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Prof. Dr. Thomas Krabichler

IFL Institut für Finance und LawKompetenzzentrum Banking und Finance

+41 58 257 12 18thomas.krabichler@ost.ch

Thomas is a professor at the campus in St. Gallen. His research focuses on applications of machine learning in finance. He is an associate member of the «Interdisciplinary Centre for Artificial Intelligence (ICAI)» and holds a doctoral degree from ETH Zürich in mathematics. His collaboration with Josef Teichmann was honoured with the «Swiss Risk Award» in 2020. Previously, he worked for about ten years as a quant specialist in the financial industry. In this role, he was mainly engaged in the valuation and hedging of financial derivatives for major investment banks in the UK, France and Switzerland. Current research interests include asset-liability-management (ALM), derivative pricing and hedging, modelling the dynamics of volatility surfaces, goal-based investing, automated market making, and machine learning.

Area of Expertise

  • Asset-Liability-Management (ALM)
  • Automated Market Making (AMM)
  • Credit Risk
  • Deep Learning
  • Derivatives
  • Financial Modelling
  • Hedging
  • Illiquidity
  • Investment Strategies
  • Machine Learning in Finance
  • Mathematical Finance
  • Model Validation
  • Programming
  • Quantitative Analyses
  • Reinforcement Learning
  • Risk Management
  • Risk Quantification
  • Scenario Generation
  • Structured Products
  • Term Structure Modelling
  • Valuation

Education

2012 - 2017 Doctor of Sciences, Stochastic Finance Group, ETH Zürich
2010 - 2013 Advanced Studies in Actuarial Science (Actuary SAA), ETH Zürich
2007 - 2009 Mathematics Teaching Certificate, ETH Zürich
2004 - 2009 Master of Science in Mathematics, ETH Zürich
1999 - 2003 Matura, Kantonsschule am Burggraben, St.Gallen

Professional Experience

2023 - present Professor, Eastern Switzerland University of Applied Sciences, St.Gallen (OST)
2020 - 2023 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

Teaching Experience

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

Projects

2025 - present Battery Energy Storage System (BESS) Optimisation
2022 - 2023 Establishment and Liquidation of a Long-Term Fund
2020 - 2025 Automated Collateral Management Systems (53978.1 INNO-ICT, 55591.1 IP-ICT)
2020 - present Bespoke Data Analytics
2020 - present Goal-Based Investing
2020 - present Optimisation of Gas Storages and Illiquid Portfolios
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

Memberships

  • Associate Member of the «ICAI Interdisciplinary Centre for Artificial Intelligence»
  • 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 Partner of the CQF Institute

Editorials and Reviewing

  • Independent reviewer of scientific journal articles in the field of mathematical finance

Awards

  • Swiss Risk Award 2020 together with Josef Teichmann (ETH)

Peer-Reviewed Journal Articles and Conference Proceedings

  • Werner Brönnimann, Pascal Egloff, and T.K. (preprint 2023, published 2024). Automated Market Makers and their Implications for Liquidity Providers. Digital Finance. Vol. 6, No. 3, pp. 573–604. https://doi.org/10.1007/s42521-024-00117-0. [peer-reviewed research article].
  • Holger Englisch, T.K., Konrad J. Müller, and Marc Schwarz (preprint 2022, published 2023). Deep Treasury Management for Banks. Frontiers in Artificial Intelligence. Vol. 6. https://doi.org/10.3389/frai.2023.1120297. [peer-reviewed research article].
  • Songyan Hou, T.K., and Marcus Wunsch (preprint 2021, published 2022). Deep Partial Hedging. Journal of Risk and Financial Management. Vol. 15, No. 5, Article 223. https://doi.org/10.3390/jrfm15050223. [peer-reviewed research article].
  • T.K. and Marcus Wunsch (preprint 2021, published 2024). Hedging Goals. Financial Markets and Portfolio Management. Vol. 38, pp. 93–122. https://doi.org/10.1007/s11408-023-00437-y. [peer-reviewed research article].
  • Nicolas Curin, Michael Kettler, Xi Kleisinger-Yu, Vlatka Komaric, T.K., Josef Teichmann, Hanna Wutte (published 2021). A Deep Learning Model for Gas Storage Optimisation. Decisions in Economics and Finance. Vol. 44, pp. 1021–1037. https://doi.org/10.1007/s10203-021-00363-6. [peer-reviewed research article].
  • T.K. and Josef Teichmann (preprint 2020, published 2023). A Case Study for Unlocking the Potential of Deep Learning in ALM. Frontiers in Artificial Intelligence. Vol. 6. https://doi.org/10.3389/frai.2023.1177702. [peer-reviewed research article].
  • T.K. (published 2019). If only there were no liquidity constraints. IFZ Working Paper Series. https://doi.org/10.5281/zenodo.2590926. [working paper].
  • T.K. (published 2019). If only we knew the drift. IFZ Working Paper Series. https://doi.org/10.5281/zenodo.2590924. [working paper].
  • T.K. and Josef Teichmann (2018). A Constraint-Based Notion of Illiquidity. https://arxiv.org/abs/2004.12394. [preprint].
  • T.K. and Josef Teichmann (preprint 2018, published 2024). The Jarrow & Turnbull Setting Revisited. International Journal of Theoretical and Applied Finance. Vol. 27, No. 3. https://doi.org/10.1142/S0219024923500322. [peer-reviewed research article].

