MSCA Individual Research Projects
Strengthening European financial service providers through applicable reinforcement learning
- Host institution: University of Twente.
- Starting month: M3.
- Duration: 36 months.
- Pillar 1: Introduction to AI for financial applications (WWU, 4 ECTs), Work Package 2
Objectives
Reinforcement Learning (RL) has become popular for automating uncertain decision-making in complex environments. Deep reinforcement learning can make impressive algorithmic decisions in closed environments, but real-world applications in open environments are harder. This project examines how RL can advance digital finance.
Expected Results
The project will address several RL implementation issues in digital finance. Utility-based RL deliverables will improve financial decision-making by developing multi-criteria analysis, extreme scenarios, and risk management methods. RL in decision-support will be optimised for explainability, regulatory compliance, model abstractions, and human judgement. We will also examine technological challenges like Digital Twin environments, machine learning pipelines, and digital finance ecosystem integration.
Planned Secondments
- CAR, Altin Kadareja (CEO), M6, 18 months, applied research on Fintech innovations with Deep learning
- ECB, Lukasz Kubicki, M27, 4 months, training on EU principles, supervision policies and research
Modelling green credit scores for a network of retail and business clients
- Host institution: University of Twente.
- Starting month: M6.
- Duration: 36 months.
- Pillar 1: Sustainable finance (UNA, 4 ECTs), Work Package 5
Objectives
Some markets use green credit scores to assess SME credit risk in sustainable and circular economies. Simultaneously, network customers' default likelihood has been studied. This study develops and deploys green credit score models that account for customers' networks. We show the impact and give financial institutions methods to improve credit risk assessment and access.
Expected Results
Green credit score models will be developed and implemented. These models inform SMEs about their carbon footprint, their main risks in a low-carbon economy, and how to mitigate them. SMEs leading on sustainability could gain easier access to capital by demonstrating positive relationships between creditworthiness and sustainability, creating a fairer credit risk assessment that explicitly factors in sustainability metrics and encouraging low-carbon measures.
Planned Secondments
- SWE, Prof. Dr. Tadas Gudaitis, M12, 18 months, ESG and credit score modelling
- ECB, Lukasz Kubicki, M33, 4 months, exposure to globally leading central bank, research training on EU principles, supervision
Industry standard for blockchain
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
A recommender system to re-orient investments towards more sustainable technologies
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M12
- Duration: 36 months
- Pillar 1: Sustainable finance (UNA, 4 ECTs), Work Package 5
- Work Packages: WP5, WP6, WP7, WP8
Objectives
Recommender systems are well-known information filtering systems that suggest items most relevant to a user. To our knowledge, there are none that suggest investments in sustainable technologies and businesses. This project will develop and deploy a recommender system to help financial institutions and their clients invest in sustainable technologies.
Expected Results
The project informs user groups about investment sustainability. Sustainability KPI mapping and evaluation are project deliverables. The recommender system's explainability is crucial. Thus, the recommendations will be tailored to multiple user classes with appropriate explanations and interpretations. The system recommends sustainable investments, monitors portfolio performance, and dynamically updates financial and sustainable KPIs.
Planned Secondments
- RAI, Dr. Stefan Theußl, M24, 18 months, research exposure in a global business environment,
- ARC, Prof. Dr. Ioannis Emiris, M18, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Fraud detection in financial networks
- Host institution: WU Vienna University of Economics and Business, Austria (WWU)
- Starting month: M3
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
- Work Packages: WP4, WP6, WP7, WP8
Objectives
Detecting fraud is currently one of the most important topics in Finance. However, it is also one of the most complex, given that fraudsters typically represent and generate a highly dynamic system, requiring that the boundaries and objectives of any system designed to detect and reduce fraud be constantly adapted to new extrinsic structures. This enables the definition of not only a static fraud detection system, but also a dynamic AI learning system, particularly in relation to network analysis.
Expected Results
On a meta-level, a set of Machine Learning and Artificial Intelligence models will be defined to enable a research-based approach that can be applied directly in financial institutions. The models are defined in such a way that the outcomes of the learning process within the institutions can be used to define and design new algorithms from a scientific standpoint. The work on network algorithms during the process of designing Machine Learning environments, will result in the publication of seminal papers.
Planned Secondments
- RAI, Dr. Stefan Theußl, M6, 18 months, research exposure in a global business environment
- ECB, Dr. Lukasz Kubicki, M27, 4 months, exposure to globally leading central bank, research training on EU principles, supervision
Collaborative learning across data silos
- Host institution: WU Vienna University of Economics and Business, Austria (WWU)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Foundation of data science (BBU, 4 ECTs), Work Package 1
Objectives
Connecting several dozen different data pipeline components and integrating an excessive number of APIs to leverage siloed data is a significant barrier to the comprehensive implementation of AI-based systems in finance. Currently, very little research is devoted to addressing all of the challenges associated with training, testing, and deploying cutting-edge ML and DL methods while leveraging siloed data. We will concentrate on the data challenges that finance service providers face by proposing solutions to streamline data collection, resolve data quality issues, and structure data to support downstream processes.
Expected Results
APIs for integrating Machine Learning and Deep Learning algorithms into FinTech processes necessitate careful abstraction of the specified input and output, which is the responsibility of the researchers to simplify and aggregate the complexity. This project produced a large number of API definitions that are closely related to research papers in the fields of theory of Artificial Intelligence and Machine Learning, as well as theory of Finance applications in various sub-fields such as security and compliance. The API specification itself should not only be integrated into financial institutions' business processes, but should also provide fruitful input for new research papers that are of interest to readers and users of all involved fields of research.
