> You can load in tons and tons of demographic data, and it’s disturbing when you see percent black in a zip code and percent Hispanic in a zip code be more important than borrower debt-to-income ratio when you run a credit model.
https://thenextweb.com/syndication/2017/12/29/future-fintech...
The fact that you can sometimes use ethnicity as a proxy for social status and other things just show the discrimination that happens in some places. But when you set all other factors equal someone from Asia, Africa, Northern Europe will have the same default rate. At least in the (European) countries I've run models in.
> We do get a some variables even if we are not allowed to use them.
Cool, so you had access to an ethnicity variable to measure its proxy power and significance? I feel this is important and very rare outside of Europe.
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.