Fraud Detection in Electronic Auction

Auction frauds plague electronic auction websites. Unfortunately, no

literature has tried to formulate and solve the problem. This paper aims to tackle

it by suggesting a novel method to detect auction fraudsters, which involves determining and extracting characteristic features from exposed fraudsters,

through analyzing the fraudsters’ transaction history which exists as a graph.

We then use the features for detecting other potential fraudsters. Choosing the

best features is a challenging and non-trivial task; however, with the features

that we have currently selected, our method has already achieved a precision of

82% and a recall of 83% during an evaluation on some real test data from eBay.

3.1 Problem Definition

Here, we examine the problem of detecting auction fraudsters. Specifically, we define

the problem as:

Given:

· The information of some electronic auction users: their profiles and their transaction

history

· Some exposed fraudsters

We want to find out:

· Who else are also fraudsters

The profiles and transaction history of eBay users are readily available from the eBay

website (Appendix), while the knowledge about the exposed fraudsters can be acquired from

news articles, user forum on eBay, and by noting the large number of negative feedback

we define the auction fraud problem as given these two pieces of information, how do we

given by other buyers or sellers saying the fraudsters have never delivered the items. Thus,

identify other potential fraudsters before they carry out frauds.

source:

http://www.cs.cmu.edu/~dchau/papers/chau_fraud_detection.pdf