Planned Secondments
- SWE, Prof. Dr. Tadas Gudaitis, M18, 18 months, research on prototype implementations, applied research
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Risk index for cryptos
- Host institution: Humboldt University Berlin, Germany (HUB)
- Starting month: M3
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
The cryptocurrency market is exceptional: it is volatile, with a constantly shifting market structure. As cryptocurrencies evolve into a class of investable assets, the need for an index product arises. We investigate the dependencies of tail risk events within cryptocurrencies, which entails identifying coins with high or low joint tail event risks. Based on this, we intend to develop a risk index for cryptocurrencies to measure joint tail events, which will be an important tool for communicating risks to the public and regulators.
Expected Results
Develop a risk index to understand, measure and forecast upcoming risk flows from all cryptocurrency market participants and risk drivers. The possibility of pinpointing co-tress components in a dynamic network context makes the tool versatile and flexible for Digital Finance. The index will provide a thorough understanding of cryptocurrencies, and measure the dependencies and spillover effects in tail risk events within cryptocurrencies. It helps investors to manage risks and support decision-making.
Planned Secondments
- ROY, Dr. Michael Althof, M6, 18 months, research in innovation-driven business, use-case implementation
- FRA, Prof. Dr. Ralf Korn, M27, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Detecting anomalies and dependence structures in high dimensional, high frequency financial data
- Host institution: Humboldt University Berlin, Germany (HUB)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Foundation of data science (BBU, 4 ECTs), Work Package 1
Objectives
Herding, a well-known financial anomaly, is thought to cause high volatility, volatile prices, and low liquidity (Bikhchandani and Sharma, 2000). Greed and herd behaviour caused the seventeenth-century tulip mania, the 1995–2000 Internet bubble, and the 2015 Chinese stock market crash. This project studies high-dimensional sentiment networks and herd behaviour on the stock market. To better fit investor sentiment, the project will calibrate the option pricing model, Stochastic Volatility and Correlated Jump (SVCJ).
Expected Results
The project will detect anomalies like herd behaviour and dependence structures in high-dimensional, high-frequency financial data. We plan to create a tail event-driven network that graphs or matrices the interconnections of a large panel to understand sentiment network mechanics. That will inform our herd behaviour detection and option pricing model calibration. 1) Publications in prestigious journals available via public repositories, 2) Presentations at prestigious conferences, and 3) Knowledge exchange
Planned Secondments
- INT, Prof. Dr. Marco Bianchetti, M18, 18 months, Analyse quantitative modelling & technology and risk
- ARC, Prof. Dr. Ioannis Emiris, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Audience-dependent explanations
- Host institution: University of Naples Federico II, Italy (UNA)
- Starting month: M3
- Duration: 36 months
- Pillar 1: The need for eXplainable AI: methods and applications in finance (BFH, 4 ECTs), Work Package 3
Objectives
To address the issue of explainability in complex models, the literature has proposed an ever-expanding list of post-hoc explainability methods that can be used to gain some understanding of the inner workings of complex models. However, explaining the inner workings of algorithms and their interpretation is entirely dependent on the target audience. The existing literature fails to match the growing number of explainable AI (XAI) methods with the varying explainability requirements of stakeholders. To promote the widespread adoption of AI-based systems in finance, additional research is required to map the requirements of explainable systems across the various stakeholders in the finance industry
Expected Results
Finance decision-makers and AI model builders don't understand XAI's capabilities or ESG's impact on society and economy. This project promotes dialogue and knowledge transfer between those camps. It facilitates AI, Sustainable Finance, and ESG Technology innovation and collaboration. The following channels will disseminate expected results: 1) technical reviews, newspapers, and magazines, 2) public events (workshops for results presentation), and 3) knowledge exchange with stakeholders and project partners.
Planned Secondments
- SWE, Prof. Dr. Tadas Gudaitis, M6, 18 months, policies for asset, sustainable fund management
- FRA, Prof. Dr. Ralf Korn, M27, 4 months, improve know-how transfer by using and implementing advanced financial models
Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Predicting financial trends using text mining and NLP
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Challenges and opportunities for the uptaking of technological development by industry
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Deep Generation of Financial Time Series
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Investigating the utility of classical XAI methods in financial time series
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure
Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns
- Host institution: University of Twente, The Netherlands (UTW)
- Starting month: M9
- Duration: 36 months
- Pillar 1: Introduction to Blockchain applications in finance (HUB, 4 ECTs), Work Package 4
Objectives
Cryptocurrencies and other digital assets have proliferated in recent years, elevating blockchain technology. Decentralised finance requires it. Blockchain is well-established in digital finance, but it lacks maturity and scalability. This project leverages the extensive and diverse DIGITAL network, which includes financial industry leaders, to set a blockchain industry standard.
Expected Results
The project will map and categorise blockchain technology literature and practice to create an industry standard. DIGITAL includes the ECB, multinational banks, and leading digital finance tech companies. This combination lets us analyse blockchain and find commonalities and desires that could lead to a standard. We will work on several blockchain use cases, inspired by industrial partners, to ensure that the results do not stall at a normative framework level but trickle down to concrete and relevant demonstrations of the standard implemented in digital finance.
Planned Secondments
- DEL, Alberto Ferrario, M18, 18 months, business modelling, research on use-cases and prototypes
- FRA, Prof. Dr. Ralf Korn, M12, 4 months, applied industry-research, exposure to world-leading research centre and infrastructure