Like Data Governance, predictive model also has its own governance process. There are multiple teams like but not limited to Core team, extended team, decision making/Steering committee & implementation team. This Governance process typically requires following steps.
1) Inputs : The generation of a request for a new or updated version of model
2) Model Need, Design and Direction: Technical process to validate the requirement, scope and high level implementation
3) Model Build: Creates the model and develops implementation requirements (along with legal and regulatory considerations)
4) Model Approval: Multistep approval process (technical, business, risk, legal) to affirm and ascertain the model
5) Model Implementation: Data integrity, end to end testing and detailed implementation
6) Monitoring: This process is done for post implementation monitoring and understanding the data drift.
In addition to this, there is a model review process at a regular frequency to decision on refreshing the model.
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