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Visa, HNC Inc. develop neural network as a weapon to fight fraud

   Date: Wed, 25 Aug 93 08:37:07 -0700
   From: strick -- henry strickland <[email protected]>

   Extracted from "FC NEWSBYTES 1.3", David Geddes <[email protected]> Editor,
	   where FC = FutureCulture mailing list <[email protected]>.    strick
   ____________________________________________ _ _..........
   B Y T E 4:

   Visa, HNC Inc. develop neural network as a weapon to fight fraud
   SAN FRANCISCO (AUG. 10) PR NEWSWIRE - Visa International and HNC Inc. have
   announced a strategic agreement to develop a comprehensive merchant risk
   detection system.  The new system will be designed to better control fraud at
   the merchant level by determining the risk associated with individual card

For those who are not familiar with the details of neural networks,  I
thought I would point out that this represents a departure from the 
current notion of a credit rating in two ways:

1) There is no clear way to fix your "neural credit rating" if there
   is a problem.

The neural network program which predicts the probability of fraud will
give its guess as to the probability of fraud.  If you are a cardholder
and it predicts that a transaction is likely to be fraudulent,  then
your purchase won't be accepted.  But,  unlike conventional credit reporting
firms which use a credit report,  the neural network cannot explain
anything about how it came to its decision.  With existing credit reporting
schemes,  you at least have the option of acquiring your credit report and
taking the necessary steps to repair your credit rating if there is a
problem.  With the use of neural networks,  this is no longer possible.

Given the current state of neural network research,  a percentage of the 
rejections will be false.  This means that a number of card users will be 
denied service for no other reason than the fact that neural networks make 

2) You are no longer judged on your own actions,  but on the similarity
   of your purchasing patterns with those who have committed fraudulent acts.

Instead of being judged on your trustworthiness based on your past actions,
you will be judged based on whether people whose purchasing profiles are
similar to yours are trustworthy.  An example of this being problematic
is say you purchase a particular CD and the neural network decides that,
partly based on this and partly on other information,  that you won't
pay your bill because most of the people in the database who bought that
CD didn't pay their bills.