The successful applicant will join the Industrial Engineering and Business Information Systems (IEBIS) section of the High-Tech Business & Entrepreneurship Department (HBE) at the Faculty of Behavioural, Management and Social Sciences (BMS).
Background This Ph.D. position is one four positions at the University of Twente (UT) and one of 19 positions in the context of the international Marie Sk?odowska-Curie Actions project DIGITAL. For the general description of DIGITAL and its Ph.D. positions, please check this page. Information about all other positions is available at EURAXESS, if you would be interested in any of the other positions as well, clearly state that in your cover letter.
DIGITAL' main goal? To significantly advance the methodologies and business models for Digital Finance through the use of five interconnected research objectives:
- Ensure sufficient data quality to contribute to the EU's efforts to build a single digital market for data;
- Address deployment issues of complex artificial intelligence models for real-world financial problems;
- Validate the utility of pioneering eXplainable Artificial Intelligence (XAI) algorithms to financial applications and extend existing frameworks;
- Design risk management tools concerning the applications of Blockchain technology in Finance;
- Simulate financial markets and evaluate products with a sustainability component.
The challenge Reinforcement Learning (RL) has become a popular paradigm for automating decision-making under uncertainty in complex environments. Although deep RL has had several breakthroughs in recent years and proven impressive algorithmic performance in closed environments, it has not yet found its way to real-world applications in open environments. In practice, RL algorithms have to work with imperfect data, be integrated into existing ecosystems, and be of use to human decision-makers. Additionally, the financial sector is subject to heavy regulation and high standards concerning risk management, fairness, and explainability. Although successful integration of RL may enhance the quality of decision-making in digital finance, several hurdles need to be overcome. Thus, this Ph.D. project examines how RL can advance digital finance.
You will address several RL implementation issues in digital finance, including both technical challenges and domain-specific ones. Utility-based RL results will improve financial decision-making by developing multi-criteria analysis, extreme scenarios, and risk management methods. RL in decision support will be optimized for explainability, regulatory compliance, model abstractions, and human judgment. We will also examine technological challenges like digital twin environments, machine learning pipelines, and digital finance ecosystem integration.