In this essay, we explain how applying model risk management principles on data transformation both lower risk and helps to extract more value.
The key points of this essay are:
- Poor data can lead to arbitrary large errors on model output
- Solving this problem requires both a technological solution as well as mathematical techniques
- Addressing data transformation from within the context of model risk allows for the early identification of value
- This approach leads to both added value and cost reduction
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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