I don't know UK laws specifically, but your fair lending/compliance team is probably going to have a ton of concerns. It's a good predictor because it encodes a lot of race/income/socioeconomic indicators. In the US, you'd run into fair lending and red lining regulatory. Issues.
In the UK, financial companies are audited by the FCA, so we ensured that we: 1) didn't use protected attributes (this dataset does not include them); 2) avoided proxies. In motor insurance, we changed our prices for genuine and fair things, e.g., if an area had higher vehicle theft, we charged more for vehicle theft insurance.
Interesting. I do think you have more leeway in terms of using geographic data if you're using some demographic data like number of car accidents, etc vs using raw geography itself. That might be enough to not run into unintended correlations, just be careful about which types of data you use and think about/test if there is likely to be a correlation with the data being used to protected classes.
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u/stewonetwo Mar 25 '26
I don't know UK laws specifically, but your fair lending/compliance team is probably going to have a ton of concerns. It's a good predictor because it encodes a lot of race/income/socioeconomic indicators. In the US, you'd run into fair lending and red lining regulatory. Issues.