13 sections from the Description of Action (Part B)
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.3
● They contribute to the UN Sustainable Development Goals.4
● 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 nine leading
European Universities (University of Twente, WU Vienna, , University of Naples, Kaunas University of Technology,
Bucharest University of Economic Studies, Babes-Bolyai University ,Bern Business School, Poznan University of
Economics and Business and University of Kaiserslautern-Landau), all ranked among the top 200 universities
globally in their fields, three major international corporations (Swedbank, Raiffeisen Bank and Deutsche 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), the Bank for International Settlements 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.
3 A 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) 4 https://sdgs.un.org/goals
4 https://sdgs.un.org/goals
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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022
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 (DCs) 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, customercentric
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 20205):
● 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.
Research programme. The strategic priorities of the European Union, industrial needs and academic research
gaps lead to the scope of our research programme. As detailed in Part A, within the structure of DIGITAL's work
packages, four early stage researchers (DCs 6, 8, 13, 15) will focus on proposing novel methodologies and
applications to address the key data quality issues associated with high-dimensional, high-variety, and
highvelocity datasets. In addition, deployment issues with cutting-edge machine learning, deep learning, and
reinforcement learning methods must be addressed in order for methodological advances to be useful in real-world
applications. DCs 12, 14 will be solely devoted to addressing the most significant obstacles to industry adoption
of technological innovation. DCs 1, 9, 16, 17 will conduct work closely related to this. They will establish the
principles of a trustworthy and secure AI for financial applications and define the methodologies that can satisfy
the explainability requirements of various stakeholders within the financial sector. In this context, the outputs will
become central to the applicable regulation of this technology. DCs 3, 5, 7 will examine the efficacy and impact of
Blockchain adoption in financial markets and propose risk management tools to address some of the main concerns
around this technology (e.g., financial stability and fraud detection). Additionally, guidelines for a supervisory
approach to machine learning for digital finance, and various perspectives will be explored. . DCs 2, 4, 10, 11 will
focus on sustainable financial products, simulating markets and designing recommender systems to analyse the
effects of adopting principles of sustainable finance philosophy.
This collaborative effort will serve as the foundation for a new qualification standard that will provide European
firms the competitive advantage they need to compete in the area of Digital Finance. To achieve DIGITAL's research
5 WEF 2020, https://www3.weforum.org/docs/WEF_AI_in_Financial_Services_Survey.pdf
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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022
and training objectives, all projects must be handled collectively for an integrated view of Digital Finance;
standalone projects will not have a significant impact.
The 17 Individual Research Projects (IRPs) are grouped into five inter-connected WPs. WP 1 lays the foundation
for all other WPs. Data is in the centre of the digital transformation (A European strategy for data, European
Commission 20206 ), though utilising it is accompanied by many challenges. By ensuring that all dimensions
(accuracy, consistency, completeness, currency, volatility and timeliness) of data quality are satisfied, that we have
sufficiently large, high-quality data available and that we can detect dependencies in high dimensions, high frequency
and high veracity financial data, DIGITAL is in a position to move towards a European financial data space and
make use of substantially more data sources than today.
WP 2 ensures the deployability of AI models. AI has improved decision making in numerous areas, including risk
management, compliance (anti-money laundering, fraud prevention, KYC), trading strategies and personalised
banking and advice (A European approach to AI, European Commission 20217), and yet, adoption of AI-based
tools in practice has still been rather slow. With DIGITAL, we will describe the primary challenges and
opportunities for industry's adoption of technological development, encourage a larger deployment of stateof-
the-art ML models in real-world financial applications and simulate the market environment in Reinforcement
Learning (RL) applications for market applications, thereby removing the primary barrier to the application of this
technology in finance.
WP 3 verifies that deployed AI models are explainable, trustworthy and unbiased. AI-driven innovation can bring
enormous benefits but such complex solutions are often referred to as “black boxes” because typically it is difficult
to trace the steps the algorithms took to arrive at its conclusions (The European Approach to Excellence and Trust,
European Commission 20208). DIGITAL will describe how well XAI tools meet the explainability requirements of
various financial value chain stakeholders, develop non-perturbation-based XAI methods that preserve the natural
time ordering and dependence structures of the data and create methodologies to ensure that algorithmic systems do
not produce socially biassed outcomes that exacerbate inequalities.
