Risk-Rating
When we apply Risk-Rating when evaluating models.
For a high rating what does it actually mean?
Like if a model has a huge financial significance (for severity) and extremely complex (for Likelihood).
Does it make it high? What if it was tested extensively and validated using lots of different method, over a million simulations and even margin has been placed.
Would it still be high?
Does high mean its risky and dangerous? or does it simply mean it has the characteristic mentioned in the wiki?
I guess I am ultimately asking just because its complex, that doesn't make it high risk. If it is accurate and does a good job, would it still have a high rating?
Severity:
Financial significance
Importance of model
Frequency of use (high frequency of use means higher risk)
Likelihood
complexity of model (bells and whistles are nice but too many complicated features increase model risk)
expertise of users (if the user is a total dumb-ass then the likelihood of failure is epic!)
docs (crappy docs increase risk)
testing (if your assistant is posting to Instagram when they're supposed to be testing the model, then failure is near certain!)
Comments
You have just listed the definitions of a high risk model exactly at the end of your question.
"Does it make it high? What if it was tested extensively and validated using lots of different method, over a million simulations and even margin has been placed." It would still be high risk if the model is important or has financial significance, similar to the points you just described.
By definition, being risky and dangerous means the model has some financial significance, otherwise no one would care about it and it wouldn't be risky.
"I guess I am ultimately asking just because its complex, that doesn't make it high risk. If it is accurate and does a good job, would it still have a high rating?" If it meets the criteria that you just listed, then it is high risk, regardless of whether it is accurate or does a good job.