Wednesday, 16 December 2020
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10:00 - 12:00
The following topics will be covered:
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- Why is it important to look at data and algorithmic quality?
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- Dimensions of quality – for data and for actuarial models
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- Let’s have a look at current regulation – what do professional standards and supervisors tell us?
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- Predictive modelling, Analytics, Machine Learning, AI (ML) – a short overview over algorithmic approaches
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- Transparency, explainability and fairness – why are they even more important for these approaches?
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- Let’s have a look at the further regulatory discussion – what do professional associations and supervisors think about ML?
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- A practical approach to increase explainability and manage risk around ML – including the discussion of a real-world case study
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(Time zone: CET - Central European Time)