WP 4 uses the Blockchain technology for digital innovations. Blockchain is another driver of the technological
change of the financial ecosystem (The European Commission’s Blockchain strategy, 20219), though there are no
systematic studies that assess whether the benefits outweigh the costs. DIGITAL will analyse the efficiency of
financial service providers that adopt the blockchain technology, contribute to more robust and efficient financial
markets by understanding how to tokenize financial assets, reduce the risk of fraud and highly volatile crypto
assets and establish guidelines for a supervisory approach to machine learning for digital finance. WP 5 makes
Digital Finance more sustainable. Climate change and environmental degradation are becoming an existential threat
to Europe and the world (European Green Deal, 201910), though inevitably, transitioning to a sustainable future with
inclusive, green economies and resilient ecosystems is associated with many challenges. Within DIGITAL, we will
make financial strategies for sustainable investing more objective, optimised, integrated, and operational, measure
the social and environmental effects of sustainable finance, define the primary characteristics of sustainable
financial assets and instruments and overcome the most significant obstacles to implementing sustainable finance
strategies.
WP 6 will provide Doctoral Training in a new, innovative and interdisciplinary training structure for Digital Finance
scholars. The required subjects are currently not available or are so far included in very different academic
disciplines. They will be combined in a coherent study and training plan that will result in the formation of highly
qualified professionals as well as a long-term sustainable PhD programme, thus guaranteeing the competitiveness
of the European industry in a strategic domain such as that of Digital Finance, following the MSCA guidelines on
supervision.11
WP 7 ensures communication, dissemination and exploitation of our research results, both in the scientific and
public domain with substantial impact due to our focus on industry prototypes and use-cases.
DIGITAL’s research and training framework is tightly integrated into the European Digital Finance Package (EC
202012), with substantial involvement of the European Central Bank as one of our partners and will contribute
to one of the four EU’s key strategic orientations,13 namely the promotion of an open strategic autonomy through
6 https://digital-strategy.ec.europa.eu/en/policies/strategy-data
7 https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
8 https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf
9 https://digital-strategy.ec.europa.eu/en/policies/blockchain-strategy
10 https://ec.europa.eu/info/strategy/priorities-2019-2024/european-green-deal_en
11 https://op.europa.eu/en/publication-detail/-/publication/bb02d56e-9b3c-11eb-b85c-01aa75ed71a1/language-en
12 https://finance.ec.europa.eu/publications/digital-finance-package_en-
13 https://www.eeas.europa.eu/sites/default/files/horizon_europe_strategic_plan_2021-2024.pdf
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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022
the development of key digital, enabling and emerging technologies, sectors and value chains that further accelerate
the digital and green transition of Europe.
State-of-the-art research and beyond. Both academia and industry are increasingly faced with data characterized
with staggeringly high number of dimensions, high variability and high veracity. Utilising such data is accompanied
by many challenges. In WP 1, an unprecedented collection of innovative data quality methodologies and data
augmentation techniques will be applied to a wide variety of industry-relevant datasets. Overcoming the
obstacles of data quality and availability (through novel or extended NLP methods,14 deep generation of data,15 and
anomaly- and dependence detection models baked on network concepts) will contribute to the literature and further
provide a valuable tool for the European Finance industry to enhance product offerings, reduce financial market
risks, and work toward a European financial data space.
In WP 2, the emphasis will be placed on the deployment of complex AI models to pertinent financial problems.
For financial applications such as risk management, trading strategies, and client-centric financial products, AI
models trained and tested in closed, academic settings have shown great promise. Yet, real-world applications (in
open environments) are more challenging. Using industry-ready use cases, we will demonstrate the viability of
novel dynamic rating models,16 automated trading platforms,17 and market environments for RL agents18 in
real-industry settings for the first time. WP 2 will provide first-of-its-kind qualitative analysis of the primary
obstacles to deploying innovative technologies in industry and propose new solutions for resolving these obstacles
which in turn is a crucial step towards a widespread adoption of complex models in the financial sector.
