20and%20schedule/Doctoral%20Network%20Training%20Program.xlsx?d=wfcad97a0d2d649cabd12a8e421ad213e&csf=1&web=1&e=t6RZcP Doctoral Network Training Program] |
| − | * [https:// | + | * [https://universiteittwente.sharepoint.com/:x:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP6%20Doctoral%20Training/Course%20syllabi%20and%20materials/Course%20summaries%20website.xlsx?d=wc8617bae46084fb2863cc7654bfe3cab&csf=1&web=1&e=WvA5he Course Information] |
| + | * [https://universiteittwente.sharepoint.com/:x:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP6%20Doctoral%20Training/Course%20overview%20and%20schedule/Training%20calendar.xlsx?d=w2f66be98d37b4fe0b8b79b6d3fa3c9f4&csf=1&web=1&e=r6hFjQ&nav=MTVfe0I0RkJCNDRCLTg0Q0YtNDc0OS04MjkxLTBFQTJCMkY2ODc5Q30 Training Calendar] | ||
| − | == | + | == Reporting == |
| − | * [https:// | + | * [https://universiteittwente.sharepoint.com/:x:/r/sites/MSCADigitalFinance2/Gedeelde%20documenten/WP8%20Project%20Management/Continuous%20reporting/MSCA%20DIGITAL%20Continuous%20Reporting.xlsx?d=w45bf26c4c716410d9fcf90d688408bef&csf=1&web=1&e=kywQIY&nav=MTVfezAwMDAwMDAwLTAwMDEtMDAwMC0wMDAwLTAwMDAwMDAwMDAwMH0 Continuous Reporting Excel] |
| − | |||
| − | + | == Additional information for members and institutions == | |
| − | == | + | The [https://www.digital-finance-msca.com/partners-area partner area] contains additional information about: |
| − | + | * Reporting responsibilities | |
| − | + | * Dissemination and exploitations responsibilities | |
| − | + | * Course- and event-organizing responsibilities | |
| − | + | * Hiring process | |
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Latest revision as of 15:48, 30 September 2024
MSCA Industrial Doctoral Network on Digital Finance - Reaching New Frontiers
Executive Summary
MSCA Digital Finance - European Industrial Doctoral Network on Digital Finance
The Marie Sklodowska-Curie Action Industrial Doctoral Network on Digital Finance (MSCA Digital Finance) represents a groundbreaking European PhD Research and Training Programme, focusing on innovation and research across the European Union.
Objective: MSCA Digital Finance aims to integrate advanced research and training in the field of digital finance. The primary goal is to foster the development of next-generation researchers and professionals who can contribute significantly to the digital finance sector. This initiative is designed to bridge the gap between academia and industry, ensuring that PhD candidates are well-equipped with both theoretical knowledge and practical skills relevant to the rapidly evolving digital finance landscape.
Funding and Support: The program receives substantial funding from the European Horizon framework, underscoring the EU's commitment to advancing research and innovation in digital finance. This financial support is instrumental in facilitating collaborative research, offering scholarships, and providing state-of-the-art resources and opportunities for participants.
Collaborative Network: The MSCA Digital Finance brings together a diverse network of academic institutions, research centers, and industry partners across Europe. This collaboration ensures a multidisciplinary approach and allows for a wide array of research topics within digital finance.
Impact and Benefits: The program is designed to produce a significant impact on both the academic and industrial sectors in digital finance. Graduates of MSCA Digital Finance are expected to emerge as leaders and innovators, driving forward the digital transformation in the financial industry, enhancing the competitiveness of the European digital finance sector on a global scale.
The MSCA Digital Finance programme is a strategic initiative aligning with the European Union's vision of fostering innovation and expertise in digital finance.
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.
