Quantifying Model Uncertainty with AI

Background information

Quantifying model uncertainty refers to the process of estimating and measuring the level of uncertainty associated with a particular model or statistical analysis. This can involve techniques such as sensitivity analysis, uncertainty analysis, and risk assessment to identify and evaluate sources of uncertainty and the potential impacts on the model’s results.

Quantifying model uncertainty is an important aspect of model risk management, as it helps to ensure that the model is accurately reflecting the underlying data and is reliable for decision-making purposes.

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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