Dr. Thomas Krabichler

Curriculum Vitae


  • 2012 - 2017 Doctor of Sciences, Stochastic Finance Group, ETH Zürich
  • 2010 - 2013 Advanced Studies in Acturial Science (Actuary SAA), ETH Zürich
  • 2007 - 2009 Mathematics Teaching Certificate, ETH Zürich
  • 2004 - 2009 Master of Science in Mathematics, ETH Zürich

Professional Experience

  • 2020 - present Lecturer & Quant Researcher, Eastern Switzerland University of Applied Sciences, St. Gallen (OST)
  • 2018 - 2020 Lecturer & Quant Researcher, Lucerne University of Applied Sciences and Arts (HSLU - IFZ)
  • 2010 - 2018 Quantitative Finance & Risk Consultant, PricewaterhouseCoopers AG (PwC)
  • 2009 - 2010 Trading Desk Quant (Internship), Credit Suisse AG


  • ALM
  • Credit Risk
  • Deep Learning
  • Derivatives
  • Financial Modelling
  • Hedging
  • Machine Learning in Finance
  • Model Validation
  • Programming
  • Reinforcement Learning
  • Risk Quantification
  • Structured Products
  • Valuation


  • Analysis
  • Analytics
  • Credit Risk
  • Machine Learning
  • Mathematical Finance
  • Optimisation
  • Probability Theory
  • Quantitative Modelling
  • Risk Management
  • Statistics


  • 2020 - present Automated Collateral Management Systems (53978.1 INNO-ICT, 55591.1 IP-ICT)
  • 2020 - 2021 Gas Storage Optimisation
  • 2020 - present Goal-Based Investing
  • 2019 - present Deep ALM
  • 2019 - 2021 Optimisation of Hydroelectric Power Plants
  • 2019 - 2020 Predictive Credit Analytics with Neural Networks
  • 2019 - 2020 Quicktest of Credit Capacity for SMEs (37920.1 INNO-SBM)
  • 2018 - 2020 Reinforcement Learning for Pricing & Hedging of Derivatives


  • Einführung in Künstliche Intelligenz und Machine Learning in Finance. (2022). Guest Lecture, Certificate Program Blockchain & Fintech, University of Liechtenstein (FL).
  • Künstliche Intelligenz im Spannungsfeld zwischen Mensch und Maschine. (2022). SGKB Konjunktur- und Trendforum Horizonte, St. Gallen (CH), Live Broadcast.
  • What Can SMEs Learn from «ML in Finance»? (2021). CSEMnext, Alpnach (CH).
  • Balance Sheet Optimisation: Vom Bauchgefühl zur Wissenschaft mit AI und ML. (2021). BANKINGCLUB-Online-Forum, Panel Discussion, Köln (D), Webinar.
  • Prescriptive Analytics and Artificial Intelligence. (2021). Guest Lecture, CAS Digital Controlling, IFZ – Institute of Financial Services Zug (CH).
  • Deep Asset-Liability-Management. (2021). COST Fintech and Artificial Intelligence in Finance (FinAI), Webinar.
  • Datenbasierte Anwendungen aus der Praxis. (2021). Guest Lecture, Executive MBA HSG, University of St. Gallen (CH).
  • Rare Events in Financial Modelling. (2021). Data Innovation Alliance: ML-Clinic Expert Group Meeting, Berne (CH).
  • Machine Learning in Finance. (2021). Advisory Board Meeting «Banking East», St. Gallen (CH).
  • Machine Learning in Finance. (2021). Guest Lecture, EMBA, Solvay Brussels School of Economics and Management (B), Webinar.
  • Machine Learning for Pension Funds. (2021). Strategy workshop of a Swiss investment committee, Switzerland (CH).
  • A Deep Learning Model for Gas Storage Optimisation. (2021). Energy Finance Italia 6 Workshop, University of Brescia (I), Webinar.
  • A Deep Learning Model for Gas Storage Optimisation. (2021). SIAM Conference on Financial Mathematics and Engineering, Philadelphia (U.S.), Webinar.
  • Two Showcases of Deep ALM. (2021). SRA Chapter Event: New Frontiers in Data Analytics for Risk and Asset Management, Webinar.
  • Predictive Technologies for Better Business Lending. (2020). Professional Risk Managers' International Association (PRMIA), Singapore, Webinar.
  • Deep Replication of a Runoff Portfolio. (2020). ETH Stochastic Finance Group, Webinar.
  • New Frontiers in Quantitative Risk Management. (2019). IFZ Fintech Colloquium, Rotkreuz (CH).
  • Dynamic Financial Analyses with Reinforcement Learning. (2019). Expert meeting of an international insurance company, Switzerland (CH).
  • Machine Learning in Finance. (2019). Data Science Fundamentals, University of St. Gallen (CH).
  • Deep ALM. (2019). Minisymposium on Mathematical Finance in the age of Machine Learning, ÖMG Conference, Dornbirn (A).
  • Deep ALM. (2019). FPWZ Seminar, University of Padova (I).
  • Credit Risk Management. (2019). Board meeting of a Swiss retail bank, Switzerland (CH).
  • The Transformation of Treasury/ALM to Deliver Optimised Performance Management. (2019). Finastra Universe, Panel Discussion, Frankfurt (D).
  • Reinforcement Learning in Quant Finance: An Introduction for Non-Financial Experts. (2018). Swiss Data Alliance: ML-Clinic Expert Group Meeting, Schweizerische Mobiliar, Berne (CH).
  • A Joint Modelling Framework for Credit and Liquidity Risk. (2018). Workshop of the Freiburg-Strasbourg Research Group on Financial and Actuarial Mathematics, Freiburg Institute for Advanced Studies (D).
  • Term Structure Modelling Beyond Classical Paradigms. (2017). Doctoral Defence, ETH Zürich (CH).
  • The Jarrow & Turnbull Setting Revisited. (2017). 5th Imperial - ETH Workshop on Mathematical Finance, Imperial College London (UK).
  • Term Structure Modelling in the Presence of Multiple Yield Curves. (2016). Challenges in Mathematical Finance, University of Cape Town (ZA).
  • Term Structure Modelling in the Presence of Multiple Yield Curves. (2015). 3rd Imperial - ETH Workshop on Mathematical Finance, Imperial College London (UK).