WP 3 will address the crucial question of how to build trust in human-centric AI models as opposed to the currently
widespread AI black boxes,19 which do not meet the modern European requirements of explainability, trust and
unbiasedness. This WP will also utilise the outcomes of WP 2, as the precise methodologies for explainable AI will
depend heavily on the specific models developed. We will validate the applicability of state-of-the-art XAI algorithms
to financial applications and extend XAI frameworks, ensuring that complex models applied to financial use cases
satisfy the explainability requirements of different stakeholders within the finance value chain and do not reinforce
social biases. A qualitative evaluation of the comprehensive frameworks' insights into explainability will be made in
comparison to the baseline models. Through industry-ready use cases, we will demonstrate for the first time ever
the viability of the proposed framework for audience-dependent explanations,20 the novel timeseries XAI
methods, and the fair algorithmic designs.21
In addition, WP 4 will utilise the research progress made in the first three WPs to make use of Blockchain technology
and to design systematic studies aimed at advancing financial digital innovation. These studies will serve as a crucial
input for a new infrastructure that will be utilised in a wide variety of IT domains. This infrastructure will account for the
integration of multiple data sources, define a standard dictionary, eliminate ambiguity, and enable other teams to access
all customer data from a centralised repository, thereby ensuring interoperability. Defining and monitoring efficiency
measures will ensure the quality of the proposed frameworks. In addition, a qualitative evaluation will be conducted
based on the comparison of our proposed frameworks that go beyond the state of the art of traditional standard
approaches.22 This is intended to serve as the basis for a new blockchain-based financial infrastructure and as a
European industry standard for blockchain applications. In addition, WP 4 will also focus on proposing novel risk
management solutions to some of the main concerns around blockchain applications in finance, like fraud detection and
financial stability. Utilising concepts from network theory and a set of exploratory tools to improve statistical models, we
will develop industry-ready use cases for fraud detection in financial networks23 and propose a comprehensive and
dynamic risk index for cryptocurrencies.24
In WP 5, a novel study will be conducted to simulate and evaluate markets in order to replicate the relationship
between banks, financial institutions, and their retail and business clients in a sustainable environment that takes into
14 Li et al. (2022). Incorporating Transformers and Attention Networks for Stock Movement Prediction, Complexity. https://doi.org/10.1155/2022/7739087
15 Wiese, M. et al. (2020). Quant GANs: Deep generation of financial time series. Quantitative finance. 20. https://doi.org/10.1080/14697688.2020.1730426
16 Vana et al. (2021). Dynamic modelling of corporate credit ratings and defaults. Statistical Modelling. https://doi.org/10.1177/1471082X211057610
17 Cohen, G. (2022). Algorithmic Trading and Financial Forecasting Using Advanced AI Methodologies. https://doi.org/10.3390/math10183302
18 Karpe et al. (2020). Multi-agent reinforcement learning in a realistic limit order book market simulation. ICAIF '20: Proceedings of the First ACM International
Conference on AI in Finance. 30. https://doi.org/10.1145/3383455.3422570
19 Rudin, C. et al. (2019). Why are we using black box models in AI when we don't need to? Harvard Data Science. https://doi.org/10.1162/99608f92.5a8a3a3d
20 Hadji Misheva, B. et al. (2021). Audience-Dependent Explanations for AI-Based RM Tools: A Survey. Frontiers. https://doi.org/10.3389/frai.2021.794996
21 Hajian, S. et al. 2016. Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining. 22nd ACM SIGKDD 2016. Association for Computing
Machinery, New York, NY, USA, 2125–2126. https://doi.org/10.1145/2939672.2945386
22 Bruno Biais et al, The Review of Financial Studies, Volume 32, Issue 5, May 2019, Pages 1662–1715, https://doi.org/10.1093/rfs/hhy095
23 Ashfaq, T. et al. (2022). A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism. MDPI. 22. https://doi.org/10.3390/s22197162
24 Trimborn, S. and Haerdle, W. (2018). CRIX an Index for Cryptocurrencies. Journal of Empirical Finance. 49. https://doi.org/10.1016/j.jempfin.2018.08.004
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account various factors (e.g., Green AI25 and green credit score26). To evaluate markets, numerous indicators and
metrics, including loan interest rate, customer attrition, CO emissions, customers' access to credit, and firms' profits,
2
will be compared. We will use agent-based modelling for the first time to simulate different market scenarios in which
agents involved in the industry take sustainable actions.27 Long-term financial growth will be analysed, and the
findings will inform the development and modification of industry policies and strategies. This will ensure that
sustainability considerations are integral to the Digital Finance industry, contributing to the European Green
Deal in the process.
