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> The key here being that non-financial data isn't actually useful in predicting ability to repay loans. Sure it is. Even religion correlates with loan risk. (

> The key here being that non-financial data isn't actually useful in predicting ability to repay loans. Sure it is. Even religion correlates with loan risk. (Could be a proxy for social status and people from your tribe helping you out when you can't pay back). I could probably get a predictive model better than random guessing by mining your HackerNews comments or Facebook likes. > I'm sure it'd be used somehow by modern financial institutions if it were predictive. It is used. All data that is even remotely informative is used. To the fullest extend made possible by jurisdiction/ anti-discrimination laws.

diminoten 7y ago
Cool, I look forward to your evidence and data that you're using to contradict the story in the submission, because the very article states that if you tried to pitch a loan approval algorithm with any of the things you suggested in a modern bank, you'd be laughed out of the firm.
- throwawaywego 7y ago
  The evidence is in the article. It tells of a US company using 10.000 data points in a jurisdiction where this is allowed. The article cites Turner, who a few years back wrote this: > The non-financial data was found predictive in all three outcomes examined when no other ‘traditional’ credit information was used, strongly suggesting that alternative data would be useful to lenders in underwriting the so-called ‘no-file’ or ‘no-score’ consumer who have little or no payment/credit information available. I think his quote about "laughing out" is taken out of context. No predictive modeler will throw away informative features, because she can not distinguish noise from signal after 26 variables. That's 10 to 1% of a modern credit scoring model. It may be the perspective of a regulator though (they start drowning in noise after reviewing 100+ variables). Yes, all data that is legal to use and predictive, will get used, if not by you, then by your competitor. And informative variables that can not be used in the decision to give a loan, are used internally to predict if the loan will be paid back. There is more to credit scoring than the initial yes-no.
  - diminoten 7y ago
    The US company doesn't operate in the US, and the quote wasn't taken out of context. The context is that 10,000 was way over the 10 or so maximum number of actually valuable points of data. The whole article is about how utterly useless the vast majority of "data" ends up being, and how they are not used internally to predict if the loan will be paid back. So you are 100% in disagreement with this article, and forgive me if I trust a nationally published periodical such as Newsweek of a throwaway commenter on the Internet.
    - throwawaywego 7y ago
      Please reread the article. The article itself admits that non-financial data is useful and used (where this is allowed). I agree 100%. You drew the wrong conclusion about something you don't know a lot about and doubled down. Good luck with that and I forgive you.
      - diminoten 7y ago
        No, it says it's used incorrectly/wrongly. The whole article is about how hard it is to get an accurate view of a person's likelihood to pay a loan back, and the crazy (and generally inaccurate) ways lenders try to make up for that. You drew the wrong conclusion about something you don't know a lot about and doubled down. Good luck with that, though I don't honestly forgive you.

diminoten 7y ago

Cool, I look forward to your evidence and data that you're using to contradict the story in the submission, because the very article states that if you tried to pitch a loan approval algorithm with any of the things you suggested in a modern bank, you'd be laughed out of the firm.

throwawaywego 7y ago
The evidence is in the article. It tells of a US company using 10.000 data points in a jurisdiction where this is allowed. The article cites Turner, who a few years back wrote this: > The non-financial data was found predictive in all three outcomes examined when no other ‘traditional’ credit information was used, strongly suggesting that alternative data would be useful to lenders in underwriting the so-called ‘no-file’ or ‘no-score’ consumer who have little or no payment/credit information available. I think his quote about "laughing out" is taken out of context. No predictive modeler will throw away informative features, because she can not distinguish noise from signal after 26 variables. That's 10 to 1% of a modern credit scoring model. It may be the perspective of a regulator though (they start drowning in noise after reviewing 100+ variables). Yes, all data that is legal to use and predictive, will get used, if not by you, then by your competitor. And informative variables that can not be used in the decision to give a loan, are used internally to predict if the loan will be paid back. There is more to credit scoring than the initial yes-no.
- diminoten 7y ago
  The US company doesn't operate in the US, and the quote wasn't taken out of context. The context is that 10,000 was way over the 10 or so maximum number of actually valuable points of data. The whole article is about how utterly useless the vast majority of "data" ends up being, and how they are not used internally to predict if the loan will be paid back. So you are 100% in disagreement with this article, and forgive me if I trust a nationally published periodical such as Newsweek of a throwaway commenter on the Internet.
  - throwawaywego 7y ago
    Please reread the article. The article itself admits that non-financial data is useful and used (where this is allowed). I agree 100%. You drew the wrong conclusion about something you don't know a lot about and doubled down. Good luck with that and I forgive you.
    - diminoten 7y ago
      No, it says it's used incorrectly/wrongly. The whole article is about how hard it is to get an accurate view of a person's likelihood to pay a loan back, and the crazy (and generally inaccurate) ways lenders try to make up for that. You drew the wrong conclusion about something you don't know a lot about and doubled down. Good luck with that, though I don't honestly forgive you.