3 ms·

You can't tell how well these features correlate without knowing what the population of non-fraudsters is that satisfy each feature.

Houshalter 11y ago
Presumably they have that data. That's how they found these correlations.
- throwaway2048 11y ago
  that is not something that should be presumed, this kind of error is super common, especially with people that are otherwise not statistically well grounded. if 1% of transactions are fraudulent, and these indicators have only a 1% false positive rate (which it has nowhere close to), and it will match the fingerprint of every fraudulent transaction (which it wont) you get an absolutely titanic false overall positive rate, somewhere around >50% false positive. This is not some trivial pedantic thing, its a very important aspect of statistical models that must be explicitly stated to be accounted for. It is not enough to say "every y has characteristic X" if no other information about X is provided.
  - URSpider94 11y ago
    These people sell anti-fraud services to merchants. I'm pretty sure they know of what they speak.
  - Houshalter 11y ago
    Yes I understand Bayes Rule. Nowhere in the article did they make any such fallacy. They are just stating what features happen to be predictive of fraud. Anyone actually using this would use a proper statistical model and real data, not random figures they found on a blog. But this post does give useful advice of what features to test. In any case, if there is only a 50% chance that a user is a fraudster, it still might be worth rejecting them. Anyway, using your assumption of 1% fraud, and using all of the features listed in the article (and presumably there would be other features too, but let's just use the ones they talked about), I do the following calculation: 1% = 1:99 x 8:1 (32 bit OS) x 9:1 (fresh cookies) x ? (Null values doesn't give a number, or suggests that 100% of null values are from fraudsters and never appear organically, so I will exclude it) x 5:1 (referrer history) x 96:70 (Windows) x ? (Number for plugins not given, but it seems to be a low correlation) x ? (No number given for incognito, but it's probably low.) = 5:1 or 83% probability they are fraudulent, excluding the missing values, which would raise it further.
URSpider94 11y ago
At the top of the article, they mention that they are comparing incidence between the 2% of the device population that represent fraudulent transactions vs. the 98% that do not.
- SideburnsOfDoom 11y ago
  % of the general population is not so relevant - they don't mention what % of the population that exhibits these markers is not fraudulent.

Houshalter 11y ago

Presumably they have that data. That's how they found these correlations.

throwaway2048 11y ago
that is not something that should be presumed, this kind of error is super common, especially with people that are otherwise not statistically well grounded. if 1% of transactions are fraudulent, and these indicators have only a 1% false positive rate (which it has nowhere close to), and it will match the fingerprint of every fraudulent transaction (which it wont) you get an absolutely titanic false overall positive rate, somewhere around >50% false positive. This is not some trivial pedantic thing, its a very important aspect of statistical models that must be explicitly stated to be accounted for. It is not enough to say "every y has characteristic X" if no other information about X is provided.
- URSpider94 11y ago
  These people sell anti-fraud services to merchants. I'm pretty sure they know of what they speak.
- Houshalter 11y ago
  Yes I understand Bayes Rule. Nowhere in the article did they make any such fallacy. They are just stating what features happen to be predictive of fraud. Anyone actually using this would use a proper statistical model and real data, not random figures they found on a blog. But this post does give useful advice of what features to test. In any case, if there is only a 50% chance that a user is a fraudster, it still might be worth rejecting them. Anyway, using your assumption of 1% fraud, and using all of the features listed in the article (and presumably there would be other features too, but let's just use the ones they talked about), I do the following calculation: 1% = 1:99 x 8:1 (32 bit OS) x 9:1 (fresh cookies) x ? (Null values doesn't give a number, or suggests that 100% of null values are from fraudsters and never appear organically, so I will exclude it) x 5:1 (referrer history) x 96:70 (Windows) x ? (Number for plugins not given, but it seems to be a low correlation) x ? (No number given for incognito, but it's probably low.) = 5:1 or 83% probability they are fraudulent, excluding the missing values, which would raise it further.

URSpider94 11y ago

At the top of the article, they mention that they are comparing incidence between the 2% of the device population that represent fraudulent transactions vs. the 98% that do not.

SideburnsOfDoom 11y ago
% of the general population is not so relevant - they don't mention what % of the population that exhibits these markers is not fraudulent.