Existing doctoral training programmes. Across Europe and globally, many separate PhD graduate schools exist in
the different disciplines involved, most notably Finance (incl. many Ivy league Finance programmes), Computer
Science, Applied Mathematics and Economics. Those are very-well established programmes with a long history
of achievements. Two past doctoral training programs related to Finance and Data Science topics have been funded
by the Marie Skłodowska-Curie Innovative Training Network and have offered interdisciplinary training to
candidates (Data Engineering for Data Science (DEDS),28 Training for Big Data in Financial Research and Risk
Management (BigDataFinance)29).
Gaps in existing training schemes. Existing programs, however, are insufficient for the new era of Digital
Finance, which requires a comprehensive set of intersectoral and interdisciplinary competencies. None of them can
provide the necessary skill set, as the necessary courses are either not provided or taught in separate silos, not to the
same cohort of DCs. Courses that are essential, but not offered in classical Finance PhD programs are e.g.: big data
analytics and data management, deep data generation, reinforcement learning, XAI, blockchain technologies and
network theory. These gaps will be closed by our doctoral training, which combines seven disciplines with a unique
training offer, also for transferable skills, from some of the leading European institutions, such as full access to all
training courses offered by the European Central Bank, EIT Digital, the European Consortium for
Mathematics for Industry, and world-renowned universities.
We fill the interdisciplinarity void with a network of seven disciplines, Economics, Finance, Management,
Economic Development, Computer Science and Informatics, Applied Mathematics, and Political Science. In terms
of substantial inter- and intra-sectoral training (European Central Bank, Bank for International Settlements,
DeutscheBank, ARC, EIT Digital, Fraunhofer Institute), as well as training for sustainability and entrepreneurship,
we will close the gap that exists in current programs. Finally, our unique collaboration with industry ensures both
training in transferable skills and academic training related to the industry, most notably the European Central
Bank, making their in-house courses available.
Long-term sustainability of the Doctoral Training. All partners have fully committed to the work of the University
Business Forum30 and the results of the EUA DOC-CAREERS31 project, and will support the development of the
university partners' curricula and doctoral training with a long-term sustainable doctoral course offering and ensure
that the acquired skills more closely match the needs of our European industry. All courses and training offers will
last beyond the life-time of the MSCA Doctoral network and doctoral training rules will be harmonised, as
already agreed upon by all partners.
An ambitious project. DIGITAL is ambitious in that it seeks to train doctoral candidates and develop and disseminate
research on all topics pertaining to Digital Finance. The ideal trained personnel will be capable of addressing data
quality issues, developing, implementing, and supervising AI models, designing, proposing, and critiquing XAI tools,
implementing and managing Blockchain applications, and facilitating the transition of the industry's current tasks
toward the adoption of sustainable finance.
25 Schwartz et al. (2020). Green AI. Communications of the ACM, 63. https://doi.org/10.48550/arXiv.1907.10597
26 Akomea et al. (2022). A review of studies on green finance of banks, research gaps and future directions. J of Sustainable Finance & Investment, 12.
https://doi.org/10.1080/20430795.2020.1870202
27 Abdou, M., et al (2012). Designing and building an agent-based model. In Agent-based models of geographical systems (pp. 141-165). Springer, Dordrecht.
