In this module, students explore recent advances in machine learning with a focus on deep learning methods. These methods have become central to many state-of-the-art predictive tools that provide a competitive advantage in areas such as credit default prediction, pattern recognition, stock price forecasting, sentiment analysis, outlier detection, and natural language processing. The course introduces key deep learning architectures, including Feed Forward Neural Networks and Recurrent Neural Networks. Students apply these methods to problems such as credit default, customer churn, and stock price prediction. The module addresses interpretability challenges arising from the “black box” nature of deep learning models and discusses related regulatory and methodological considerations.
- Analyze business applications: evaluate case studies on how machine learning creates strategic value and competitive advantage in finance and assess implications such as opportunities, risks, and ROI
- Synthesize technical concepts: explain and differentiate core machine learning and deep learning methods, and connect them to business strategies to address financial challenges
- Apply model optimization and NLP techniques: design optimization strategies to improve model performance and apply NLP methods to extract insights from unstructured financial data
- Understand and employ large language models: develop frameworks for integrating LLMs into financial decision-making and apply prompt engineering to solve business problems
- Critically assess implications: evaluate ethical, legal, and business challenges of deep learning and NLP in finance and derive best practices for responsible AI use
This course is part of the prestigious part-time Master in Finance, conducted in English on Fridays and Saturdays on Campus Westend. It offers a valuable opportunity to network and gain expertise without committing to a full degree program. Upon completion, participants receive a Certificate of Participation. This course may be credited towards the Certificate of Advanced Studies (CAS) in Financial Technology Management when completed together with the other required courses within one academic year. As the number of seats is limited, we recommend to register early. If you're a GBS or Goethe University alum, explore our attractive alumni discount options.
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Next course offering: Summer Semester 2027. New dates will be announced soon.
Prof. Dr. Kevin Bauer
Dr. Kevin Bauer is Professor of Applied AI at Goethe University Frankfurt, integrated into Hessian.AI, and a bridge professor at the Leibniz Institute for Financial Research SAFE. His research focuses on explainable AI, causal machine learning, and human information processing at the intersection of technology and economics. His work has been published in leading journals, including Information Systems Research and Management Science. He has received several research awards, including the JAIS Best Paper Award, ISR Best Paper Award, and AIS Senior Scholar Award, and serves on the Editorial Review Board of Information Systems Research.
Candidates with an undergraduate degree (i.e., bachelor or equivalent) of 180 credit points (CP) based on the European Credit Transfer System (ECTS) can apply for our Open Programs. The allocation of credit points for work experience will appear on the Academic Transcript of Records and be titled ‘Prior Learning Assessment’.
Open Program applicants must submit a recent proof of English language proficiency at the higher B2 level per the Common European Framework of Reference (CEFR). The GBS Office of Admissions will accept test results of the following tests, taken no longer than two years ago upon the date of submission:
- TOEFL iBT®
- TOEFL iBT® Home Edition
- IELTS
- Cambridge Certificate of Proficiency in English
- English Language Level Test (ELLT) - Oxford International Digital Institute
Applicants may request a waiver of proof of proficiency in English in case one of the following conditions applies. The applicant
- is a native speaker of English;
- has graduated from an international baccalaureate program
- has completed their undergraduate or master-level studies in a program taught entirely in English;
- has worked in an English-speaking country for at least one year.
If the applicant has not taken any of the above tests when submitting the application, the applicant can upload a confirmation of the test registration to the online admissions portal.
Applicants who submit a language certificate older than two years but without an expiry date will be invited for an additional interview with a native speaker of English.
Proof of relevant post-undergraduate work experience of at least one year is to be submitted, e.g., a performance review, reference letter, or written confirmation from your employer.