Weitere Angaben

  • Fully Qualified Actuary within the Swiss Association of Actuaries («Sektion Aktuare SAV»)
  • Member of the Data Innovation Alliance
  • Member of the Swiss Risk Association (SRA)
  • Academic Research Partner of Ubinetic AG/youves, Zug
  • Advisory Board Member of Systemcredit AG, Schlieren
  • Independent reviewer of scientific journal articles in the field of mathematical finance

Publications and Essays

  • Hou, S., Krabichler, T., and Wunsch, M. (2022). Deep Partial Hedging. Journal of Risk and Financial Management, Vol. 15, No. 5, Article 223, https://www.mdpi.com/1911-8074/15/5/223.
  • Krabichler, T. and Wunsch, M. (2021). Hedging Goals. Preprint, arXiv:2105.07915.
  • Curin, N., Kettler, M., Kleisinger-Yu, X., Komaric, V., Krabichler, T., Teichmann, J., and Wutte, H. (2021). A deep learning model for gas storage optimization. Decisions in Economics and Finance, Vol. 44, pp. 1021–1037, https://link.springer.com/article/10.1007/s10203-021-00363-6.
  • Krabichler, T. and Teichmann, J. (2020). Deep Replication of a Runoff Portfolio. Preprint, arXiv:2009.05034.
  • Krabichler, T. and Teichmann, J. (2020). A constraint-based notion of illiquidity. Preprint, arXiv:2004.12394.
  • Krabichler, T. and Teichmann, J. (2020). The Jarrow & Turnbull setting revisited. Preprint, arXiv:2004.12392.
  • Krabichler, T. (2019). Reinforcement Learning for Pricing & Hedging of Derivatives - A Simplified Showcase. IFZ Working Paper Series.
  • Krabichler, T. (2019). If only there were no liquidity constraints. IFZ Working Paper Series.
  • Krabichler, T. (2019). If only we knew the drift. IFZ Working Paper Series.
  • Krabichler, T. (2019). Künstliche Intelligenz in der Finanzbranche - eine Utopie? IFZ Retail Banking Blog.
  • Krabichler, T. (2018). Term Structure Modelling Beyond Classical Paradigms - An FX-like Approach. Dissertation.


  • Stefan Borkert. (2022). Künstliche Intelligenz kann nicht alles. Press Article, St. Galler Tagblatt (15.03.2022).
  • Bechtiger, P., Spring, R. (2022). Orientierung statt Moneypulierung. Verlag SKV. (Machine Learning in Financial Planning).
  • Cuchiero, C., Larsson, M. and Svalutto-Ferro, S. (2018). Polynomial jump-diffusions on the unit simplex. Annals of Applied Probability. Vol. 28, No. 4, pp. 2451–2500.
  • Golnaraghi, M. (2018). Climate Change and the Insurance Industry: Taking Action as Risk Managers and Investors. The Geneva Association.