Model risk and data - a symbiosis

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

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