Interesting article in today’s WSJ about Ebay and computer scientists who track fraud through algori
I don’t have a link to the article in today's Wall Street Journal, but it is pretty interesting. I am no math geek, so bear with me as I try to explain the article. A group of computer scientists from Carnegie Mellon developed a theory called “bipartite core” in order to predict fraud on Ebay. A bipartite core is a term from math and computer science that describes a situation where members of Group A will deal with members of Group B, but never with each other; the pattern become apparent when the interactions of the two groups are plotted on a graph.
To my simple mind, an example of this is a fraudster creating multiple Ebay accounts and creating multiple accomplice accounts and generating positive feedback with related party transactions and a few outside people. The article explains this a lot better than I can, but supposedly these computer scientists created an algorithm that can analyze a large amount of data on Ebay and predict which accounts engage in fraud (or are more likely to engage in a future fraudulent transaction).
If anyone has today’s paper, the article in on page B3.
To my simple mind, an example of this is a fraudster creating multiple Ebay accounts and creating multiple accomplice accounts and generating positive feedback with related party transactions and a few outside people. The article explains this a lot better than I can, but supposedly these computer scientists created an algorithm that can analyze a large amount of data on Ebay and predict which accounts engage in fraud (or are more likely to engage in a future fraudulent transaction).
If anyone has today’s paper, the article in on page B3.
Always took candy from strangers
Didn't wanna get me no trade
Never want to be like papa
Working for the boss every night and day
--"Happy", by the Rolling Stones (1972)
Didn't wanna get me no trade
Never want to be like papa
Working for the boss every night and day
--"Happy", by the Rolling Stones (1972)
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Comments
Camelot
It should be very simple for them to do with programs what we do with simple observation.
"We ran the algorithm over a large set of data from eBay, and we were able to locate some obvious cases of fraud that hadn't been spotted before."
I have seen forum members here team up and accomplish the same thing during a Sunday halftime. The fact remains that Ebay should be at the forefront of this work, not Carnegie Mellon grad students or coin collectors.
Very interesting link-- thanks!
Whoever is careless with the truth in small matters cannot be trusted with important matters.
Millertime
Complete Dime Set
Whoever is careless with the truth in small matters cannot be trusted with important matters.
1 - Ebay needs to get control of their world
B - It shouldn't be up to people that are losing money to prove they've been wronged, Ebay should have be held to the same controls that CEOs and CFOs are with the SOX requirements
4 - It's just sad that some students did this and Ebay couldn't
Didn't wanna get me no trade
Never want to be like papa
Working for the boss every night and day
--"Happy", by the Rolling Stones (1972)
<< <i>"We ran the algorithm over a large set of data from eBay, and we were able to locate some obvious cases of fraud that hadn't been spotted before." >>
If Category = "Coins" and Seller_Location = "China" then Fraud = "Yes"
They just do not want to catch people
Longacre, I owe you an email on an unrelated topic. Could you PM me your address?
<< <i>This is nothing new. I use to do it for a job. We use similar algorthms to make sure that our traders are not doing anything illegal. There are a TON of simple tests that you could do and catch ebay people (I do it all of the time and report them, but 75% of the time, nothing happens). If I had acccess to IP addresses of where bids were placed, I could catch a ton more. >>
This reminds me of some of the discussions we had on heuristics when I was taking some CS graduate courses. This would have been an interesting case study to talk about in the lectures.
Regarding 3 types of people on e-bay...
There are 10 types of nerds in the world. Those that know binary and those that don't.