December 6, 2006
Data Mining Against Fraud
Researchers at Carnegie Mellon have devised algorithms relying on data miniing to catch people who commit fraud on internet auction sites:
Online auction sites are immensely popular. The largest, eBay, reported third quarter revenues of $1.449 billion, up 31 percent from the previous year, and registered 212 million users, up 26 percent. But the popularity of online auction sites also makes them a target for crooks. Internet auction fraud, such as failure to deliver goods after a sale, accounted for almost two-thirds of the 97,000 complaints referred to law enforcement agencies last year by the federal Internet Crime Complaint Center.Perpetrators of these frauds have distinctive online behaviors that cause them to be readily purged from an online auction site, said Computer Science Professor Christos Faloutsos. The software developed by his research team — Network Detection via Propagation of Beliefs, or NetProbe — could prevent future frauds by identifying their accomplices, who can lurk on a site indefinitely and enable new generations of fraudsters.
In a test analysis of about one million transactions between almost 66,000 eBay users, NetProbe correctly detected 10 previously identified perpetrators, as well as more than a dozen probable fraudsters and several dozen apparent accomplices.
I know data mining is a bad word for some people, but as they are careful to point out this is all public info. It would be interesting to find out if auction sites use this in house if non-public info helps. Now if this will stand up in court, we can get somewhere. I wonder if you could use public info on prior auctions & other bidders to help you craft a bidding strategy. Posted by Kevin Murphy at December 6, 2006 11:38 AM | Technology