28 https://cordis.europa.eu/project/id/675044
29 https://cordis.europa.eu/project/id/955895
30 http://ec.europa.eu/education/higher-education/doc1261_en.htm
31 http://www.eua.be/eua-work-and-policy-area/research-and-innovation/doctoral-education/doc-careers
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The network follows an approach of broad comprehensive training complemented by cutting-edge research projects
executed by 17 DCs. Researchers will not only develop key scientific skills and propose novel technologies that are
on the frontiers of big data-, AI-, blockchain- and sustainability research, but they will also build easily deployable
solutions to the key hurdles that finance service providers face in using these technologies in production,
disseminating this to all stakeholders, incorporating feedback from industry and policy makers.
The central research question in DIGITAL is how innovative technologies, like big data, artificial intelligence and
blockchain, can be used to support Digital Finance in view of the emerging complexities: (i) high-dimensional,
highvariety, high-velocity dataset; (ii) limited samples of high-quality data to train various ML models, (iii) no
comprehensive pipeline for building and deploying complex ML models in real settings, (iv) no explainability
techniques that are specifically tailored to financial datasets and satisfy the explainability needs of various financial
stakeholders, (v) no industry standard for blockchain applications and (vi) no common ESG scoring framework.
All of these complexities are methodological in nature and in order to tackle them, DIGITAL will carry out pragmatic,
data-focused, inductive research using a combination of research strategies (case studies, experiments and
actions) and methods enriched with continuous cooperation with and feedback loop from industry and
regulators. We elaborate on the central layer of the research methodology, which combines academia, industry,
dissemination, and training with seven building blocks.
communication efforts (such as more than 120 academic events organised by our extended network since 2020),
which will enable us to collect feedback, stay abreast of the most recent developments, and shape the future of this
research direction. DCs receive extensive feedback from academics and other stakeholders, allowing them to expand
or shift their research focus and methods, as well as validate the effectiveness of their approaches.
DCs will routinely present their findings at DIGITAL seminars, and supervisors will be expected to incorporate new
findings into doctoral training programs. This will be accomplished primarily through the new modules developed
by the network (see Table 1.3.c), which will be continuously updated to include the new methods, technologies,
solutions, tools, and business models created by the network.
very large networks of researchers (more than 400 academics in COST, 170 institutions in ECMI) to collect
feedback and learn the latest techniques, as well as leading institutions, such as the European Central Bank and the
Bank for International Settlements.
Challenges. There are both data availability and methodological challenges. Data has already been used, but if
some new data cannot be shared easily, we will provide specialised legal solutions (NDAs), and acquire the required
licences. Additionally, in case the problem remains unsolved, we will employ data anonymization techniques that
enable data sharing or resort to synthetic data generation techniques (GANs). On the methodology-related
challenges, in cases in which the planned techniques do not lead to the envisioned outcomes, we will expand the
scope of models to be trained and tested and reevaluate the planned effort. In case of any deployment issues (i.e.
the developed methodologies are not fully suited to the IT constraints in which finance service providers operate),
we will offer the solution as a standalone product rather than as an incorporated service.
Digital Finance necessitates the integration of methods, information, data, techniques, tools, perspectives,
concepts, and theories from the seven essential disciplines that we combine in our network. Those are Economics,
Finance, Management, Economic Development, Computer Science and Informatics, Applied Mathematics, and
Political Science. This emphasis on interdisciplinarity will be incorporated not only into the design and
implementation of the research framework, but also into the training program.
Interdisciplinarity of Research Methods.
● Each IRP is developed at the intersection of multiple disciplines and requires input and feedback from
academic experts conducting research in various fields. Thus, each DC will be assigned three supervisors
with complementary skills and experience to receive inter-disciplinary guidance to meet research objectives.
In addition, all DCs will have access to our three international partner networks, with over three hundred
researchers from around the world working on AI and fintech technologies.
● All IRPs have an applied focus, each addressing a specific obstacle companies face when deploying
innovative financial technologies. To provide each researcher with the necessary applied guidance, an
industry supervisor will be assigned to lead and oversee the CDP's applied objectives.
