About
“Dutch PhD candidate with a disciplined approach and analytical mindset, showing interest in Artificial Intelligence (AI) within finance.”
Jens, 23 years old, is currently part of the MSCA (Marie Skłodowska-Curie Actions) Digital Finance doctoral (PhD) network. This PhD tries to go beyond the traditional Finance PhD education in a wide range of inter-sectoral applications: data quality, AI and Machine Learning (ML), Explainability of AI (XAI), blockchain applications and sustainable finance.
Now located in Switzerland, Jens has two MSc degrees in the areas of Financial Engineering & Management, Data Science & Business, and Enterprise Architecture & IT Management. Moreover, his BSc in Electrical Engineering forms an ideal basis for technical problem solving within the financial industry.
His technical proficiency spans across a diverse range of programming languages and mathematical knowledge. Next to technical expertise, he excels in communication and interpersonal skills, demonstrating a proven ability to work independently or collaboratively. This is demonstrated through various projects, publications, and positions at companies, aiming to support his interests.
His passion in the area of digital finance makes him eager to leverage his skills and passion in this field. With a strong drive for growth, Jens actively seeks new challenges and steps out comfort zones to continuously improve.
Key Achievements and Outputs
During the first year of his doctoral research, Jens Reil established a comprehensive foundation for evaluating explainable AI (XAI) methods in financial time series. An extensive literature overview has been created that investigates classical XAI techniques, such as SHAP, LIME, and Integrated Gradients, focusing on their applications (specifically the financial domain).
His empirical analyses revealed critical limitations of widely used explainability methods when applied to temporal financial data. These findings provide one of the first structured assessments of XAI reliability in digital finance and form the basis of an initial journal publication currently in preparation. Jens has presented this work in multiple research seminars within the DIGITAL network and to practitioner audiences, incorporating feedback into the ongoing development of improved evaluation techniques.
In parallel, Jens is going to engage with experts from the European Central Bank (ECB) and academic partners at the University of Twente and BFH. These collaborations help translate regulatory and industry requirements into measurable explainability criteria, strengthening the practical relevance of his research.
Research Interests
XAI, Machine Learning, Finance