University of Twente Student Theses
EU Regulation of Reinforcement Learning in Digital Finance
Geugies, Bart (2024) EU Regulation of Reinforcement Learning in Digital Finance.
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| Abstract: | The integration of Artificial Intelligence (AI) into Digital Finance has grown significantly in recent years, driven by the potential to enhance decision-making and operational efficiency. This study aims to explore the various key application categories of Reinforcement Learning (RL) in Digital Finance, and evaluate their regulatory feasibility by identifying the current and future regulatory challenges within the European Union (EU). Through a Systematic Literature Review and a Grey Literature Search, key application areas were identified, including Portfolio Management, Risk Management, Trading, and Tax Management. The findings highlight that while RL can significantly enhance financial operations, its deployment can be complicated by regulatory frameworks such as the EU AI Act, which categorises AI applications based on risk levels and imposes stricter regulations on high risk applications. Analysis reveals however that most RL applications in finance are expected to fall under minimal to limited risk categories, which imposes few to no restrictions on development and deployment. This research further recommends the need for adaptive regulatory frameworks and interdisciplinary studies to fully understand the wider impact of RL in Finance while ensuring regulatory compliance and consumer protection. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science, 86 law |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/101092 |
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