Programme Overview
Financial and insurance institutions increasingly face risks that are rare, interconnected, fast-moving or poorly represented by historical averages. Climate and environmental shocks, cyber incidents, liquidity stress, contagion and extreme losses challenge both traditional models and purely data-driven solutions.
At the same time, modern statistical learning, machine learning, deep learning and generative AI provide powerful ways to work with complex data, detect structure, construct scenarios and monitor changing behaviour. These methods create value only when they are grounded in an appropriate risk framework, validated carefully and used with a clear understanding of their limitations.
This CAS brings those elements together. It develops rigorous foundations for quantitative risk analysis and connects them to contemporary AI methods, realistic applications and responsible model governance.
Curriculum 2027
Module 1 - Quantitative Foundations for Risk Analysis
Module overview. The module combines mathematical tools for risk analysis - extreme value theory, large deviations, dependence models and point processes - with the foundations of quantitative risk management, including risk concepts, pricing, aggregation, diversification, capital, capital allocation and internal models.
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Credits |
4 ECTS |
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Teaching allocation |
5.5 days March 2027: 5, 6, 12, 13, 19, 20 (half day) |
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Module lecturers |
Prof. Ashkan Nikeghbali; Dr. Michel Dacorogna |
Module 2 - AI for Risk Modelling, Validation and Monitoring
Module overview. The module introduces statistical and model-based learning, supervised and unsupervised machine learning, deep learning and black-box decision search.
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Credits |
3 ECTS |
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Teaching allocation |
5 days April 2027: 16, 17 (half day), 23, 24 May 2027: 21 (half day), 22 |
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Module lecturers |
Prof. Reinhard Furrer; Prof. Anastasiia Koloskova; Prof. Nicola Serra; Prof. Jan Wegner |
Module 3 - Explainable and Ethical AI
Module Overview. This module gives insurance and finance professionals the concepts and practical tools required to explain, audit and improve machine-learning models used in risk analysis. It covers interpretable models, black-box explanations and responsible-AI practices built on fairness, ethics, accountability, explainability, privacy, security and governance. It also situates these tools within current industry practice in explainable AI. Each concept is illustrated with insurance cases such as pricing, scoring and underwriting and connected to the regulatory frameworks named in the source material: the EU AI Act, the NIST AI Risk Management Framework and US state insurance bulletins on algorithmic bias.|
Credits |
2 ECTS |
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Teaching allocation |
1 day May 2027: 29 |
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Module lecturer |
Dr. François Hu |
Module 4 - Applications Studios: Emerging Risks
Module overview. The module frames emerging risks, considers model strategy and risk mitigation, and develops applications in cyber risk, liquidity and systemic risk, and climate/environmental shocks.
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Credits |
3 ECTS |
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Teaching allocation |
3.5 days June 2027: 4, 5, 18, 19 |
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Principal application areas |
Cyber risk; liquidity and systemic risk; climate risk |
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Module lecturers |
Prof. Alexandre Bovet; Prof. Caroline Hillairet; Prof. Reinhard Furrer; Prof. Jan Wegner |