● Regulatory and supervisory bodies play a crucial role in defining the business models of financial service
providers. To account for the regulatory/supervisory discipline as well, researchers will have the opportunity
to validate their research through secondments at the European Central Bank.
Interdisciplinarity of Training.
● All DCs will take an interdisciplinary set of courses, such as: classical finance (risk management, trading
and portfolio optimisation, etc.), computer and data science (synthetic data generation, blockchain in digital
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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022
finance, RL for finance, etc.), statistics and econometrics (risk quantification, dependence structure in
highfrequency data, etc.), database management systems (big data, etc.), and AI (digital finance regulation,
monitoring practices, etc.).
● All DCs will receive transferable skills training, including tutorials from industry. This training will also
be highly interdisciplinary, covering very diverse industry areas, such as large banks, innovative Fintech
companies, consulting companies, international research centres and the European Central Bank.
Industry will focus on providing data-sets and applied research topics, as well as converting prototypes and
use cases to financial products, and training DCs in transferable skills and applied research.
Table 1.2.a Role of each consortium member in the research program
Consortium member Role
All partners Co-setting the specific research objectives within their respective IRPs, co-supervising DCs, and collaborating with
their respective industry and academic co-supervisors.
Project coordinator. WP 6, 8 Lead. Contribute data on crowdfunding, financial time series and ESG scores as well as
theoretical and applied expertise in ML modelling, Fintech business models, sustainable finance, blockchain
University of Twente
technologies.
WP 2 Lead. Contribute theoretical and applied expertise in building supervised and unsupervised ML systems, AI and
WU Vienna
financial technology, applied mathematical programming.
WP 5 Lead. Contribute theoretical and applied expertise in analysing complex categorical data structures, survey
University of Naples
analytics, latent models, link functions and gender studies.
Kaunas University of Ensure coherence in the research and applied objectives within each IRP. Contribute theoretical and applied expertise
Technology in blockchain-based conditional payments, stochastic modelling, supervised learning algorithms.
Bucharest University WP 4 Lead. Contribute data on cryptos, SMEs financial data as well as theoretical and applied expertise in building
of Economic Studies predictive ML and DL models for finance, clustering and community detection algorithms, case-study analysis.
Babes-Bolyai WP 1 Lead. Contribute theoretical and applied expertise in spatial econometrics, text mining and NLP, fraud detection
University systems, panel data modelling.
Poznan University Contribute data on cryptos, theoretical and applied expertise in statistics, cryptocurrencies, credit risk, predictive
analytics in digital finance
University of Contribute with theoretical and applied expertise in machine learning, time-series forecasting, credit portfolio analyses,
Kaiserslautern-Landau data analytics, impact of innovation in Fintech
Cardo AI Contribute data on private debt investment and expertise in building software for transparent data analytics.
Raiffeisen Bank Contribute data, training on financial research, and overall expertise in retail banking, asset management.
Swedbank Contribute data, training on retail banking, and expertise in asset management and other financial services.
DeutscheBank Contribute with various datasets, provide expertise on applications of AI, especially anomaly detection and early
warning systems, as well as expertise on predictive analytics, semantic analysis and risk management.
Royalton Partners Contribute data on cryptos and expertise in portfolio and risk management, compliance and regulatory reporting.
Bern Business School WP 3, 7 Lead. Contribute data on SME and personal loan performance and ESG scoring as well as expertise in
algorithmic design, ML and DL models for credit risk management, deploying explainable AI systems.
ARC Contribute data and overall expertise in applied research, product development and infrastructure service provision
for digitising processes and societies.
EIT Digital Co-setting the specific training objectives within WP 7; ensure the excellence of the training programme, by bringing
in their experience from the European Master in Digital Finance.
Fraunhofer Institute Contribute data and overall expertise in enabling efficient and sustainable transfer of scientific knowledge into
commercial use. Exposure to a world-leading applied research environment
European Central Contribute various macro-economic datasets as well as overall expertise in banking supervision, statistics, foreign
Bank exchange operations, payment systems and defining monetary and fiscal policy.
