363-1100-00L  Risk Case Study Challenge

SemesterAutumn Semester 2019
LecturersB. J. Bergmann, A. Bommier, S. Feuerriegel, J. Teichmann
Periodicityyearly recurring course
Language of instructionEnglish
CommentLimited number of participants.

Please apply for this course via the official website (www.riskcenter.ethz.ch). Once your application is confirmed, registration in myStudies is possible.


AbstractThis seminar provides master students at ETH with the challenging opportunity of working on a real risk case in close collaboration with a company. For Fall 2019 the Partner will be Credit Suisse and the topic of cases will focus on machine learning applications in finance.
ObjectiveStudents work in groups on a real risk-related case of a business relevant topic provided by experts from Risk Center partners. While gaining substantial insights into the risk modeling and management of the industry, students explore the case or problem on their own, working in teams, and develop possible solutions. The cases allow students to use logical problem solving skills with emphasis on evidence and application and involve the integration of scientific knowledge. Typically, the cases can be complex, cover ambiguities, and may be addressed in more than one way. During the seminar, students visit the partners’ headquarters, interact and conduct interviews with risk professionals. The final results will be presented at the partners' headquarters.
ContentGet a basic understanding of
o Risk management and risk modelling
o Machine learning tools and applications
o How to communicate your results to risk professionals

For that you work in a group of 4 students together with a Case Manager from the company.
In addition you are coached by the Lecturers on specific aspects of machine learning as well as communication and presentation skills.
Prerequisites / NoticePlease apply for this course via the official website (www.riskcenter.ethz.ch/education/lectures/risk-case-study-challenge-.html). Apply no later than September 13, 2019.
The number of participants is limited to 16.