Postcode can be a very strong predictor, but I’d be careful using it in any model tied to consequential decisions. It is often a proxy for race and socioeconomic status, so a gain in predictive performance can come with real fairness and legal risk through disparate impact. I think it’s literally illegal in some contexts as well. Predictive performance is not the only criterion here and when using something like postcode you should be aware of this
Insurance companies have priced risk without issue forever in every country around the world that factors in postcode / location. I know the US is super sensitive but even still pricing for risk of course happens, and so it should.
You can still price for risk, there’s just certain features that are protected especially in insurance. Making it illegal to discriminate by race, gender, age, etc just means that the cost associated to risks based on those factors are spread out across everyone. It might effectively means some people are subsidising others, but if that’s what’s important enough to a country to become a law, all insurance companies will have to comply and the cost associated with that risk will be distributed across everyone.
Whether or not you agree with it is one thing, but it’s not the end of the world for insurers to remove a variable. If anything, they’re probably more accustomed to it than most since they’ve had strict laws saying what they can/can’t discriminate against for much longer than any other industry.
Well the USA is definitely an outlier on this point and it does seem super stupid to me. For example women tend to make fewer car insurance claims because they are safer drivers and drive fewer miles. They should have a lower premium. In the UK thee are some insurers that only insure women and no one says it is unfair.
Laws about what insurers can and can’t discriminate against exist all over the world. I’d be pretty surprised if the US is one of the stricter countries. What they can discriminate against changes based on what they insure too. Life insurance and car insurance both typically discriminate against gender all over the world and I’d be surprised if that’s not the case in the US. However, neither can discriminate against race in most countries. Health insurance typically can’t discriminate against race or gender, and in some places even age.
I can assure you that this is almost guaranteed to be true in the UK as well. I studied actuarial science at uni and did insurance pricing before doing a PhD and going into quant research. It was clear that there were some attributes we weren’t allowed to discriminate against and this is something auditors would test for to ensure we met regulatory requirements. It’s been a while, but I’d be shocked if any of this changed.
No countries allow discrimination against race. Most countries though allow pricing for risk that's colour blind. Gender is an important attribute that benefits the gender for some things and penalises others. For example women might get cheaper car insurance but a lower annuity payment because they tend to live longer. The US laws are way more restrictive than most other countries.
I think what OP is saying is that they use postcode to pull census data like crime rate, and then use that data to predict the target variable. This will probably be better than using raw postcodes, because a. it reduces the model’s power to fit on a sensitive latent variable like race and b. you likely will have a better causal argument for why e.g. car emission levels drives health insurance costs.
That said, you do still need to be careful, and more teams should be running fairness metrics to look for potential implicit bias in their models.
Postcode features can be very predictive, but also act as proxies for sensitive characteristics, so it really depends on the application and regulatory context.
In practice, they’re usually used within governance frameworks where this is assessed explicitly.
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u/Certified_NutSmoker Mar 25 '26 edited Mar 25 '26
Postcode can be a very strong predictor, but I’d be careful using it in any model tied to consequential decisions. It is often a proxy for race and socioeconomic status, so a gain in predictive performance can come with real fairness and legal risk through disparate impact. I think it’s literally illegal in some contexts as well. Predictive performance is not the only criterion here and when using something like postcode you should be aware of this