QED #0004: The FRM covers credit risk models. It doesn't really prep you for the fact that fair-lending testing starts by estimating race.

Something that caught me off guard moving into consumer credit. The curriculum trains you on PD/LGD, discrimination stats, model validation. What nobody mentions: for most consumer products the lender is legally prohibited (ECOA/Reg B) from collecting the applicant's race - yet is still required to demonstrate no disparate impact.

So before you can even test for a gap, you have to estimate the protected class you're not allowed to record. The standard tool is BISG - combine a race probability from the surname with one from the applicant's census tract, update with Bayes, and you get a per-applicant probability vector you can aggregate into disparity metrics.

It reframes the whole thing: fair-lending analysis is an inference problem sitting on top of a proxy, before it's a compliance exercise. And the proxy is imperfect enough that its accuracy is itself contested in enforcement.

What made it click for me was actually testing it. I had a set of loans where I knew the real self-reported race, hid it, and let BISG guess from surname + tract. Watching a clear 1.58x Black/White denial gap get quietly compressed to 1.39x by the proxy - same loans, just estimated race instead of observed - is what drove it home: in most fair-lending work outside HMDA, the "race" in your disparity test is a posterior probability, not a fact, and the estimation error almost always understates the gap.

Anyone else working in this space: how much did you have to learn on the job vs from any formal curriculum? And do you treat BISG output as good enough, or fight the methodology every cycle?