The whole point of the model is to find who not to lend to. You are always going to exclude people by definition.

There are so many ways you can accidentally systematize racism in software like automated lending.

In the past there were explicitly racist policies like redlining. This leads to a historical data set of loan denials to people in specific racial group. If that group has other traits that correlate to their race, e.g. the neighborhood they live in then you could presumably have a model that doesn't explicitly have race as a feature but uses that historical data and some subset of racially correlated features and as a result disproportionately excludes people of that race.

I am not sure how one would remove all ageism, sexism, racism, classism, title-ism, and so on from lending. The whole concept is about making a prediction about the future with sub optimal information, guessing who will default on a loan and who won't. Same goes with insurance.

I have been pretty tempted to lie about where I live in order to reduce my insurance costs. It would reduce the insurance cost by half. It seems pretty disproportionately harsh that I should get lumped together with the people who simply happen to live around me.

Is it possible to make predictions illegal if they are based on historical data from other than the individual customer?

I should clarify, the point is to not discriminate against a protected class.

Tell that to the legislators and prosecutors who create laws and enforce laws against you.

Yes, but we should exclude people for valid reasons, not for their race.

A review doesn't necessarily mean you need to resolve all diversity/inclusion issues. It can merely require that you identify the issues and understand the risks of not resolving them.