Books and Research Monographs

  • T.K. (published 2017). Term Structure Modelling Beyond Classical Paradigms – An FX-like Approach. ETH Research Collection. https://doi.org/10.3929/ethz-b-000199168 [dissertation].

Professional Journals and Newspaper

Selected Contributions:
- Pascal Egloff, Sabrina Graf, T.K., and Rajesh Tharyan (2025). Let there be liquidity: Automated market making in small stocks’ markets. SSRN https://dx.doi.org/10.2139/ssrn.5147234. [preprint].
- T.K. (published 12th December 2024). Dezentral und digital: die nächste Generation der Handelsplattformen. IFL Newsletter. https://www.ost.ch/de/forschung-und-dienstleistungen/wirtschaft/iflinstitut-fuer-finance-und-law/aktuelle-themen/dezentral-und-digital-die-naechste-generation-der-handelsplattformen/. [blog post].
- Pascal Egloff, and Pascal Turnes (published 2023). Blockchain in der Finanzwelt. Verlag SKV. https://verlagskv.ch/produkte/blockchain-der-finanzwelt. [textbook].
- Wilfried Lux, T.K., and Marco Gehrig (published 1st April 2023). Unternehmerische Resilienz und Resilienzverlust. WEKA. https://www.weka.ch/themen/finanzen-controlling/iks-undrisikomanagement/risikomanagement/article/unternehmerische-resilienz-so-meistern-unternehmen-unvorhergesehene-krisen/. [trade journal article].
- Pascal Bechtiger, and Reto Spring (published 2022). Orientierung statt Moneypulierung – Machine Learning in Financial Planning. Verlag SKV. https://verlagskv.ch/produkte/orientierungstatt-moneypulierung/. [textbook].
- Tanja Millius (published 1st June 2022). Derivate 2.0. LEADER – Das Ostschweizer Unternehmermagazin. https://www.leaderdigital.ch/hauptausgaben/juni-juli-2022-501.html. [interview].
- Stefan Borkert (published 15th March 2022). Künstliche Intelligenz kann nicht alles. St.Galler Tagblatt. https://www.tagblatt.ch/wirtschaft/konjunktur-experte-am-stgaller-konjunkturforumsagt-kuenstliche-intelligenz-kann-nicht-alles-ld.2262950. [press article].
- Andreas Dietrich (published 8th April 2019). Künstliche Intelligenz in der Finanzbranche – eine Utopie?. IFZ Retail Banking Blog. https://blog.hslu.ch/retailbanking/2019/04/08/kuenstlicheintelligenz-in-der-finanzbranche-eine-utopie/. [blog post].
- Maryam Golnaraghi (published 1st January 2018). Climate Change and the Insurance Industry: Taking Action as Risk Managers and Investors. The Geneva Association. https://www.genevaassociation.org/publication/climate-change-environment/climate-change-and-insurance-industry-taking-action-risk. [trade journal article].
- Christa Cuchiero, Martin Larsson, and Sara Svalutto-Ferro (published 2017). Polynomial jump-diffusions on the unit simplex. Annals of Applied Probability. Vol. 28, No. 4, pp. 2451–2500. https://doi.org/10.1214/17-AAP1363. [research article].

