Unsupervised Profiling Methods for Fraud Detection 论文

摘要

Credit card fraud falls broadly into two categories: behavioural fraud and application fraud. Application fraud occurs when individuals obtain new credit cards from issuing companies using false personal information and then spend as much as possible in a short space of time. However, most credit card fraud is behavioural and occurs when details of legitimate cards have been obtained fraudulently and sales are made on a 'Cardholder Not Present' basis. These sales include telephone sales and e-commerce transactions where only the card details are required. In this paper, we are concerned with detecting behavioural fraud through the analysis of longitudinal data. These data usually consist of credit card transactions over time, but can include other variables, both static and longitudinal. Statistical methods for fraud detection are often classification (supervised) methods that discriminate between known fraudulent and non-fraudulent transactions; however, these methods rely on accurate identification of fraudulent transactions in historical databases -- information that is often in short supply or non-existent. We are particularly interested in unsupervised methods that do not use this information but instead detect changes in behaviour or unusual transactions. We discuss two methods for unsupervised fraud detection in credit data in this paper and apply them to some real data sets. Peer group analysis is a new tool for monitoring behaviour over time in data mining situations. In particular, the tool detects individual accounts that begin to behave in a way distinct from accounts to which they had previously been similar. Each account is selected as a target account and is compared with all other accounts in the database, using either external comparison criteria or i...