Using the ROC method to optimize controls

In the world of risk, analysts and managers alike try to reduce the likelihood of an event occurring by inserting controls between the event's driving factors and its outcome.

While the logic around this is sound, not all controls are equal and more often than not, some key risk indicators emit erroneous measures which mislead entire risk teams.
 
In this short post, we look at a method for weeding out erroneous control signals.
 
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