Teaching related publications

  • T.K. (published 2022). Risikokalkül für eine Leasing-Gesellschaft. Open Education Platform (OEP) for Management Schools. https://doi.org/10.25938/oepms.319. [case study and teaching note].

Presentations

  • Deep ALM – A Journey from Simplicity to Depth (12th November 2025). Guest Lecture at the Executive School of the University of St.Gallen, Zürich. [professional audience, presentation].
  • Deep ALM – A Showcase and Best Practices (21st February 2025). Association of Swiss Cantonal Banks (VSKB), ETH Zürich. [technical audience, presentation].
  • AI in Action – Ramifications of Deep Hedging (15th November 2024). Guest Lecture in the CAS Artificial Intelligence, Rapperswil. [professional audience, presentation].
  • ML für Pensionskassen – Aktuelle Perspektiven für den Einsatz von KI (8th November 2024). Management and Board of Directors of the St.Galler Pensionskasse (sgpk), Appenzell. [board members, presentation].
  • AI in Action – Ramifications of Deep Hedging (4th November 2024). Data Science Fundamentals, University of St.Gallen. [undergraduates, presentation].
  • Kann KI mein Geld anlegen? (22nd September 2024). Data Science Talks, University of Hamburg (D). https://open.spotify.com/show/5T02RSRfup08oR2c5SEHit?si=2e38a1f31e984252. [general audience, podcast].
  • Exploring the Dynamics of Liquidity Pools: A Mathematical Approach (4th July 2024). Seminar of the Dipartimento di Scienze Economiche, Università di Verona (I). [academic audience, presentation].
  • Exploring the Dynamics of Liquidity Pools: A Mathematical Approach (27th February 2024). Digital Assets Switzerland, St.Gallen. [technical audience, webinar].
  • A Parametric Spot and Vol Surface Model for Equities (16th February 2024). Seminar of the ETH Stochastic Finance Group, ETH Zürich. [academic audience, presentation].
  • Ramifications of Deep Hedging (30th November 2023). Research Seminar of the Faculty of Mathematics, Informatics and Natural Sciences, University of Hamburg. [academic audience, webinar].
  • Automated Market Makers and their Implications for Liquidity Providers (14th September 2023). Applications of ML to Sustainable Finance & Energy Markets, Wolfgang Pauli Institute (WPI), Vienna (A). [academic audience, presentation].
  • Automated Market Makers and their Implications for Liquidity Providers (27th June 2023). 3rd Oxford ETH Workshop on Mathematical & Computational Finance, University of Oxford (UK). [academic audience, presentation].
  • Deep Asset-Liability-Management (28th September 2022). 7th European COST Conference on AI in Industry & Finance, ZHAW Winterthur. [professional audience, presentation].
  • Deep Treasury Management (26th September 2022). KI Erfahrungsaustausch Schweizer Inlandbanken, Zürich. [technical audience, webinar].
  • ML in Finance (19th September 2022). Public Lecture, St.Gallen. [general audience, presentation].
  • Einführung in Künstliche Intelligenz und Machine Learning in Finance (7th April 2022). Guest Lecture in the Certificate Programme Blockchain & Fintech, University of Liechtenstein (FL). [professional audience, presentation].
  • Künstliche Intelligenz im Spannungsfeld zwischen Mensch und Maschine (14th March 2022). SGKB Konjunktur- und Trendforum Horizonte, St.Gallen. [professional audience, live broadcast].
  • What Can SMEs Learn from ML in Finance? (30th November 2021). CSEMnext, Alpnach. [professional audience, presentation].
  • Balance Sheet Optimisation: Vom Bauchgefühl zur Wissenschaft mit AI und ML (21st October 2021). BANKINGCLUB – Online-Forum, Köln (D). [technical audience, panel discussion].
  • Prescriptive Analytics and Artificial Intelligence (1st October 2021). Guest Lecture in the CAS Digital Controlling, IFZ, Zug. [professional audience, presentation].
  • Deep Asset-Liability-Management (28th September 2021). COST Fintech and Artificial Intelligence in Finance (FinAI), St.Gallen. [academic audience, webinar].