EU Guidelines: European Approach to artificial intelligence ( https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence), the EU Digital Finance Package (https://finance.ec.europa.eu/publications/digital-finance-package_en) WEF 2020, https://www3.weforum.org/docs/WEF_AI_in_Financial_Services_Survey.pdf https://digital-strategy.ec.europa.eu/en/policies/strategy-data https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf https://digital-strategy.ec.europa.eu/en/policies/blockchain-strategy https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en https://op.europa.eu/en/publication-detail/-/publication/bb02d56e-9b3c-11eb-b85c-01aa75ed71a1/language-en https://finance.ec.europa.eu/publications/digital-finance-package_en- https://www.eeas.europa.eu/sites/default/files/horizon_europe_strategic_plan_2021-2024.pdf
Network
For this purpose, we have gathered an internationally recognized network consisting of seven leading European Universities (WU Vienna, 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 and Raiffeisen Bank International AG) 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 nineteen Doctoral Candidates 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
| Institution | Role | Academic or Industry | Link to Gender Equality plan |
|---|---|---|---|
| University of Twente | Coordinator | Academic | https://www.utwente.nl/en/organisation/about/diversity/211128-gep.pdf |
| WU Vienna | Partner | Academic | https://www.wu.ac.at/en/the-university/about-wu/strategy/social-responsibility/ |
| University of Naples | Partner | Academic | https://www.unina.it/documents/11958/28410551/GEP_Ateneo_2022-2024_english.pdf |
| Bucharest University of Economic Studies | Partner | Academic | https://egedi.ase.ro/?p=157 |
| Babes-Bolyai University | Partner | Academic | https://www.ubbcluj.ro/en/despre/organizare/files/EN_Ghid_egalitate_de_gen.pdf |
| Kaunas University of Technology | Partner | Academic | https://equal-ist.eu/eq-uploads/2017/01/EQUAL-IST-GEP-Sustainability-plan-_KTU.pdf |
| Cardo AI | Partner | Industry | https://cardoai.com/wp-content/uploads/2024/03/Gender-Equality-Plan-GEP-for-Cardo-AI-March-2024.pdf |
| Raiffeisen Bank International AG | Associated | Industry | NA |
| Deloitte | Associated | Industry | NA |
| Swedbank | Associated | Industry | NA |
| European Central Bank | Associated | Industry | NA |
| Bern Business School | Associated | Academic | NA |
| Royalton Partners | Associated | Industry | NA |
| ARC Greece | Associated | Industry | NA |
| EIT Digital | Associated | Industry | NA |
| Fraunhofer Institute | Associated | Industry | NA |
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 each of the 17 IRPs, there is one ESRs.
- 1. Strengthening European financial service providers through applicable reinforcement learning
- 2. Modelling green credit scores for a network of retail and business clients
- 3. Machine Learning for Digital Finance
- 4. A recommender system to re-orient investments towards more sustainable technologies
- 5. Fraud detection in financial networks
- 6. Collaborative learning across data silos
- 7. Risk index for cryptos
- 8. Detecting anomalies and dependence structures in high dimensional, high frequency financial data
- 9. Audience-dependent explanations
- 10. Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy
- 11. Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period
- 12. Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms
- 13. Predicting financial trends using text mining and NLP
- 14. Challenges and opportunities for the uptaking of technological development by industry
- 15. Deep Generation of Financial Time Series
- 16. Investigating the utility of classical XAI methods in financial time series
- 17. Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns
Training
The training of the ESRs is built on four pillars:
- 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.
Pillar 1: 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 (ASE, 4 ECTS)
- Sustainable finance (UNA, 4 ECTS)
- Ethics Applicable to Digital Aspects (UTW, 4 ECTS)
Pillar 2: 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 (ASE, 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 (ASE, 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)
Pillar 3: Transferable skills training
1. Gender, Diversity and Ethics (M3, 2 ECTS): Gender and Diversity Dimension in Research (ECB); Ethical dimensions
2. Research and Project management (M12, 4 ECTS): Project Management (Royalton), HE framework and research project management (ASE), Research Ethics and Sustainable Research Management (BFH), Environmental Aspects (UNA);
3. Research Skills (M18, 4 ECTS): Scientific Writing (BFH), Scientific Communication (RAIFFEISEN), Open Science Principles (UNA), Citizen Science (WWU);
4. Entrepreneurship (M24, 4 ECTS): Intellectual Property Rights and Patenting (ECB), Entrepreneurship Training (EIT), Entrepreneurial Finance (WWU), Start-ups and Industry Transfer (EIT, DELOITTE);
5. Labor Market Skills (M36, 2 ECTS): Job Applications (UTW), communication skills (UTW, DELOITTE)
Pillar 4: Training through secondments
Each ESR spends four months at a research center, and 18 months in industry (see also MSCA Individual Research Projects for the comprehensive path of each of the ESRs)
Meetings
A list of meeting is visible at the [https://www.digital-finance-msca.com/events | MSCA website)
Information for new joiners
Welcome to our network! To ensure a smooth onboarding process we ask you to complete a few steps.
- (*If you already have an EU account you can skip this step*) First of all, sign up to the EU portal via this link
- Next, please also sign up to our COST network as a Work Group member via this link
- Furthermore, please join our LinkedIn group via this link
- Please also apply to our mailing lists via email. State your first name, last name, institution, role (researcher, early stage researcher, or administrative), and (if not the address you are emailing with) your preferred email. Please also include the email address you use for the EU portal so that we can add you there.
- Additionally, the introductory presentations can be found here and the previous meeting presentations and minutes can be found here. Other relevant information is found on the rest of this Wiki.
If you have any further questions, feel free to reach out to us!
MSCA Continuous Reporting
Additional Links
Note: most links are protected and only accessible for members of the MSCA network
Introductory Presentations
Network
Recruitment
- Job Offers for IRPs
- DIGITAL Promotional Poster
- Folder for Career Development Plans (CDPs)
- Recruitment Status
Doctoral Training
Reporting
Additional information for members and institutions
The partner area contains additional information about:
- Reporting responsibilities
- Dissemination and exploitations responsibilities
- Course- and event-organizing responsibilities
- Hiring process