Model risk management (MRM) and model validation teams, who act as model risk gatekeepers, need a completely new methodology to address issues with explainability, bias, and overfitting, among others.
- We present a solution for uniting all model validation and model risk management stakeholders around a common goal—deploying powerful AI models without compromising on their reliability, explainability, or speed of deployment.
- We introduce a new Causal AI methodology that successfully resolves many traditional risk and validation challenges associated with machine learning.
- Using a real-life case study of a staff retention model, we illustrate how our solution enables the deployment of transparent and reliable models.
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