  • Datenbasierte Anwendungen aus der Praxis (15th July 2021). Guest Lecture in the Executive MBA HSG Programme, St.Gallen. [professional audience, presentation].
  • Machine Learning in Finance (26th June 2021). Guest Lecture in the Executive MBA Programme, Solvay Brussels School of Economics & Management (B). [professional audience, webinar].
  • Rare Events in Financial Modelling (23rd June 2021). Data Innovation Alliance: ML-Clinic Expert Group Meeting, Berne. [technical audience, presentation].
  • A Deep Learning Model for Gas Storage Optimisation (3rd June 2021). SIAM Conference on Financial Mathematics and Engineering, Philadelphia (US). [academic audience, webinar].
  • Machine Learning in Finance (18th May 2021). Banking East: Advisory Board Meeting, St.Gallen. [board members, presentation].
  • Machine Learning for Pension Funds (30th March 2021). Strategy workshop for the investment committee of the St.Galler Pensionskasse (sgpk), St.Gallen. [board members, webinar].
  • A Deep Learning Model for Gas Storage Optimisation (23rd February 2021). Energy Finance Italia 6 Workshop, University of Brescia (I). [academic audience, webinar].
  • Two Showcases of Deep ALM (20th January 2021). SRA Chapter Event: New Frontiers in Data Analytics for Risk & Asset Management, Zürich. [professional audience, webinar].
  • Predictive Technologies for Better Business Lending (10th September 2020). Professional Risk Managers’ International Association (PRMIA), Singapore (SG). [professional audience, webinar].
  • Deep Replication of a Runoff Portfolio (24th April 2020). Seminar of the ETH Stochastic Finance Group, Zürich. [academic audience, webinar].
  • New Frontiers in Quantitative Risk Management (27th November 2019). IFZ FinTech Colloquium, Rotkreuz. [professional audience, presentation].
  • Dynamic Financial Analyses with Reinforcement Learning (11th November 2019). Expert Group Meeting, Helvetia Insurance, Zürich. [technical audience, presentation].
  • Machine Learning in Finance (8th November 2019). Data Science Fundamentals, University of St.Gallen. [undergraduates, presentation].
  • Deep ALM (19th September 2019). OMG Minisymposium on Mathematical Finance in the Age of Machine Learning, Dornbirn (A). [academic audience, presentation].
  • Credit Risk Management (12th September 2019). Management and Board of Directors of the Graubündner Kantonalbank (GKB), Arosa. [board members, presentation].
  • Deep ALM (17th May 2019). FPWZ Seminar, University of Padova (I). [academic audience, presentation].
  • The Transformation of Treasury/ALM (6th February 2019). Finastra Universe, Frankfurt (D). [professional audience, panel discussion].
  • Reinforcement Learning in Quant Finance: An Introduction for Non-Financial Experts (25th October 2018). Swiss Data Alliance: ML-Clinic Expert Group Meeting, Schweizerische Mobiliar, Berne. [technical audience, presentation].
  • A Joint Modelling Framework for Credit and Liquidity Risk (2nd October 2018). Freiburg-Strasbourg Research Group on Mathematical Finance, Freiburg Institute for Advanced Studies (FRIAS) (D). [academic audience, presentation].
  • Term Structure Modelling Beyond Classical Paradigms – An FX-like Approach (26th September 2017). Doctoral Defence, ETH Zürich. [academic audience, presentation].
  • The Jarrow & Turnbull Setting Revisited (28th March 2017). Imperial - ETH Workshop on Mathematical Finance, Imperial College London (UK). [academic audience, presentation].
  • Term Structure Modelling in the Presence of Multiple Yield Curves (7th July 2016). Challenges in Mathematical Finance, University of Cape Town (ZA). [academic audience, presentation].
  • Term Structure Modelling in the Presence of Multiple Yield Curves (5th March 2015). 3rd Imperial - ETH Workshop on Mathematical Finance, Imperial College London (UK). [academic audience, presentation].

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