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