New Technologies in Finance and Insurance

Technological advances, digitization and the ability to store and process vast amounts of data has changed the landscape of banking and finance. This course unpacks the technologies underlying these transformations and reflects on the impact they have on the financial world. The course covers changes in management as well. This lecture shares expertise from ETH researchers and professionals across emerging areas like machine learning, cyber security, distributed ledger technology, digital currencies and quantum computing.

The financial manager of the future is commanding a wide set of skills. Those skills range from a great familiarity with technological advances and a sensible understanding of the impact on workflows and business models. Students with an interest in finance and banking are invited to take the course, although they may not have any explicit theoretical knowledge of financial economics.

Next Course: Fall 2023, Fridays 14.00 to 16.00

Lecturers:

Bastian Bergmann (D-MTEC), Patrick Cheridito (D-MATH), Stephan Eckstein (D-MATH), Philipp Kammerlander (PHYS), Josef Teichmann (D-MATH), Roger Wattenhofer (D-ITET), Mario Wüthrich (D-MATH)

Learnings:

After taking this course, students will be able to

- Understand recent technological developments in Machine Learning and emerging technologies like Quantum Computing and how they drive transformation in banking, finance & insurance
- Understand the skill set needed in these technological domains.
- Reflect on the impacts this transformation has on workflows and applications.

Sessions:

Part 1: Introduction to Machine Learning

22.09: Session 1: Intro to ML - Part 1 (S. Eckstein, P. Cheridito)
29.09: Session 2: Intro to ML - Part 2 (J. Teichmann, B. Bergmann)
06.10: Session 3: Explainability, Trust, Ethics, (A. Ferrario) AI in Banking (P. Mangold, UBS)
13.10 Session 4: Intro to ML in Insurance (M. Wüthrich)

Part 2: Cases on ML in Finance

20.10 Session 5: ML in Finance - Case 1 (Sergio Herrero Lopez & Jakob Maciag UBS)
27.10 Session 6: ML in Finance - Case 2 (Marcos Lopez del Prado, ADIA)
03.11 Session 7: ML in Finance - Case 3 (Roger Wattenhofer & Béni Egressy)

Part 3: Emerging Technologies

10.11 Session 8: Intro Quantum Computing (P. Kammerlander)
17.11 Session 9: [Mid-Exam] + Guest Speaker tba
24.11 Session 10: Insights on ML & Quantum Computing in Finance (O. Kondratyev, ADIA)
01.12 Session 11: Digital Assets [M. Studach, SDX]

Part 4: Cases and Ethics of AI

08.12 Session 12: tba
15.12 Session 13: tba

Required competences

Because the course will cover topics like machine learning an understanding of these technologies is an advantage but not a necessity. The course will also go beyond technological advances and will also cover management-related contents.

In addition to weekly session there are optional exercises with more hands-on coding opportunities.

The course (3 ETCS) runs in the FAll Semester as an Elective Course in the D-MTEC curriculum (MSc and MAS) and the Master in Quantitative Finance Program.

Link to Course Catalogue