MSCA Individual Research Projects
In the DIGITAL program, each doctoral candidate is dedicated to a specific research project with a common overarching goal: to advance the digitalization of finance.
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Towards a European Financial Data Space (WP1)IRP6 - Collaborative learning across data silos IRP8 - Detecting anomalies and dependence structures in high dimensional, high frequency financial data IRP13 - Predicting financial trends using text mining and NLP IRP15 - Deep Generation of Financial Time Series Work Package 1 Page
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Artificial Intelligence for Financial Markets (WP2)IRP12 - Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms IRP14 - Challenges and opportunities for the uptaking of technological development by industry Work Package 2 Page
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Towards explainable and fair AI-generated decisions (WP3)IRP1 - Strengthening European financial service providers through applicable reinforcement learning IRP9 - Audience-dependent explanations IRP16 - Investigating the utility of classical XAI methods in financial time series IRP17 - Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns Work Package 3 Page
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Driving digital innovations with Blockchain applications (WP4)IRP3 - Machine learning for digital finance IRP5 - Fraud detection in financial networks IRP7 - Risk index for cryptos Work Package 4 Page
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Sustainability of Digital Finance (WP5)IRP2 - Modelling green credit scores for a network of retail and business clients IRP4 - A recommender system to re-orient investments towards more sustainable technologies and businesses IRP10 - Experimenting with Green AI to reduce processing time and contributes to creating a low-carbon economy IRP11 - Applications of Agent-based Models (ABM) to analyse finance growth in a sustainable manner over a long-term period Work Package 5 Page
The DIGITAL program is designed to drive forward the digitalizations of finance by assigning each doctoral candidate a specific project that addresses different aspects of the field. These projects have specific objectives and expected outcomes, covering a wide range of topics such as: collaborative learning across data silos, detecting anomalies and dependencies in high dimensional financial data, language models, reinforcement and deep learning, eXplaianble AI, blockchain technology standards, and many more. Doctoral Candidates (DCs) are expected to produce at least three papers during their PhD program, with a minimum of two involving collaboration within their network. They must also participate in three conferences and present at two academic and one industry seminar each year. These requirements aim to promote collaboration, spread knowledge, and support the DCs' academic and professional growth while fully delivering the IRPs research objectives.