Bank for International Contribute macro-economic datasets, ongoing projects as well as overall expertise in banking supervision, statistics,
Settlements foreign exchange operations, remittances, payment systems, central bank digital currencies and defining monetary and
fiscal policy.
In addition to all WPs strictly adhering to diversity considerations, WP 3 is specifically devoted to fair, trustworthy,
and unbiased artificial intelligence.
Gender dimension and other diversity aspects in the project's research and innovation activities. DIGITAL will
represent EU values of sex and gender equality and diversity, believing that research and innovation must recognize
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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022
and integrate these issues for societal relevance and quality, following the EU policy report on Gendered
Innovations.32 Sex and gender will be incorporated into its research design. DIGITAL's strategy will be:33
Adherence to all mandatory and recommended open science practices. DIGITAL has extensive experience with
and will adhere to all mandatory open science practices outlined in the Horizon Europe Programme Guide36, the
obligations in Articles 17 of the Model Grant Agreement, and the corresponding guidelines, monitored by the SB.
We will even go above and beyond these requirements by extending the adherence to all associated partners.
All partners will also adopt all recommended practices and attend a mandatory open science course.
Mandatory training and institutional changes. We will actively change all beneficiaries' institutional governance
to ensure open science practices last beyond the project's funding.
Open Science practices are a common standard in our discipline. Early and open sharing of research will be done
via publishing preprints at arxiv and pre-registering research plans and reports at OSF and Protocol Exchange.
Research Data Management is achieved by using DOIs for all digital objects and metadata frameworks (incl. data,
publications and other research outputs), using free and open source licences. Zenodo is our trusted repository.
Based on the European Open Science Cloud (EOSC), reproducibility will be guaranteed, via using the DMP,
FAIR principles, and GitHub for storing and managing code. Open access to research outputs will be mandatory
for all partners, via publishing in open-access and open-peer review journals such as Open Research Europe
(Consortium members already have a leading role).
Citizen, civil society and end-user engagement will be ensured via co-designing, co-creating and co-assessing
activities. We have dedicated workshops with civil society as well as an annual Open Science day. Already in 2010,
our partners (coordinated by ASE) have built their own open science ecosystem for sharing datasets and software
with more than 1000 software pieces and 2000 datasets collected, used by more than 500 researchers from our
disciplines.37
DIGITAL's data management planning (WP 8) relies on the infrastructure, tools, and support of each partner and
their data management departments (incl. dedicated Data Stewards). During months 1-6, data for each IRP will be
collected and integrated into DIGITAL's systems. Tests will ensure data is securely stored and shared. DIGITAL's
main goal in WP 1 is to propose a FAIR dataset (including software and metadata) for each IRP, accounting for:
which beneficiaries and partners will implement under the supervision of computer engineers and data management
experts. Partners will provide the use of resources at no cost. The SB will monitor data curation quality and DMP
compliance. Data will be permanently archived in OpenAIRE to support European research even after the project. A
detailed DMP inline with GDPR will be written that follows open access principles to research data, being updated
regularly according to new types and volumes of data used in each IRP. The DMP will contain sufficient
documentation of data and metadata, data workflows, software licensing, privacy policies, and intellectual property
rights. It explains when commercial data can be reused and how the data meets FAIR and EOSC standards.38
DIGITAL proposes developing and employing a wide range of AI systems (WP 1 - 5) for use in Digital Finance,
while adhering to the European Commission's AI39 guidelines on a consistent basis. By design, technical robustness
will be ensured (WP 3). These systems will be developed to become:
proposing an agenda for their meeting.
55 DIGITAL will comply to the gender balance while forming all management teams, following the European Commission guidelines (https://research-
andinnovation.ec.europa.eu/strategy/strategy-2020-2024/democracy-and-rights/gender-equality-research-and-innovation_en ) 57 https://www.desca-
agreement.eu/desca-model-consortium-agreement/
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Supervisory Board (SB). The Supervisory Board/ Steering Committee is composed of one representative from
each partner, all supervisors, all WP leaders, and two doctoral candidates who rotate annually. The SB is co-chaired
by the project coordinator and the EU representative. It will elect one representative to serve as the SB's diversity
coordinator. The SB will have a kick-off meeting at the start of the network activity, with bi-annual consecutive
meetings. The SB is in charge of approving and supervising the financial, marketing, and communication strategies.
