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A brief history and future of credit scores

economist.com

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Re: A brief history and future of credit scores

#41

Post by teekert »

I think it is important to realize that this is really a US thing. In my country people have a mortgage and that is usually it. The rest, we save money for and buy it when we have the money.

Our mortgages are pretty bad though and rent combined with housing prices pretty much adapt to the median income, imo, when rent goes down, housing prices shoot up to compensate and get monthly costs back at the same level where a median family need two working parents for a reasonable home in the city roughly. Now, rent has nowhere to go but up and housing prices will fall again, I predict.

Re: A brief history and future of credit scores

#42

Post by AnthonyMouse »

adaptiveValleys wrote: post #34
skybrian wrote: post #32

Earlier quoted context omitted.

Maybe the idea is that banks should have an incentive to find some better data that isn't a proxy for race? That is, getting closer to the ideal of judging people as individuals instead of as a members of prohibited groups.

I think that's the intent, but it seems to only have incentivized discovering another proxy for race. For example, I recently watched an infosec talk 'hacking your credit score.'[0] Where the presenter mentioned that Fair Isaac (a reporting agency mentioned in the article) has a parameter in their algorithm called 'HMA' (High Minority Area), that he found in an internal presentation. I think a solution would be any p…

> I think a solution would be any parameter that remotely gives a correlate for race should be entirely eliminated from scoring systems.

The problem is, that's every parameter. The outcome you're trying to predict (will borrower repay debt) correlates with race, so other things that correlate with that outcome also correlate with race.

What you really need to do is to include as many non-race factors as you can, to give people with a couple of negative factors (who are often black or hispanic) more chances to redeem themselves with the other ones, to get the false positive rate down as much as you can.

Because the false positives (and false negatives) are really the problem. The true positives are... well, true.

Re: A brief history and future of credit scores

#43

Post by cjsawyer »

lifeisstillgood wrote: post #39

I often think the most effective means to increase loan repayment rates is to provide the debtor with a effective accurate money management tool - sort of like Mint but better. I am supposedly a well educated intelligent software engineer and yet trying to get a single unified view of what I spent is outrageously challenging or requires discipline at a level of dieting. Apart from fraudsters, people who take out a lo…

That’s part of the problem. A budget is a plan, not a running total. You budget what you can spend then change what you do spend to match. (If only it were that easy, though. I’m human too)

Re: A brief history and future of credit scores

#44

Post by AnthonyMouse »

emadelwany wrote: post #9
dd36 wrote: post #6

Earlier quoted context omitted.

I get confused by those they claim some of these ML or NN algos are black boxes and so could be breaking the law. If you’re not inputting illegal info (like race, sex, national origin, religion, name, etc. or corollaries), then it’s not making its risk assessment on that basis. All you have to do is look at the inputs. It isn’t unknowable whether or not it’s breaking the law.

Let me try to clear your confusion: an input my seem innocent (e.g. zip code), but a zip code is likely to correlate to ethnicity and race in some regions. So even if the inputs seem legal, an ML model that’s sophisticated enough can derive illegal results that discriminate against certain populations.

> So even if the inputs seem legal, an ML model that’s sophisticated enough can derive illegal results that discriminate against certain populations.

That doesn't make any sense.

The old school discrimination (e.g. redlining) worked like this. They would find some factor that correlates strongly with black people (e.g. black neighborhood zip codes), then assign a weight for that factor based on how well it correlated with what they wanted to discriminate against (black people) rather than how well it correlated with what they were supposed to be measuring (creditworthiness).

You can certainly do that on purpose with ML, but the way you do it is to give the algorithm the data on which factors are associated with black people and then ask it to assign blackness scores rather than credit scores and use the blackness scores for making credit approvals. I am not aware of anybody stupid/racist enough to actually be doing that in 2019.

What you're supposed to do is to weight factors based on how well they correlate with the outcome you're trying to predict (e.g. loan repayment) and use those weights rather than the ones chosen purposely to discriminate on the basis of race.

That doesn't mean none of those factors will ever correlate with race. Everything correlates with everything to one degree or another. True independent variables are the exception rather than the rule. But weighting each factor based on how well it correlates with the outcome you're trying to predict rather than how well it correlates with race is maximally non-discriminatory -- doing something else would be purposely giving advantage to one race over another disproportionate to the best available information. And nobody who is not an overt racist has reason to fudge the numbers that way, because it would also make worse predictions and cause you to lose money.

Re: A brief history and future of credit scores

#46

Post by tareqak »

tareqak wrote: post #40
jetrink wrote: post #16

Earlier quoted context omitted.

A recruiting tool used by Amazon developed a bias against women despite not being told candidates' genders. It penalized candidates who were graduates of all-women's colleges and also those who had the word "women" in their resume (e.g. “women’s chess club captain.”) It had been trained on resumes submitted to Amazon during the previous ten years, so the tool's bias was likely reflective of real human bias in Amazon'…

From my reading of that article, I think the recruiting tool was fed resumés and a data point saying whether or not the corresponding candidate was hired or not. As a result, the tool not only developed a bias against women, but was effectively evidence that there was bias against women in the original hire / not hire decisions.

I just missed the deadline to edit my post, so I am replying to myself.

Looking at the parent comment again, I seemed to have just restated it without adding anything new of my own. I meant to add that my reasoning for why Amazon pulled development of this tool was not just because the tool’s bias, but also because that the existence of the tool and its associated training data could open up Amazon to litigation claiming that their hiring decisions were biased against women in ten year span referred to by the article.

Re: A brief history and future of credit scores

#48

Post by maccard »

teekert wrote: post #41

I think it is important to realize that this is really a US thing. In my country people have a mortgage and that is usually it. The rest, we save money for and buy it when we have the money. Our mortgages are pretty bad though and rent combined with housing prices pretty much adapt to the median income, imo, when rent goes down, housing prices shoot up to compensate and get monthly costs back at the same level where…

It's not really a US only thing - based on your username I'm assuming Dutch? - a quick Google says that there was 6 billion euro in new consumer credit in the Netherlands last year, and it's been much more than that even in the past decade. People in the Netherlands seem jsut as likely to buy cars on finance and rack up credit card debt as everyone else.

Re: A brief history and future of credit scores

#49

Post by teekert »

maccard wrote: post #48
teekert wrote: post #41

I think it is important to realize that this is really a US thing. In my country people have a mortgage and that is usually it. The rest, we save money for and buy it when we have the money. Our mortgages are pretty bad though and rent combined with housing prices pretty much adapt to the median income, imo, when rent goes down, housing prices shoot up to compensate and get monthly costs back at the same level where…

It's not really a US only thing - based on your username I'm assuming Dutch? - a quick Google says that there was 6 billion euro in new consumer credit in the Netherlands last year, and it's been much more than that even in the past decade. People in the Netherlands seem jsut as likely to buy cars on finance and rack up credit card debt as everyone else.

Oh.. must be my bubble then :)

Re: A brief history and future of credit scores

#50

Post by lifeisstillgood »

cjsawyer wrote: post #43
lifeisstillgood wrote: post #39

I often think the most effective means to increase loan repayment rates is to provide the debtor with a effective accurate money management tool - sort of like Mint but better. I am supposedly a well educated intelligent software engineer and yet trying to get a single unified view of what I spent is outrageously challenging or requires discipline at a level of dieting. Apart from fraudsters, people who take out a lo…

That’s part of the problem. A budget is a plan, not a running total. You budget what you can spend then change what you do spend to match. (If only it were that easy, though. I’m human too)

but an accurate and up to date running total of how you match to budget helps enormously (even if the budget is the default of "don't spend it all"
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