CAS ETH in ML in Finance and Insurance
The CAS ETH in ML in Finance and Insurance provides of a deep understanding of the intersection between machine learning technology and applications to foster innovation in the rapidly changing financial services landscape.
Application Window for Cohort 2025 open from 1. Nov 2024 to 31. January 2025
The fascinating success of Machine Learning (ML) in language processing or image recognition and lately also generation have triggered many fantasies to apply these technologies in other fields as well, including the area of finance, banking and insurance. This tremendous opportunity requires a new generation of leaders who combine deep industry knowledge to the understanding of ML methods in order to harness the power of innovation.
Explore in some detail what the CAS ETH in Machine Learning in Finance and Insurance has to offer.
Degree: Certificate of Advanced Studies ETH in Machine Learning in Finance and Insurance
Total ECTS: 15
Tuition Fee: CHF 13’000
Programme Start: March 1, 2024
Finish: End of November
Duration: 9 months
Application Window: 1. November 2023 to 31. January 202:
Please note: Admissions are rolling during the window. The CAS might be fully booked before closing the Application Window.
Live Chat Information Event (online): January, 15 - 12.15 – 13.00 Register here!
Qualifications: Professionals with a science and engineering back-ground who want to deepen their knowledge in machine learning and unlock its potential in the financial industry with minimum of two years of professional experience in finance, banking or insurance.
Coding Requirement: Prior coding experience is welcome but not mandatory. During the course you are expected to follow basic coding examples as a basic requirement for the credits. In addition, you have the opportunity to deep dive into coding for a specific project guided by faculty and professionals.
How to apply: Applications coordinated by the ETH School for Continuing Education
Contact for Information on the Programme & Application:
Bastian Bergmann, Executive Director ETH FinsureTech Hub
E-Mail:
The building blocks and preliminary dates for the CAS ETH Maschine Learning in Finance and Insurance
- Provides you with a comprehensive introduction to the fundamentals of machine learning, including key concepts, algorithms, and practical applications.
- You will gain a solid foundation in machine learning and develop the skills to build and evaluate machine learning models for various tasks in the following blocks and modules.
Offered as interactive lecture sessions with simple coding exercises to get you onboard: 8 Sessions à 4h at ETH Zurich or hybrid on Friday mornings.
- Get exposure to real-world case studies and projects in finance and insurance where ML methods have been successfully applied.
- Gain insights and understanding of the overall system landscape & architecture in which your machine learning model is embedded.
- Choose and deep dive into cases and applications guided by ETH faculty and professionals from finance, banking and insurance.
Structured as an interactive workshop on Fridays and Saturdays. Students select 3 out of 4 workshops offered between June, July and September. Workshops take place at ETH or at corporate facilities.
Preliminary Dates (tbc)
Cases in ML in Banking and Finance I
- Day 1: Friday, June 7 2024 - 9.00 - 17.00
- Day 2: Saturday, June 8 2024 - 9.00 - 16.00
Cases in ML in Banking and Finance II
- Day 1: Thursday, July 4 2024 - 9.00 - 17.00
- Day 2: Friday, July 5 2024 - 9.00 - 17.00
- Day 3: Saturday, July 6 2024 - 9.00 - 16.00
Cases in ML in Insurance I
- Day 1: Thursday, June 20.6 - 9.00 - 17.00
- Day 2: Friday, June 21.6 - 9.00 - 17.00
- Day 3: Saturday, June 22.6 - 9.00 - 17.00
Cases in ML: Startups and Fintech
- Day 1: Thursday, Sep 5 - 9.00 - 17.00
- Day 2: Friday, Sep 6 - 9.00 - 17.00
- Gain insights on key concepts creating innovation, like defining ideas, prototyping, iteration, growth, scaling, to create an innovation plan for a ML driven project in finance, banking or insurance.
- You can choose from a list of open projects provided by faculty and companies or you bring your own project.
- Projects can focus on machine learning modelling, the risks of machine learning-empowered services, the design of innovation with machine learning in finance and insurance, or a combination of the above.
This block starts with a one-day workshop about creating innovation in finance, banking and insurance with ML. Afterwards, during 6-8 weeks you work on a project, guided by faculty and a pool of mentors from industry.
Key Learnings
- The basics of machine learning, with a deep dive into selected cases of supervised and unsupervised learning problems in finance
and insurance, as well as deep learning methods and large language models. - The value chain of a machine learning project in industry, including the discussion of the IT architectures supporting machine
learning models, their training, validation, deployment and maintenance. - The most relevant interpretability methods, current strategies to foster trust in services using machine learning and ensure their
trustworthiness, as well as relevant regulations for a responsible use of machine learning-empowered technology. - The typical phases of the innovation process of a ML project in a corporate or Fintech innovation setting.
Lecturers
- Bastian Bergmann, Executive Director, FinsureTech Hub, ETH Zurich
- Patrick Cheridito, Professor of Mathematics, ETH Zürich
- Andrea Ferrario, Senior Researcher at the University of Zurich and Lecturer at ETH Zurich
- Josef Teichmann, Professor of Mathematics, ETH Zurich
Guest Speakers
Sharing insights on cases and applications is key to our CAS. Hence, building on the network of the FinsureTech Hub we integrate leading industry professionals to share their expertise, insights and experience.
Selection of Speakers:
- external pageLisa Bechtold, Head AI Governance, Zurich Insurance
- external pageChristiane Hoppe-Oehl, AI in Financial Markets, FINMA
- external pageLuca Baldassare, Lead Data Scientist Advanced Analytics Center of Expertise, Director, Swiss Re
- external pageMatteo Vagnoli, Senior Manager Analytics and Responsible AI, Swiss Re
- external pageJürg Schelldorfer, Lead Acturial Data Scientist, Swiss Re
- external pageKris Pawluk, Google
- external pageLukas Gubler, Head Risk Management and Valuation, Axpo Group
- external pageMarcos Lopez de Prado, Global Head - Quantitative R&D, ADIA and Cornell
- external pagePhilippe Mangold, Managing Director, UBS
- external pageDan Wunderli, Head of Data Innovation Lab, FINMA
- external pageGerhard Dolge, Head Competence Center Data & Analytics, AXA
- external pagePetter Kolm, NYU and Aisot Technologies
- external pageNino Antulov-Fantulin, Head of Research at Aisot Technologies AG, ETH-Zurich Spin-off
- external pageMatthias Braendle, Team Lead Data Strategy & Product Owner Data Science, Mobiliar
- external pageFlorian Wespi, Data Scientist, Mobiliar
- ...
- TBC: IBM, Swiss National Supercomputing Center, Selection of Fintech Startups, ...
Mentors
The role of the mentor is to support you on your Innovation Project. Background and focus of the mentors vary from more technical background to mentors with a more strategic role and senior experience. This will allow to offer individualized mentoring, tailored
to the individual projects characteristics.