Date: Wed, 25 Aug 93 08:37:07 -0700 From: strick -- henry strickland <strick@versant.com> Extracted from "FC NEWSBYTES 1.3", David Geddes <dgeddes@NETCOM.COM> Editor, where FC = FutureCulture mailing list <FUTUREC-request@UAFSYSB.UARK.EDU>. 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 transactions. 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 mistakes. 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. Andy