I make credit scoring models for a living and I can tell you that in most countries you won't find "proxies" or any other strong variables just by applying "machine learning".
Usually when you try neural networks in this segment you end up with exactly the same variables and outcome as you would with a normal logistic regression with 10x the complications and a lot less stability of the model.
There simply is not enough input parameters that are significant to the outcome.
But it might be in the US where the field is less regulated and you are allowed to collect all kinds of information on the person that you could proxy something like ethnicity although I have not found ethnicity significant in any of my sets. We do get a some variables even if we are not allowed to use them. Again this might be different in the US.
Usually when you try neural networks in this segment you end up with exactly the same variables and outcome as you would with a normal logistic regression with 10x the complications and a lot less stability of the model.
There simply is not enough input parameters that are significant to the outcome.
But it might be in the US where the field is less regulated and you are allowed to collect all kinds of information on the person that you could proxy something like ethnicity although I have not found ethnicity significant in any of my sets. We do get a some variables even if we are not allowed to use them. Again this might be different in the US.