In addition, monitoring and evaluating the progress of training and research milestones. The SB is also responsible
for serving as an internal audit team for the EB when it decides on budget allocation, organisational structure, and
consortium strategy.
External Advisory Board (EAB): The EAB will review DIGITAL's progress in training and research every six
months. The EAB will be composed of prominent academics and senior executives with dual responsibilities. They
will issue recommendations and brainstorm ideas for training and research programs, and they will also propose
corrective measures.
Doctoral Candidates Committee (DCC): The DCC has two members and one doctoral candidate representative
from each beneficiary, and its chair is chosen annually by the DCC's members. The DCC is responsible for
supervising training and facilities issues, ensuring DIGITAL's policies are consistent with their implementation,
engaging in discussions and voicing ideas and concerns to their representative on the SB, and ensuring engagement
of the DIGITAL network by promoting social events for doctoral candidates and all consortium members.
Research and Training Committee (RTC): The RTC is composed of two members each from the academic and
non-academic sector, and one representative from an associated partner. It is chaired by a designated training advisor
and co-chaired by a representative of an industrial partner. The RTC is responsible for setting the strategy on
organising courses and training activities, creating an individual training plan for doctoral candidates that takes into
account their career development, and assisting the consortium with supervision-related issues and conflicts.
Communication and Dissemination Board (CDB): The CDB will be chaired by a representative of an associated
partner and composed of a representative of DIGITAL's beneficiaries, a member representing the industrial partners,
another member representing the associated partners, and a member representing the doctoral candidates. The CDB
will be chaired by a representative of the beneficiaries, and its primary responsibilities will include ensuring that
multiple and appropriate audiences are targeted throughout the various communication campaigns, monitoring the
implementation of communication and dissemination strategies, and monitoring engagement from different
audiences in order to maximise the impact of DIGITAL results. A media communication officer will be appointed.
IP and Exploitation Team (ET): The ET will consist of a representative from each industrial partner and two
academic advisors designated by the partners. ET will be chaired by a representative of an industrial partner and will
be responsible for identifying potential products and services from each IRP's outputs, proposing and monitoring the
creation of roadmaps, prototypes, and software. In addition, through the adoption of open science practices, ET will
ensure the sharing of knowledge, skills, and data. All partners have substantial exploitation teams at their institutes.
To support us, we will have one designated IP officer and two officers for exploitation from within our network.
Project Coordinator Team (PCT): The PCT is chaired by the project coordinator. A dedicated technical manager
and financial manager will be assigned. The project coordinator will establish the network, manage the relationships
with the external advisory board and coordinate the overall work of the project and its implementation, both at the
technical and at the financial level. The coordinator will monitor the content and progress of each work package as
well as coordinate the cooperation among work packages and assure the effectiveness of the project, according to
appropriate measurement standards. In this respect, the coordinator will assign a technical manager to the project
which will work in coordination with the project coordinator to ensure that milestones are respected, and deliverables
are submitted in time with high quality.
Specifically, the technical manager will check with each partner ahead of the planned milestones and deliverables
whether they are going to be completed in time and with the required quality. The coordinator will monitor how the
financial resources allocated to each partner are employed, to ensure effectiveness of the project, keeping high quality
standards for the produced deliverables. In this respect, the coordinator will assign a financial manager to the project
which will work in coordination with the administrative offices of University of Twente to ensure financial
compliance and transparency. Specifically, the financial manager will check with the technical manager in advance
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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022
whether each partner respects the milestones and deliverables and in which proportion; and will assign the
corresponding budget.
The coordinator will be responsible for maintaining internal communications within the consortium, with the
supporting external parties and for reporting to the EC.
Figure 3.1. DIGITAL Network Organisation