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Old Jan 8, 12, 5:10 am
  #14  
frank_10b
 
Join Date: Mar 2003
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Originally Posted by redtop43 View Post
There is a regional department store chain based in Reading, PA called Boscov's. Many years ago I saw a credit scoring algorithm that added points for having accounts with many providers, but deducted points if you had a Boscov's account.

More to the point, algorithms to flag potentially defaulting accounts are by their nature agnostic. They don't try to "guess" what qualities will predict default (or fraud, or anything else, good or bad, you are trying to predict). They simply track the past data that, singly or in combination, are most predictive of what they are trying to predict. If people who make purchases of exactly $13.22 had statistically higher defaults than those who don't, then you would be more likely to get an FR if you bought something for $13.22. It's not about logic, it's about the data. These systems cost millions to implement and maintain. (I work for a company that develops them.)
Thank you for your insights,
so what do you think that the data has found or most likely to find in creating profiles of "dangerous" or customers that should be "targeted" for either good or bad treatment?
Certainly Walmart is a big No-no, any other retailers? Which ones may be surprising positives? I heard that some cc issures like Starbucks as a first purchase on a new account.
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