IMF researchers: digital footprint yields better credit assessment

Post by [deleted] » Sat, Feb 13, 2021, 4:04 PM UTC

numpad0 wrote: post #76

aneemzic wrote: post #63

Earlier quoted context omitted.

I don't agree that this has anything to do with political objectives. It's a question of ethics. My domain knowledge is irrelevant, the ML Modelling aspect is irrelevant. The discussion was specifically around whether it's ok to include inherent traits when determining the credit worthiness of an individual. If you think it is, that's fine. We might as well just taking the same approach to crime, and start locking in…

> It's a question of ethics. Which is an important thing that isn’t math.

Post by dredmorbius » Sat, Feb 13, 2021, 4:58 PM UTC

tryptophan wrote: post #122

zaptheimpaler wrote: post #112

Earlier quoted context omitted.

Have you considered the logical endpoint of your line of reasoning? Basically, loans should only be given to those who are 99% likely to pay them back. Anyone who is at risk of default, say because they lost their job should be denied, making a permanent descent into poverty and misery even more likely. Health insurance should only be given to those who are already healthy. Anyone who is sick enough to significantly…

The purpose of risk assessment isn't to only give loans/insurance to 0 risk people. It's to quantify the level of risk. Once you know the level of risk you can then give loans, adjusting the interest rate to make up for the risk. By hiding information from lenders, you actually make them less likely to give a loan, rather than more likely. An unknown level of risk is 100x worse than a known high level of risk.

Post by dredmorbius » Sat, Feb 13, 2021, 5:06 PM UTC

yabones wrote: post #138

This seems ridiculously simple to abuse. Set up a 'headless' chromium container with your social media cookies, set the user-agent to the very latest $5,000 Mac, and have it google stuff like "most exclusive golf clubs in xxx city" and "best winter tires to optimize insurance rates" Few days of that and you'll qualify for a mcmansion mortgage in the monoculture part of the suburbs where all the dentists live. Future…

Post by antihero » Sat, Feb 13, 2021, 5:12 PM UTC

Nextgrid wrote: post #134

hntrader wrote: post #36

Earlier quoted context omitted.

DDG is more popular in the tech-literate crowd, most of whom are earning large salaries and therefore we'd expect that DDG usage should be positively (not negatively) correlated with credit worthiness

Tech literacy is negatively correlated with the likelihood of falling for scams or predatory practices. Companies who typically make their money out of screwing their customers over (and relying on the fact that they don't know/don't bother to rectify the situation) might very well preemptively raise prices or outright deny service to people who they think wouldn't fall for their bullshit.

Post by dredmorbius » Sat, Feb 13, 2021, 5:16 PM UTC

thegrimmest wrote: post #42

throwaway189262 wrote: post #32

Earlier quoted context omitted.

More accurate isn't always good because creditworthiness is all about stereotyping people. Consider this example: lesbians are statistically more likely to divorce. The ML models decides that lesbians are a higher mortgage risk and raises their interest rates. It does this indirectly by raising interest rates on married couples with different last names and owners of Subaru vehicles. In another, the model sees that p…

A lot to unpack here, let me try. Are machine learning algorythms capable of stereotyping people? How accurate does a stereotype have to be before it's a useful predictor? If these models lead to good data, then what's wrong with them? If people who follow the NBA do in fact default on their loans more, shouldn't they pay higher interest? I agree that the healthy should not be compelled to subsidize the costs of heal…