I had a look at LendingClub data and indeed unsexy RF and GB were pretty good. This shouldn't surprise us though, because what are the likely predictive feature

I had a look at LendingClub data and indeed unsexy RF and GB were pretty good. This shouldn't surprise us though, because what are the likely predictive features other than how much the person makes compared to what they want to borrow? Sure, there's what they intend to do with it, perhaps adjustments as well for how high their income is relative to where they live. All things that a model can help quantify, but nothing terribly surprising either. What's left is a bit of judgement as to whether an applicant is temporarily showing wrong data, eg if they're a student starting their first job, what is their income, low or high? But basically it means the bulk of cases can be done by machine and the loan officer looks at some corner cases. Note this person is also an actual person, i.e. capable of being held to account. I once worked with a prime broker who decided to waive the red flagged checks on a certain client in order to win the business. The guy blew up badly, the bank lost money, and he lost his job.