This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the design is accomplished using a series of machine learning methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of cellular telephones based on profiling customer behavior. Specific...

This paper describes the automatic design of methods for detecting fraudulent behavior. Much of the de&,, ic nrrnm,-,li~h~rl ,,&,a n .am.L~ nf mn.-h;na lm..~:~~ e-. .. ..--..*.*yYYA’“.. UY.“b Y UISLUY “I III-Yllr IxuIY11~ methods. In particular, we combine data mining and constructive induction with more standard machine learning techniques to design methods for detecting fraudulent usage of ce...

Data mining involves searching through databases for potentially useful information, such as knowledge rules, patterns, regularities, and other trends hidden in the data. Today, data mining is more widely used than ever before, not only by businesses who seek profits but also by nonprofit organizations, government agencies, private groups and other institutions in the public sector. In this pap...

Rare category detection is an open challenge for active learning, especially in the de-novo case (no labeled examples), but of significant practical importance for data mining e.g. detecting new financial transaction fraud patterns, where normal legitimate transactions dominate. This paper develops a new method for detecting an instance of each minority class via an unsupervised local-density-d...

This paper describes a data mining approach to the problem of detecting erroneous foreign trade transactions in data collected by the Portuguese Institute of Statistics (INE). Erroneous transactions are a minority, but still they have an important impact on the official statistics produced by INE. Detecting these rare errors is a manual, timeconsuming task, which is constrained by a limited amo...

According to a survey conducted by the Communications Fraud Control Association an estimated $46.3 billion were lost due to telecommunications fraud in 2013. This suggests that the potential for intentional exploitation of unsuspecting users is an ongoing issue, and finding anomalies in telecommunications data can aide in the security of users, their phones, their personal information, and the ...

a r t i c l e i n f o Keywords: Data mining Financial fraud detection Feature selection t-statistic Neural networks SVM GP Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 C...