Quantifying Model Uncertainty with AI

quantifying model uncertainty with AI

Background information

A key challenge in model risk management is to quantify model uncertainty. This slide deck was used by Jos Gheerardyn in various courses on this topic. It highlights which machine techniques can be used to make quantification more efficient.

Author

Jos Gheerardyn, CEO & Co-founder at Yields.io

Jos Gheerardyn has built the first FinTech platform that uses AI for real-time model testing and validation on an enterprise-wide scale. A zealous proponent of model risk governance & strategy, Jos is on a mission to empower quants, risk managers and model validators with smarter tools to turn model risk into a business driver. Prior to his current role, he has been active in quantitative finance both as a manager and as an analyst.

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Simon Vanooteghem
Director of Sales

Efrem Bonfiglioli
Solution Engineer

In 2022, the Chiron Model Risk Management Platform has won the ‘Model Validation Service of the Year’ award at the Risk.net technology awards for the fourth year in a row. Yields.io is widely recognized as a technology leader in MRM, and is supported by global partners such as KPMG and IBM.